GT Enzymes 2015 GGMM 2015 - 4ème Congrès du GT Enzymes

Transcription

GT Enzymes 2015
(25-27 Mai 2015)
et
GGMM 2015
(26-28 Mai 2015)
à SETE
1
Bienvenue !
Bonjour à toutes et tous et bienvenue à Sète.
Nous sommes très heureux de vous accueillir au centre Lazaret du 25 au 28 mai 2015 pour le
4ème congrès du Groupe Thé matique "Enzymes: Structure, Fonction, Catalyse, Ingénierie et
Régulation" et pour le 19 ème congrès du "Groupe de Graphism
e et de Modélisation
Moléculaire".
Cette année, ces congrès se recouperont pour accentuer les échanges entre les deux groupes
thématiques qui partagent des objectifs communs. Cet événement permettra de relier le côté
expérimental de l' enzymologie aux m éthodes prédictives en passant par le graphisme
structural et moléculaire. Deux dem i-journées seront consacrées à des thém atiques
communes: "les enzy mes comme cibles th érapeutiques" et "Ingén ierie enzymatique et
évolution dirigée". Nous rem ercions les bu reaux des deux groupes pou r avoir accepté cette
mixité exceptionnelle.
Le programme comprend 6 sessions, incluant 6 conférences plénières de 45 minutes et 3 7
conférences de 15 minutes dont les orateurs ont été sélectionnés à partir des résumé soumis, et
croyez-nous le choix n'a pas été facile! Toutes les présentations par affiche, au nom bre de 45,
donneront lieu à une présentation "Flash"
d'une diapositive unique avec une m inute de
présentation. Vous pourrez profit er des trois longues sessions de présentations par affiche
pour discuter avec leurs auteurs et deux prix pour la m eilleure affiche seront rem is à l'issue
des congrès respectifs. Vous participerez au jeu de "l'expert est dans la salle" qui permettra de
profiter de savoir-faire originaux. Enfin, nous ser ons heureux d'entendre le lauréat du prix du
GGMM, Isidro Cortes, dont l' excellent dossier a été retenu par le bureau du GGMM parmi
l’ensemble des très bons dossiers déposés.
Nous tenons à remercier les conférenciers invités, J. Blumberger, J. Cherfils, D. Douguet, D.
Hilvert, T. Isenberg et T. St rick, dont certains viennent de loin. Nous espérons que vous
profiterez pleinement de leur présence et
que les discussions seront nom breuses et
intéressantes.
Nous remercions également le comité scientifiq ue pour son aide précieuse dans l' élaboration
de ce programme: I. André, M. Baaden, S. Barb e, S. Boschi-Muller, N. Colloc' h, A. Kajava,
C. Léger, M. Remaud-Simeon, F. Rodrigues-Lima, O. Sperandio, O. Taboureau et C. Tellier.
Les rencontres se dérouleront dans un endroit c onvivial qui bénéficie d' un accès direct à la
plage. Afin de profiter au m aximum de la belle Région du L anguedoc Roussillon, un apéritif
Sétois et une dégustation de pr oduits régionaux seront offerts a ux participants. Et pour ceux
qui veulent poursuivre les discussions, le bar sera ouvert jusqu'à tard dans la soirée!
Nous vous souhaitons un excellent congrès, de
enrichissantes.
s échanges intéressants et des rencontres
L'équipe organisatrice
Rachel Cerdan
Laurent Chaloin
Frédéric de Lamotte
Gilles Labesse
Corinne Lionne
4 Le GT Enzymes
Le Groupe Thématique « Enzymes : Structure, Fonction, Catalyse,
Ingénierie, Régulation » a été créé en 2008 sous l’impulsion du conseil
d’administration de la SFBBM, et à l’initiative du Pr Guy Branlant. Ce GT a
pour but de fédérer au niveau national la communauté scientifique des
enzymologistes au sens large, de participer à la formation des étudiants et de
contribuer au maintien et au développement de cette discipline.
Depuis 2009, le GT organise un congrès tous les deux ans. Le premier
congrès était axé sur les aspects modernes de l'enzymologie en insistant sur les
problèmes à l'interface de la chimie et de la biologie (Nancy, 2009), le second
concernait les différentes disciplines et approches dans un contexte de biologie
intégrative (Ax-les-Thermes, 2011), alors que le troisième abordait les enzymes
dans leurs dimensions moléculaires et intégratives (Paris, 2013).
Les congrès du GT permettent à des chercheurs de stature internationale
mais aussi à de jeunes scientifiques de présenter leurs travaux, d’échanger sur de
nouveaux résultats et projets, mais également d’établir de nouvelles
collaborations et d’échanger informations et matériel. Ils contribuent ainsi à
maintenir une dynamique de recherche nationale et une compétitivité sur le plan
international.
5
Le GGMM
Le GGMM est une société savante qui rassemble une grande partie de la
communauté française dont l’activité́ est dédiée à, ou implique, l’utilisation de la
modélisation moléculaire. Cette association loi 1901 a été créée en 1983 à
l’initiative de Joël Janin, Evelyne James-Surcouf et Gérard Pepe dans le but de
promouvoir le développement de la modélisation moléculaire. Le GGMM est
également un groupe thématique de la Société Française de Biophysique (SFB)
et de la Société Française de Biochimie et Biologie Moléculaire (SFBBM).
Le GGMM regroupe aujourd’hui un très large panel de thématiques d'une
grande diversité allant de la bio-informatique à la chimie théorique, avec des
représentations à plusieurs échelles comme l'électron, les molécules et leurs
assemblages et même la cellule avec des approches globales de type
protéomique. Le but du GGMM est de promouvoir le développement de la
modélisation moléculaire au sens large, par la mise en place d’actions
spécifiques telles que le prix du GGMM qui est décerné, tous les deux ans, à un
jeune chercheur en récompense de travaux originaux ainsi que la diffusion
d’offres d’emplois, académiques et industrielles, en modélisation moléculaire. Il
s'agit en priorité de permettre aux futures générations de modélisateurs de
s'exprimer et s'échanger.
Tous les deux ans, le GGMM organise un congrès dont l’objectif est de
rassembler la communauté́ francophone pour communiquer sur les avancées
dans le domaine de la modélisation et la dynamique moléculaire, les nouvelles
approches et les nouveaux développements pour l’analyse des structures, les
avancées en chimie théorique en drug design, etc...
Le site web du GGMM
http://ggmmfr.wordpress.com/
est
6
accessible
à
cette
adresse
:
Nos partenaires Nous tenons à remercier chaleureusement tous nos partenaires et sponsors pour leur soutien financier et matériel, sans qui l'organisation de ce double congrès n'aurait pas été possible. Merci pour votre soutien et votre fidélité. 7 Programme
Lundi 25 mai 2015
12:30
 Déjeuner
14:15 Mot de Bienvenue – GT Enzymes
Session 1 – Structures et mécanismes enzymatiques
Modérateurs : Gilles LABESSE et Fernando RODRIGUES-LIMA
14:30 Jacqueline CHERFILS – Conférence Plénière
Structure and function of bacterial FIC toxins
15:15 Jérôme SANTOLINI
De quoi la NO-Synthase est elle le nom ?
15:30 Nolan CHATRON
Piston-driven activation mechanism of VKORC1, the human vitamin K epoxide
reductase
15:45 Bertrand CASTAING
Inhibition sélective des ADN glycosylases : approches structure-fonction
16:00
16:30
Pause café
Présentations Flash n°1 – Posters 1 à 7
S. BOSCHI-MULLER / N. COLLOC'H / R. DUVAL / A. KRIZNIK / C. MATHIEU
/ M. MERROUCH / S. RAHUEL-CLERMONT
16:45 Ewelina GUCA
Structural characterization of Plasmodium falciparum CCT and fragment-based
drug design approach for targeting phospholipid biosynthesis pathway
17:00 Pierre LAFITE
Understanding the mechanism of UGT74B1 from Arabidopsis thaliana: S- or Oglycosyltransferase ?
17:15 Claire STINES-CHAUMEIL
Monitoring the transition of apo i nto holo forms of soluble glucose
dehydrogenase by stopped-flow and c rystallographic studies to decipher the
mechanism of reconstitution/activation of this enzyme
17:30 Jean-Christophe LEC
Mécanisme de la 3-mercaptopyruvate sulfurtransférase : une enzyme impliquée
dans la production de sulfure d'hydrogène
17:45 Présentation Elsevier – Journal Biochimie
Apéritif de bienvenue SESSION POSTERS
19:30  Dîner
18:00
8
Mardi 26 mai 2015
Matin
Session 2 – Régulation et efficacité
Modérateurs : Sandrine BOSCHI-MULLER et Charles TELLIER
09:30 Terence STRICK – Conférence Plénière
Approches corrélatives en analyse molécule-unique
10:15 Hélène MUNIER-LEHMANN
Régulation des IMPDHs bactériennes au travers des modules CBS
10:30 Nicolas BOSC
Identification of Discriminant Conformation-Dependent Residues of Protein
Kinases: a Proteometric Analysis
10:45 Jessica HADJ-SAID
La Carbon Monoxide Dehydrogenase (CODH) de Desulfovibrio vulgaris
11:00
Pause café
11:30 Loic CARRIQUE
Study of the regulatory behavior of Plasmodium falciparum IMP-specific 5'nucleotidase (PfISN1)
11:45 Sophie RAHUEL-CLERMONT
Cellular redox signaling by thiol peroxidase : Mechanisms responsible for the
specificity of the redox relay H2O2/Orp1/Yap1 in S. cerevisiae
12:00 Marc-André DELSUC
Amyloid properties of a conserved domain in the N-Term domain of the Androgen
Receptor - Implications for new therapeutic routes
12:15
 Déjeuner
9
Mardi 26 mai 2015
Après-midi
14:00 Mot de Bienvenue – GGMM
Session 3 – Ingénierie enzymatique et évolution dirigée
Modérateurs : Christophe LEGER et Magali REMAUD SIMEON
14:15 Donald HILVERT – Conférence Plénière
Design and optimization of artificial enzymes: nearer to nature
15:00
I. ANDRE / S. BARBE / A. BRAKA / M. BRUT / K. DRUART / N. FLOQUET / M.
GUEROULT / S. KELLOU-TAIRI / V. MARTINY / C. RIEUX
15:15 Thomas GAILLARD
Design computationnel de protéines avec une fonction d'énergie MMGBSA
15:30 Seydou TRAORE
Cost Function Network Optimization: exact algorithms towards Computational
Protein Design
15:45 David RINALDO
Computational approach to enzyme design
16:00
F. BARAKAT / S. BAUD / J-B. CHERON / J. CORTES / S. CROUZY / D. DE
VECCHIS / S. DOUTRELIGNE / L. VERZEAUX / D. FLATTERS
16:15
Pause café
SESSION POSTERS
18:30 Apéritif Sétois
19:30  Dîner
Soirée festive
16:45
10 Mercredi 27 mai 2015
Matin
Session 4 – Les enzymes comme cibles thérapeutiques
Modérateurs : Rachel CERDAN et Laurent CHALOIN
09:00 Dominique DOUGUET – Conférence Plénière
Enzymes as target class for approved drugs The case of secretory phospholipase
A2 (sPLA2) family of proteins as novel therapeutic targets
09:45
N. FLOQUET / C. GAGEAT / S. GRUDININ / M. KATAVA / Y. LAURIN / G.
LEROUX / C. MAROT / I. MEREU / D. MIAS-LUCQUIN
10:00 Kamel DJAOUT
Développement d'antibactériens dirigés contre la ThyX de Mycobacterium
tuberculosis
10:15 Fanny KREBS
Molecular dynamics study of ligand recognition by DXR: implications for the
design of new antibiotic compounds
10:30 Jana SOPKOVA-DE OLIVEIRA SANTOS
Discovery of Oligopyridyl scaffold molecules as potent Mcl-1 inhibitors
10:45
11:15
Pause café
A-E. MOLZA / D. MONET / M. NG FUK CHONG / M-K. NGUYEN / J.
REBEHMED / N. RENAULT / E. SELWA / D. STRATMANN / T. TUBIANA /
S. WIENINGER
11:30 Florent BARBAULT
Into the intimacy of an irreversible inhibition: SMD(QM/MM) simulations of the
fibroblast growth factor receptor-1 (FGFR1) kinase domain
11:45 Inès RASOLOHERY
PatchSearch: a fast method for flexible recognition of protein binding sites
12:00 Hiba ABI HUSSEIN
PockDrug-Server: a new web server for predicting pocket druggability on hol o
and apo proteins
Prix poster GT Enzymes
12:30  Déjeuner et départ GT Enzymes
12:15
11
Mercredi 27 mai 2015
Après-midi
Session 5 – Relations dynamique - fonction
Modérateurs : Nathalie COLLOC'H et Andrey KAJAVA
14:00 Jochen BLUMBERGER – Conférence Plénière
Combining molecular dynamics and Markov state modelling to understand ligand
transport in enzymes
14:45 Jérôme EBERHARDT
Structural dynamics of the retinoid X receptor ligand binding domain by
accelerated molecular dynamics
15:00 Benoît DAVID
Turning glycoside hydrolases into transglycosidases: an e xperimental and
theoretical study of the internal water dynamics in the Thermus thermophilus βglycosidase
15:15 Julien DIHARCE
Description théorique d'un phénomène de "substrate channeling" dans la
biosynthèse des flavonoïdes
15:30 Maxime LOUET
L'allostérie dynamique de la protéine CAP révélée par les forces inter-atomiques
15:45 Présentation du jeu "L'expert est dans la salle"
16:00
Pause café
16:30 Matthieu CHAVENT
Assessing the crowding of Membrane Proteins at the Mesoscale
16:45 Kaouther BEN OUIRANE
New way to fight against antibiotic resistance: reconstruction of a t hreecomponent efflux pump
17:00 Jean-Pierre DUNEAU
Étude de l'assemblage des protéines membranaires par simulation de dynamique
moléculaire à gros grains et méthodes de Forces de Biais Adaptatif
17:15 Christophe NARTH
Tinker-HP : Dynamique moléculaire polarisable haute performance
17:30 Assemblée Générale GGMM
Dégustation de produits régionaux SESSION POSTERS
19:30  Dîner
18:00
21:00
"L'expert est dans la salle"
12
Jeudi 28 mai 2015
Matin
Session 6 – Visualisation et prédiction structurale
Modérateurs : Marc BAADEN et Frédéric DE LAMOTTE
09:00 Tobias ISENBERG – Conférence Plénière
Illustrative Molecular Visualizations
09:45 Fabrice ALLAIN
Modélisation de novo de structures protéiques par contacts évolutifs
10:00 Mathias FERBER
Integrating the Solvent Accessible Surface Distance with cross-links-based
modeling methods improves the conformational sampling of protein assemblies
10:15 Emilie NEVEU
A Detailed Data-Driven Protein-Protein Interaction Potential Accelerated By
Polar Fourier Correlation
10:30 Isaure CHAUVOT DE BEAUCHENE
Fragment-based modeling of ssRNA-protein complexes
10:45
Pause café
11:15 Jean-Christophe GELLY
ORION : improving remote homology detection using a structural alphabet
11:30 Stéphane REDON
SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems
11:45
Prix du GGMM - Isidro CORTES
Integration of Chemical and B iological (’omics’) Data for the Prediction of
Cancer Cell-Line Sensitivity
12:15
12:30
Prix poster GGMM
 Déjeuner et départ GGMM
13
Table des matières
Edito
4
Le GT Enzymes
5
Le GGMM
6
Nos Partenaires
7
Programme détaillé
8
Structures et mécanismes enzymatiques
21
Structure and function of bacterial FIC toxins, Jacqueline Cherfils . . . . . . . .
22
De quoi la NO-Synthase est elle le nom ?, Jérôme Santolini . . . . . . . . . . . .
23
Piston-driven activation mechanism of VKORC1, the human vitamin K epoxide
reductase, Nolan Chatron [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
Inhibition sélective des ADN glycosylases : approches structure-fonction, Bertrand
Castaing [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
Structural characterization of Plasmodium falciparum CCT and fragment-based
drug design approach for targeting phospholipid biosynthesis pathway, Ewelina
Guca [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
Understanding the mechanism of UGT74B1 from Arabidopsis thaliana: S- or Oglycosyltransferase ?, Pierre Lafite [et al.] . . . . . . . . . . . . . . . . . . . . . .
28
14
Monitoring the transition of apo into holo forms of soluble glucose dehydrogenase by stopped-flow and crystallographic studies to decipher the mechanism of
reconstitution/activation of this enzyme., Claire Stines-Chaumeil [et al.] . . . . .
29
Mécanisme de la 3-mercaptopyruvate sulfurtransférase : une enzyme impliquée
dans la production de sulfure d’hydrogène., Jean-Christophe Lec [et al.] . . . . .
30
Régulation et efficacité
31
Approches correlatives en analyse molecule-unique, Terence Strick . . . . . . . . .
32
Régulation des IMPDHs bactériennes au travers des modules CBS, Hélène MunierLehmann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
Identification of Discriminant Conformation-Dependent Residues of Protein Kinases: a Proteometric Analysis, Nicolas Bosc [et al.] . . . . . . . . . . . . . . . .
34
La Carbon Monoxide Dehydrogenase (CODH) de Desulfovibrio vulgaris., Jessica
Hadj-Said [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
Study of the regulatory behavior of Plasmodium falciparum IMP-specific 5’-nucleotidase
(PfISN1), Loic Carrique [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
specificity of the redox relay H2O2/Orp1/Yap1 in S. cerevisiae, Antoine Berweiler
[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
Amyloid properties of a conserved domain in the N-Term domain of the Androgen
Receptor - Implications for new therapeutic routes -, Julia Asencio Hernández [et
al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
Ingénierie enzymatique et évolution dirigée
41
Design and optimization of artificial enzymes: nearer to nature, Donald Hilvert .
42
Design computationnel de protéines avec une fonction d’énergie MMGBSA, Thomas
Gaillard [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
Cost Function Network Optimization: exact algorithms towards Computational
Protein Design, Seydou Traore [et al.] . . . . . . . . . . . . . . . . . . . . . . . .
45
Computational approach to enzyme design, David Rinaldo . . . . . . . . . . . . .
48
Les enzymes comme cibles thérapeutiques
15
49
Enzymes as target class for approved drugs The case of secretory phospholipase
A2 (sPLA2) family of proteins as novel therapeutic targets, Dominique Douguet .
50
Développement d’antibactériens dirigés contre la ThyX de Mycobacterium tuberculosis, Kamel Djaout [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
Molecular dynamics study of ligand recognition by DXR: implications for the
design of new antibiotic compounds, Fanny Krebs [et al.] . . . . . . . . . . . . . .
52
Discovery of Oligopyridyl scaffold molecules as potent Mcl-1 inhibitors, Jana
Sopkova-De Oliveira Santos [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . .
53
Into the intimacy of an irreversible inhibition: SMD(QM/MM) simulations of the
fibroblast growth factor receptor-1 (FGFR1) kinase domain., Yan Li [et al.] . . .
55
PatchSearch: a fast method for flexible recognition of protein binding sites, Inès
Rasolohery [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
PockDrug-Server: a new web server for predicting pocket druggability on holo and
apo proteins, Hiba Abi Hussein [et al.] . . . . . . . . . . . . . . . . . . . . . . . .
59
Relations dynamique - fonction
60
Combining molecular dynamics and Markov state modelling to understand ligand
transport in enzymes, Jochen Blumberger [et al.] . . . . . . . . . . . . . . . . . .
61
Structural dynamics of the retinoid X receptor ligand binding domain by accelerated molecular dynamics, Jérôme Eberhardt [et al.] . . . . . . . . . . . . . . . . .
62
Turning glycoside hydrolases into transglycosidases: an experimental and theoretical study of the internal water dynamics in the Thermus thermophilus glycosidase, Benoît David [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
Description théorique d’un phénomène de ”substrate channeling” dans la biosynthèse des flavonoïdes., Julien Diharce [et al.] . . . . . . . . . . . . . . . . . . . . .
65
L’allostérie dynamique de la protéine CAP révélée par les forces inter-atomiques, Maxime
Louet . Louet
. . . .[et
. .al.]
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Maxime
Assessing the crowding of Membrane Proteins at the Mesoscale, Matthieu Chavent
[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
New way to fight against antibiotic resistance: reconstruction of a three-component
efflux pump, Kaouther Ben Ouirane [et al.] . . . . . . . . . . . . . . . . . . . . .
69
16
Étude de l’assemblage des protéines membranaires par simulation de dynamique
moléculaire à gros grains et méthodes de Forces de Biais Adaptatif., Jean-Pierre
Duneau [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
Tinker-HP : Dynamique moléculaire polarisable haute performance, Christophe
Narth [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
71
Visualisation et prédiction structurale
72
Illustrative Molecular Visualizations, Tobias Isenberg . . . . . . . . . . . . . . . .
73
Modélisation de novo de structures protéiques par contacts évolutifs, Fabrice Allain [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
Integrating the Solvent Accessible Surface Distance with cross-links-based modeling methods improves the conformational sampling of protein assemblies, Mathias
Ferber [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
76
A Detailed Data-Driven Protein-Protein Interaction Potential Accelerated By Polar Fourier Correlation, Emilie Neveu [et al.] . . . . . . . . . . . . . . . . . . . . .
77
Fragment-based modeling of ssRNA-protein complexes, Isaure Chauvot De Beauchene [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79
ORION : improving remote homology detection using a structural alphabet, Yassine Ghouzam [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems, Nadhir Abdellatif [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83
Integration of Chemical and Biological (’omics’) Data for the Prediction of Cancer
Cell-Line Sensitivity, Isidro Cortes [et al.] . . . . . . . . . . . . . . . . . . . . . .
85
Session poster GT Enzymes/GGMM
87
Les thioltransférases : production et élimination de sulfure d’hydrogène ?, JeanChristophe Lec [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88
Rôle fonctionnel d’une cavité interne hydrophobe dans l’urate oxydase, Nathalie
Colloc’h [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
Benzene-induced leukemogenesis: Irreversible inhibition of PTPN2, a tumor suppressor phosphatase involved in leukemia by the hematotoxic metabolite benzoquinone, Romain Duval [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . .
90
17
specificity of the redox relay H2O2/Orp1/Yap1 in S. cerevisiae, Antoine Bersweiler
[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
Molecular basis of the alteration of brain glycogen metabolism by dithiocarbamate
pesticides: impairment of brain glycogen phosphorylase, Cécile Mathieu [et al.] .
93
Réactivité de la carbon monoxide dehydrogenase à nickel avec l’oxygène, Meriem
Merrouch [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94
Regulation of thiol peroxidases in peroxide-dependent redox signaling: Thioredoxin vs. glutathione in reduction of hyperoxidized 2-Cys peroxiredoxin by Sulfiredoxin, Samia Boukhenouna [et al.] . . . . . . . . . . . . . . . . . . . . . . . . .
95
Computer-aided engineering of a transglycosylase for the glucosylation of an unnatural disaccharide of relevance for bacterial antigen synthesis, Alizée Verges [et
al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
96
Computational Enzyme Design through deterministic search and counting methods, Clément Viricel [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
Towards the development of an in silico tools for kinetics prediction, Abdennour
Braka [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
In silico experiments on biomolecules through the Static Modes: An efficient approach to predict and design function, Marie Brut . . . . . . . . . . . . . . . . . . 102
Proteus: dessin de protéine en backbone flexible, Karen Druart [et al.] . . . . . . 103
Molecular dynamics with excited normal modes reveals the role of the activation
loop of Cyclin-Dependent Kinases in their open/closed conformational equilibrium, Nicolas Floquet [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Design raisonné de procédés enzymatiques pour la dégradation améliorée de biopolymères, Marc
Gueroult
[et al.]
. . . . [et
. . al.]
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
-mères, Marc
Guéroult
Ligand-Based QSAR studies on some Cis-Stilbenes as Cyclooxygenase Inhibitor, Safia
Kellou-Taïri [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
FOCUSED LIGAND LIBRARIES FOR COVALENT DOCKING ON SERINE
ACTIVE PROTEINS, Virginie Martiny [et al.] . . . . . . . . . . . . . . . . . . . 107
Inhibition sélective des ADN glycosylases : simulation par dynamique moléculaire
et docking pour la recherche et la conception d’inhibiteurs, Charlotte Rieux [et al.] 109
Spectroscopic Fingerprints of the Structural Fluctuations of a Single Amino Acid, Fatima Barakat [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
18
Unravelling the structure/activity relationship between collagen derived peptides
and the v3 integrin, Eléonore Lambert [et al.] . . . . . . . . . . . . . . . . . . . . 112
Mécanisme d’activation du récepteur T1R2-T1R3 impliqué dans la perception du
goût sucré., Jean-Baptiste Cheron [et al.] . . . . . . . . . . . . . . . . . . . . . . . 113
Caractérisation du paysage énergétique de biomolécules flexibles avec un couplage
d’algorithmes stochastiques, Juan Cortes . . . . . . . . . . . . . . . . . . . . . . . 114
Modeling methods for protein structure prediction, mechanistic and docking studies, Serge Crouzy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Mitochondrial membrane fusion: computational modelling of mitofusins, Dario De
Vecchis [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
UnityMol: Simulation et Visualisation Interactive à des fins d’Enseignement et de
Recherche, Sébastien Doutreligne [et al.] . . . . . . . . . . . . . . . . . . . . . . . 118
Study of TIMP-1/LRP-1
interaction
in neurons:
a new
approach
based Sébastien
on protein
Mécanismes
d’activation des
RCPG impliqués
dans
les sens
chimiques,
dynamic,[etLaurie
Fiorucci
al.] . Verzeaux
. . . . . .[et
. .al.]
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
SA-conf : un outil d’analyse et de comparaison de la variabilité de séquences et
de structures d’un ensemble de conformères d’une protéine, Leslie Regad [et al.] . 121
Exploration of Large Amplitude Motions of GPCRs and their complexes at the
molecular level, Nicolas Floquet [et al.] . . . . . . . . . . . . . . . . . . . . . . . . 123
HiRE-RNA: un modèle gros grain pour la simulation d’ARN et d’ADN, Cédric
Gageat [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
PEPSI-SAXS - A very fast adaptive scheme for computation of small-angle X-ray
scattering profiles via spherical harmonics expansions, Sergei Grudinin [et al.] . . 126
Conformational changes
changes in
in themophilic
thermophilicand
andmesophilic
mesophilicenymes,
enzymes,
Marina
Katava
Conformational
Marina
Katava
[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Etude de la variabilité structurale et de l’interaction du peptide NFL-TBS.40-63
avec la tubuline., Yoann Laurin [et al.] . . . . . . . . . . . . . . . . . . . . . . . . 129
Molecular basis of Olfactory Receptors: in silico modeling and in vitro validation., Gwenaëlle Andre-Leroux . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Molecular Modeling CoMFA and docking of a Novel Series of Potent 7-Azaindole
Based Tri-Heterocyclic Compounds as CDK2/Cyclin E Inhibitors, Christine B.
Baltus [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
19
Study of large-scale conformational transitions in c-Abl at the free energy level
via a structure-based model, Ilaria Mereu . . . . . . . . . . . . . . . . . . . . . . 133
Cartographie des interactions atomiques dans le filament de dystrophine, Dominique Mias-Lucquin [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
Développement d’une stratégie multi-échelle combinant des données expérimentales et in silico pour la reconstruction de complexes impliquant la dystrophine., AnneElisabeth Molza [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Analysis of protein structure and dynamics to better understand their functional
mechanisms, Damien Monet [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Conformational properties and transport of glucose by GLUT1 using in silico
approaches, Matthieu Ng Fuk Chong [et al.] . . . . . . . . . . . . . . . . . . . . . 138
As-Rigid-As-Possible interpolation for structural biology, Minh Khoa Nguyen [et
al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
What wins in determining protein regular secondary structures? The polar/non
polar binary pattern or the intrinsic amino acid composition? A statistical analysis
using the HCA approach., Joseph Rebehmed [et al.] . . . . . . . . . . . . . . . . 141
Molecular features switching off the melatonin-induced activity from MT1 to
GPR50 receptors, Nicolas Renault [et al.] . . . . . . . . . . . . . . . . . . . . . . 143
Coarse-Graining Parameterization of Specific Cardiac Membrane Lipids, Edithe
Selwa [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
Prediction of peptide conformational stability, Marie Amandine Laurent [et al.] . 145
Dynamique d’assemblage de la capside des norovirus, Thibault Tubiana [et al.] . 146
Identification and automated weighting of ensemble-averaged NOE restraints, Silke
Wieninger [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
ListeListe
des des
participants
auteurs
149
149
participants
ListeListe
des des
auteurs
153
154
Session poster GT Enzymes
160
Session poster GGMM
161
20
21
CONFERENCE PLENIERE
Structure and function of bacterial FIC toxins
Jacqueline Cherfils
1
∗† 1
Ecole Normale Superieure (ENS de Cachan) – CNRS – Laboratoire de Biologie et Pharmacologie
Appliquée- CNRS - ENS de Cachan, France
Five years ago, the large family of bacterial FIC proteins was still uncharted territory. It
has now become clear that many FIC domain-containing proteins are toxins that add diverse
post-translational modifications to target proteins. I will describe our recent structural and
biochemical studies of the FIC protein AnkX, a secreted effector from Legionella pneumophila
(the bacteria that causes the legionnaire’s disease, a severe pneumonia). AnkX takes command
of membrane traffic - the “cellular postal service” that shuttles biomolecules around the cell and
organizes the structure of organelles - by adding a phosphocholine moiety to small GTPases of
the Rab family, which are chief organizers of membrane traffic in the host cell. These studies
highlight how FIC proteins exploit similar catalytic mechanisms to regulate a variety of processes
using a broad range of substrates.
Campanacci V., Mukherjee S., Roy C.R , Cherfils J. , EMBO Journal 2013
Roy C.R. and Cherfils J., Nature Reviews Microbiology (to be published)
∗
†
Intervenant
Auteur correspondant: [email protected]
22
COMMUNICATION ORALE
De quoi la NO-Synthase est elle le nom ?
Jérôme Santolini
1
∗ 1
CEA - DSV - SB2SM - LSOD (LSOD) – Centre national de la recherche scientifique - CNRS (France),
CEA, Université Paris Sud - Paris XI – CEA-Saclay 91191 gif sur Yvette cedex, France
Nitric Oxide Synthases (NOS), the exclusive source of NO in mammals, are at the core of
a large number of signaling processes crucial in blood pressure regulation, neural communication, cell cycle... NOSs are also the source of an endogenous oxidative stress and are increasingly
associated to numerous pathological conditions, including cardiovascular, inflammatory and neurodegenerative diseases. The improvement of the gene sequencing techniques has recently led to
the discovery of numerous NOSs throughout the living kingdom. The first investigations have
revealed a strong homology with mammalian NOSs but an extremely broad range of biological
functions: from pathogenicity to detoxification, through metabolism and resilience.
The current paradigm in the NOS field, namely NOS-NO-Signalling, has become insufficient to
account for the multiple pathophysiological roles of mammalian NOSs and for the structural and
biological biodiversity of NOSs throughout the living kingdom. There is currently no understanding of the discrepancy between the strong structural identity of NOSs and their implication
in many different if not opposite biochemical processes. We have initiated an integrative approach that aims at establishing a relationship between the structural properties of NOSs and
their biological functioning. This project combines Bioinformatics, Enzymology, Biophysics and
Microbiology in order to address the structural, functional and biological diversity of this new
family of enzymes.
We will present our first results on
- Mammalian NOSs : we showed the existence of two distinct catalytic cycles, alternatively
leading to NO or to harmful nitrogen oxides. The partition between both cycles is specific to
each mammalian NOS and depends on the physiological environment, which might explain the
different contributions of NOSs to physiological and pathological processes.
- Bacterial NOS-LPs. We showed that bacterial NOSs had the ability to catalyse oxygen activation but displayed specific electronic and structural properties. This might explain their unfitness
to produce and release NO, which question their assignment as genuine NO synthases.
- Plant NOSs. In the context of the non-existence of plant NOSs, we started characterizing
NOSs from a green algae (otNOS) and cyanobacteria (spNOS). Our first results suggest that otNOS is a super NOS but raises questions about the actual biochemistry and function of spNOS.
By developing an innovative and interdisciplinary project, our final goal is to generate a reliable
Structure-Function-Environment Relationship that could predict the chemical and biological activity of any new NOS from its genomic sequence
∗
Intervenant
23
COMMUNICATION ORALE
Piston-driven activation mechanism of
VKORC1, the human vitamin K epoxide
reductase
Nolan Chatron
∗ 1
, Florent Langenfeld 1 , Etienne Benoit 2 , Virginie Lattard
2
, Luba Tchertanov 1
1
Centre de Mathématiques et de Leurs Applications - ENS Cachan (CMLA) – École normale
supérieure [ENS] - Cachan, École normale supérieure (ENS) - Cachan – ENS Cachan CNRS, PRES
UniverSud, 61 Avenue du President Wilson 94230 Cachan, France
2
VetAgro Sup [Lyon] – Campus Vétérinaire – Université de Lyon, 1 avenue Bourgelat, 69280 Marcy
l’Etoile, France
Blood coagulation is a crucial physiological process, vital for many living organisms. Vitamin
K epoxide reductase complex subunit 1 (VKORC1) plays a key role in this process, through
the catalysis of vitamin K recycling - from its inactive epoxide form to the biologically active
hydroquinone form [1]. VKORC1, an endoplasmic reticulum membrane protein, is the primary
target for vitamin K antagonists (VKAs) [2]. Appearance of VKORC1 mutations promotes
patient’s resistance to VKA drugs and/or alteration of VKORC1 activity. Our aim is to clarify
the VKORC1 catalytic mechanisms and the resistance mechanisms to VKAs on the atomic
scale. To the best of our knowledge, no structural data is available for the human VKORC1
(h-VKORC1 WT ), therefore in our study we used in silico modeling combined with molecular
dynamics (MD) simulations.
The first step consists to build a structural model of VKORC1 using the recent X-ray data for its
bacterial homolog [3]. The generated model denotes a four-helix structure of VKORC1 (Fig. 1
A-B) that is consistent with the experimentally determined enzymatic mechanism regulating the
electron transfer from cysteine residues 43-51 (in blue) to cysteine residues 132-135 (in green).
Each pair of transmembrane helices (TM) is linked by either short loops (TM3-TM4 and TM2TM3) or a long and highly flexible luminal loop (Loop 1/2, linking TM1-TM2) with a small
horizontal helix (HH) in the middle, overhanging the membrane surface. Molecular Dynamics
(MD) simulations (2 x 100 ns) revealed significant conformational changes, evidenced as a twostate transition of the h-VKORC1 WT . The MD simulations data were explored by principal
components analysis and cross-correlation analysis, which evidenced highly concerted motions of
protein residues from Loop 1/2, HH and the four TMs. We observed that the two coiled subfragments of Loop 1/2, separated by HH, show anti-correlated movement, which highly correlates
with the HH displacement. The longer the distance between the two coiled sub-fragments, the
larger the extent between HH and TMs (Fig. 1 C-D). When the two coiled sub-fragments of
Loop 1/2 adjoin, the HH moves towards the catalytic site, perturbing its topology by altering the
inter-helices space. We denominate these highly concerted movements which involve nearly all
structural subunits of h-VKORC1 WT and its all catalytic residues – C43 (LL), C51 (HH), C132
and C135 (TM4) – the ‘piston-driven mechanism’. This purely mechanistic description depicts
plausibly a first step of the h-VKORC1 WT activation which precede the enzymatic reaction(s).
∗
Intervenant
24
COMMUNICATION ORALE
Validation of the proposed model and its mechanisms will be performed through the study of
the VKORC1 mutants, resistant to VKAs, combining in silico, in vitro and in vivo approaches.
25
COMMUNICATION ORALE
Inhibition sélective des ADN glycosylases :
approches structure-fonction
Bertrand Castaing ∗† 1 , Stéphane Goffinont 1 , Franck Coste 1 , Françoise
Culard 1 , Charlotte Rieux 1 , Norbert Garnier 1 , Stéphane Bourg 1 , Pascal
Bonnet 2
1
2
Centre de Biophysique Moléculaire, UPR4301 CNRS (CBM) – CNRS – rue Charles Sadron, 45071
Orléans cedex 02, France
Institut de Chimie Organique et Analytique, UMR7311 (ICOA) – CNRS, Université d’Orléans – Pôle
de chimie de l’Université d’Orléans, Rue de Chartres, 45067 Orléans cedex 02, France
Les ADN glycosylases sont des enzymes qui initient le système de réparation par excision de
base (BER) en reconnaissant et éliminant les bases oxydées (de type radio-induites) dans l’ADN
tels que les résidus formamidopyrimidiques (FapyG), la thymine glycol (Tg) (lésions potentiellement létales associées à un blocage de la réplication) et la 8-oxoguanine (8-oxoG, lésion mutagène
associée à une haute fréquence de transversion G:C vers T:A) [1]. Bien que ces enzymes soient
impliquées dans la stabilité génétique des organismes, des travaux récents indiquent que les systèmes de réparation de l’ADN et en particulier les enzymes du système BER constituent des
cibles pharmacologiques pertinentes pour développer des stratégies anticancéreuses alternatives
et combinatoires [2,3]. Ces systèmes de réparation peuvent être en effet à l’origine de mécanismes d’échappements aux traitements anticancéreux dont l’un des objectifs est d’endommager
l’ADN de la cellule cancéreuse (exemple : la radiothérapie). Ces observations récentes justifient
la recherche d’inhibiteurs de ces enzymes. Sur ce thème général, nous avons initié cette étude sur
les ADN glycosylases de la surperfamille structurale “ H2TH ”. Une première approche structurefonction de l’inhibition de cette classe d’enzyme par des nucléobases sera présentée [4].
Dalhus, B., Laerdahl, J. K., Backe, P. H. and Bjoras, M. (2009) DNA base repair–recognition
and initiation of catalysis. FEMS microbiology reviews 33, 1044-1078.
Helleday, T. (2011) DNA repair as treatment target. Eur. J. Cancer 47 (Sup3), S333-S335.
Taricani, L., Shanahan, F., Pierce, R.H., Guzi, T.J. and Parry, D. (2010) Phenotypic enhancement of thymidylate synthetase pathway inhibitors following ablation of Neil1 DNA glycosylase/lyase. Cell cycle 9, 4876-4883.
Biela, A.; Coste, F.; Culard, F.; Guerin, M.; Goffinont, S.; Gasteiger, K.; Cieśla, J.; Winczura,
A.; Kazimierczuk, Z.; Gasparutto, D.; Carell, T.; Tudek, B.; Castaing, B. (2014) Zinc finger
oxidation of Fpg/Nei DNA glycosylases by 2-thioxanthine: biochemical and X-ray structural
characterization. Nucleic Acids Res. 42(16), 10748-10761.
∗
†
Intervenant
26
COMMUNICATION ORALE
Structural characterization of Plasmodium
falciparum CCT and fragment-based drug
design approach for targeting phospholipid
biosynthesis pathway
Ewelina Guca
∗† 1
, François Hoh 2 , Jean-François Guichou 2 , Henri Vial 1 ,
Rachel Cerdan 1
1
Dynamique des interactions membranaires normales et pathologiques (DIMNP) – CNRS : UMR5235,
Université de Montpellier – BT 24 CC 107 Place Eugène Bataillon 34095 MONTPELLIER CEDEX 5,
France
2
Centre de Biochimie Structurale (CBS) – CNRS : UMR5048, Inserm : UMR1054, Université de
Montpellier – 29 rue de Navacelles 34090 MONTPELLIER Cedex, France
Phospholipid synthesis metabolic pathways in Plasmodium falciparum are validated drug
targets for new type of antimalarials. In the de novo Kennedy pathway of phosphatidylcholine
biosynthesis, the second step catalyzed by CTP:phosphocholine cytidylytransferases [2.7.7.15] is
rate limiting and appears essential for the parasite survival at its blood stage. We are focused on
the structural characterization of this enzyme, the identification of effectors by fragment-based
drug design approach (FBDD) and then their optimization to eventually design a lead. We solved
the first reported crystal structure of the catalytic domain of the enzyme target (PfCCT) at resolution 2.2 Å, 3 enzyme-substrates complexes (CMP-, phosphocholine- and choline-bound forms)
at resolutions 1.9-2 Å and an enzyme-product (CDP-choline) complex structure at resolution
2.4 Å that give detailed images of binding pocket, demonstrate conformational changes between
apo- and holo-protein forms and provide the information about the mechanism of the catalytic
reaction at atomic level. The FBDD method uses a library of small molecules (fragments) with
molecular weight that does not exceed 300 Da to explore target binding sites. Primary screening
of fragment library (300 molecules) has been investigated by fluorescence-based thermal shift
assay and Nuclear Magnetic Resonance Saturation Transfer Difference (NMR STD) method is
used as a secondary screen to eliminate false positive ligands. This combination of techniques
identified so far 4 fragment hits that are currently evaluated for their binding modes and affinities. Co-crystallization of the protein-fragments complexes is carrying out to provide accurate
information on the binding modes of the small molecules and topology of interactions will be
used to rationally monitor every iterative round of the optimization process allowing subsequent
rational design.
∗
†
Intervenant
27
COMMUNICATION ORALE
Understanding the mechanism of UGT74B1
from Arabidopsis thaliana: S- or
O-glycosyltransferase ?
Pierre Lafite ∗ 1 , Sabine Montaut 2 , Stéphanie Marques 1 , Sami Marroun 3 ,
Gaël Coadou 3 , Hassan Oulyadi 3 , Marie Schuler 1 , Arnaud Tatibouet 1 ,
Patrick Rollin 1 , Richard Daniellou 1
1
Institut de Chimie Organique et Analytique (ICOA) – CNRS : UMR7311, Université d’Orléans –
UFR Sciences Rue de Chartres - BP 6759 45067 ORLEANS CEDEX 2, France
2
Laurentian University – Dpt of Chemistry and BIOchemistry ON P3E 2C6, Canada
3
Chimie Organique et Bioorganique : Reactivité et Analyse (COBRA) – CNRS : UMR6014, Institut
National des Sciences Appliquées [INSA] - Rouen – Rue Tesniere - 76821 - Mont Saint Aignan, France
Carbohydrates play an important part in a vast array of biological processes and therefore
glycomimetics are currently becoming a powerful class of novel therapeutics. Amongst them,
thioglycosides, or S -glycosides in which a sulfur atom has replaced the glycosidic oxygen atom,
are tolerated by most biological systems. Their major advantages rely on the fact that they
adopt similar conformations than the corresponding O-glycosides and especially that they prove
to be less sensitive to acid/base or enzyme-mediated hydrolysis. So far, few examples of natural
S -glycosides were reported in the literature. Until recently, plant glucosinolates were the only
group of S -glycosides that were characterized in Nature. These compounds are found in brassicae
and are involved in the plant protective strategy, called “mustard oil bomb”, that occurs after
their hydrolysis and subsequent degradation in toxic sulfur-containing compounds (thiocyanates,
isothiocyanates, ...).
Arabidopsis thaliana S -glycosyltransferase UGT74B1 catalyzes the formation of S-glucosidic
bond in the aromatic glucosinolates bionsynthetic pathway, using UDP-glucose as sugar donor
and thioxydromixate as thiol acceptor.
In order to assess the potency of this enzyme as a biocatalyst tool to generate unnatural S glycosides, its versality towards sugar donor and acceptor was tested. This could be efficiently
illustrated by the first chemo-enzymatic synthesis of glucosinolates analogues bearing other sugar
moiety than glucose, using other UDP-sugar donors. Using a range of acceptors, UGT74B1 could
perform S -glycosylation, as well as O-glycosylation, yet with a much lower efficiency. However,
the specificity for thiol vs. alcohol was correlated not only to the nature of the atom, but also
to the chemical properties of the acceptor (eg. pKa).
This was experimentaly demontrasted by ‘tuning’ the pKa of a range of alcohol acceptors so that
O-glycosylation rates increased to a level close to S -glycosylation.
∗
Intervenant
28
COMMUNICATION ORALE
Monitoring the transition of apo into holo
forms of soluble glucose dehydrogenase by
stopped-flow and crystallographic studies to
decipher the mechanism of
reconstitution/activation of this enzyme.
Claire Stines-Chaumeil
3
∗† 1
, François Mavre 2 , Brice Kauffmann
, Benoit Limoges 2 , Nicolas Mano
∗
1
1
Centre de recherches Paul Pascal (CRPP) – CNRS : UPR8641 – 115, Av Albert Schweitzer 33600
PESSAC, France
2
Laboratoire d’electrochimie moleculaire (LEM) – Université Paris VII - Paris Diderot – UMR CNRS P7 7591 Université Paris Diderot - Paris 7 Bât . Lavoisier, 15 rue Jean-Antoine de Baïf 7e Etage - case
7107 75205 Paris Cedex 13, France
3
Institut Européen de Chimie et Biologie (IECB) – CNRS : UMS3033, Université de Bordeaux
(Bordeaux, France) – 2 rue Robert Escarpit, F-33607 Pessac, France
Soluble glucose dehydrogenase (s-GDH) from Acinetobacter calcoaceticus belongs to the family of quinoproteins and catalyzes the oxidation of glucose into gluconolactone. This enzyme is
widely use in biotechnology, e.g. for glucose biosensors or enzymatic biofuel cells(1).
Here we focus on the mechanism of the reconstitution and activation process of the s-GDH which
is produced under apo form in Escherichia coli. Understanding this process is of interest to improve its use in electrochemical devices such as immunosensors. If heterogeneous reconstitution
studies have been recently published (2), there are no detailed studies of the homogeneous reconstitution of the s-GDH.
To decipher this reconstitution/activation mechanism, we combined stopped-flow experiments,
crystallographic studies and simulations. Our data indicates that this mechanism can be explained with a square schema mechanism with two different pathways.
Flexer V. et al., Anal.Chem, 2011, 83 (14), 5721-7
Zhang L. et al., Anal. Chem, 2014, 86 (4), 2257-67.
∗
†
Intervenant
29
COMMUNICATION ORALE
Mécanisme de la 3-mercaptopyruvate
sulfurtransférase : une enzyme impliquée dans
la production de sulfure d’hydrogène.
Jean-Christophe Lec ∗ 1 , Sabrina Colin , Séverine Boutserin , Hortense
Mazon , François Talfournier† , Sandrine Boschi-Muller‡
1
UMR 7365 CNRS-Université de Lorraine (IMoPA) – CNRS : UMR7365, Université de Lorraine – 9
Avenue de la Foret de Haye, 54505 Vandoeuvre les Nancy, France
La 3-mercaptopyruvate sulfurtransférase (3-MST) est une enzyme ubiquitaire qui participe
à la formation de sulfure d’hydrogène (H2S), un messager cellulaire pour les cellules eucaryotes
ayant également un effet cytoprotecteur. En effet, H2S est un gaz toxique, qui entraine une inhibition de la respiration mitochondriale via son action sur la cytochrome oxydase, mais également
une molécule de signalisation qui interviendrait au niveau de la neuromodulation dans le cerveau
et de la relaxation du muscle lisse dans le système vasculaire. La 3-MST eucaryote est à la fois
mitochondriale et cytosolique, tandis que les autres systèmes enzymatiques qui produisent H2S
sont exclusivement cytosoliques. La 3-MST est une enzyme à cystéine essentielle composée de
deux domaines rhodanèses qui catalyse le transfert de l’atome de soufre du 3-mercaptopyruvate
sur un accepteur de soufre (cyanure ou thiol in vitro), via la formation d’un intermédiaire persulfure sur sa cystéine catalytique. Dans le cas d’un accepteur à thiol, la décomposition du produit
de la réaction conduit à la formation d’H2S. L’étude détaillée du mécanisme catalytique des 3MST humaine et d’Escherichia coli a été réalisée par des approches cinétiques (état stationnaire
et cycle catalytique unique, systèmes enzymatiques couplés), de spectroscopie de fluorescence et
de spectrométrie de masse, incluant l’élaboration de stratégies originales afin de pouvoir étudier
les différentes étapes du mécanisme. Les résultats montrent que la première étape de transfert
de soufre est très rapide, et que l’étape cinétiquement limitante est associée à la libération du
pyruvate pour l’enzyme humaine et à la seconde étape de transfert de soufre pour l’enzyme
bactérienne.
∗
Intervenant
‡
†
30
31
CONFERENCE PLENIERE
Approches correlatives en analyse
molecule-unique
Terence Strick
1
∗ 1
IJM (Institut Jacques Monod) – CNRS : UMR7592, Université de Paris Diderot – 15 rue Helene
Brion 75013 Paris, France
Les mesures sur molecules individuelles permettent d’obsever en temps reel des interactions
moleculaires discretes et ainsi d’eviter le moyennage intrinseque a une mesure effectuee sur une
population de molecules. Ces approches sont particulierement utiles pour l’analyse de systemes
moleculaires multi-composantes et de leurs reactions typiquement multi-etapes. Cependant si
d’un cote la manipulation de molecule unique permet de deceler par sa signature ”mecanique”
une interaction moleculaire, et donc eventuellement un etat catalytique, elle permet difficilement
de connaitre la composition exacte d’un complexe qui pourrait avoir une telle interaction. D’un
autre cote la detection par fluorescence molecule-unique permet d’observer la (co)localisation
de molecules individuelles, mais renseigne difficilement (en dehors de cas specfiques tels le
TIRF) sur l’etat catalytique du systeme observe. La combinaison des deux approches permet ainsi de simultanement detecter en temps reel la composition et l’etat catalytique de systemes moleculaires complexes. Un tel systeme permet de reconstituer la voie de reparation
dite transcriptionellement-couplee d’Escherichia coli comprenant six proteines distinctes, et ainsi
commencer a etudier les liens entre composition et catalyse dans les processus de reparation
d’ADN.
∗
Intervenant
32
COMMUNICATION ORALE
Régulation des IMPDHs bactériennes au
travers des modules CBS
Hélène Munier-Lehmann
1
∗ 1
Institut Pasteur, Unité de Chimie et Biocatalyse, CNRS UMR3523 – Institut Pasteur de Paris, CNRS
UMR3523 – France
L’inosine 5’-monophosphate déshydrogénase (IMPDH) est une enzyme essentielle et ubiquitaire intervenant dans la voie de biosynthèse de novo du métabolisme des purines et responsable
de la conversion de l’IMP en XMP (1,2). Les IMPDHs humaines ont été largement documentées
et sont la cible de médicaments utilisées en clinique.
Les IMPDHs sont décrites sous forme tétramérique : chaque monomère comprend un domaine
central contenant le site actif, et un domaine Bateman constitué de deux modules cystathionineß-synthase (CBS).
Par une approche multidisciplinaire, nous avons démontré que l’IMPDH de Pseudomonas aeruginosa est une enzyme allostérique et octamérique (3), deux propriétés qui n’avaient jamais été
décrites jusqu’à présent. Ces travaux ont été étendus à sept autres IMPDHs bactériennes permettant de définir deux classes (4). Les IMPDHs de la classe 1 (incluant l’IMPDH de P. aeruginosa)
présentent une coopérativité vis-à-vis de l’IMP avec une activation par le MgATP et sont sous
forme octamérique quelles que soient les conditions testées. Les IMPDHs de la classe 2 ont une
cinétique de type michaelienne, comme ce qui a été rapporté dans la littérature sur les IMPDHs
eucaryotes. Par contre, et de façon surprenante, leur état d’oligomérisation est régulé par le
MgATP en passant d’un état tétramérique à un état octamérique. Ce sont les modules CBS qui
jouent un rôle clé soit dans la régulation de l’activité catalytique, soit dans la modulation de la
structure quaternaire. Ainsi, les modules CBS sont fonctionnels au sein des IMPDHs, et pourraient être la cible d’inhibiteurs allostériques pour le développement de nouveaux antibiotiques.
Références :
1. Hedstrom, L. (2009) IMP dehydrogenase: structure, mechanism, and inhibition. Chem.
Rev. 109, 2903-2928
2. Pankiewicz, K. W., and Goldstein, B. M. (2003) Inosine Monophosphate Dehydrogenase:
A Major Therapeutic Target. ACS Symposium Series
3. Labesse, G., Alexandre, T., Vaupre, L., Salard-Arnaud, I., Him, J. L., Raynal, B., Bron,
P., and Munier-Lehmann, H. (2013) MgATP Regulates Allostery and Fiber Formation in IMPDHs. Structure 21, 975-985
4. Alexandre, T., Raynal, B., and Munier-Lehmann, H. (2015) Two classes of bacterial IMPDHs
according to their quaternary structures and catalytic properties PloS one in press
∗
Intervenant
33
COMMUNICATION ORALE
Identification of Discriminant
Conformation-Dependent Residues of Protein
Kinases: a Proteometric Analysis
Nicolas Bosc
∗† 1
, Berthold Wroblowski 2 , Samia Aci-Sèche 1 , Christophe
Meyer 3 , Pascal Bonnet‡ 1
1
2
Janssen Research Development, a division of Janssen Pharmaceutica N.V. – Turnhoutseweg 30, 2340
Beerse, Belgique
3
Centre de Recherche Janssen-Cilag – Johnson Johnson – Campus de Maigremont - CS 10615, 27106
Val de Reuil CEDEX, France
Imatinib, a Bcr-Abl tyrosine-kinase inhibitor, was the first protein kinase inhibitor that progressed to the market in 2002. Since then, pharmaceutical companies strongly pursued research
efforts to discover novel kinase inhibitors. Today, seven kinase inhibitors binding to the inactive
conformation of specific protein kinases have been approved by the FDA. Although imatinib is
a Type-II inhibitor and four more Type-II inhibitors were approved in 2012, they still represent
less than a third of approved protein kinase inhibitor class. Since part of their chemical structure
binds outside of the ATP pocket, Type-II inhibitors could therefore increase kinase selectivity
in comparison to the classical Type-I inhibitors binding the ATP site. Hence, the identification
of key residues responsible for the binding of Type-II inhibitors could help understanding the
protein conformational changes (active to inactive state) as well as designing better selective
kinase inhibitors. The novel proteometric approach developed in the SB&C group, combines
residue descriptors of protein kinase sequences and biological activities of several Type-II kinase
inhibitors. Using Partial Least Squares (PLS) regression, we determined 29 key residues responsible for high biological activities of Type-II inhibitors. Among these residues, the gatekeeper
residue was found to be the most relevant suggesting its essential role in protein kinase conformational changes. Using the newly developed proteometric model, we also predicted the propensity
of each protein kinase to trap Type-II inhibitors. These results were further validated using two
external datasets of protein-ligand activity pairs. Other residues present in the kinase domain,
and more specifically in the binding site, have been highlighted by this approach, but their role
in biological mechanisms is still under investigation.
∗
Intervenant
‡
†
34
COMMUNICATION ORALE
La Carbon Monoxide Dehydrogenase (CODH)
de Desulfovibrio vulgaris.
Jessica Hadj-Said ∗† 1 , Maria-Eirini Pandélia 2 , Christophe Léger‡ 1 ,
Vincent Fourmont§ 1 , Sébastien Dementin¶ 1
1
2
Bioénergétique et ingénierie des protéines (BIP) – CNRS : UPR9036 – 31 Chemin Joseph Aiguier
13402 MARSEILLE CEDEX 20, France
Department of Chemistry, The Pennsylvania State University – 224 Chemistry Building University
Park, PA 16802 USA, États-Unis
Les Carbon Monoxide Dehydrogenases (CODH) à nickel catalysent l’oxydation réversible du
CO en CO2. Dans certains bactéries anaérobies, elles favorisent l’établissement d’une force proton motrice pour la synthèse d’ATP. Elles sont aussi impliquées dans la fixation du CO2 [1]. Ce
sont des enzymes homodimériques qui contiennent deux sites actifs [Ni-4Fe-4S] et trois centres
[4Fe-4S] [2]. Le mécanisme catalytique communément accepté implique la conversion du site actif
de l’enzyme d’un état inactif, appelé Cox, en deux états actifs dans des conditions réductrices:
Cred1 (Cox réduit par un électron) et Cred2 (Cred1 réduit par deux électrons) [3].
Le génome de Desulfovibrio vulgaris contient un opéron putatif codant pour une CODH (cooS)
et une protéine de maturation (cooC) [4]. Par une combinaison d’approches biochimiques et
spectroscopiques, nous avons caractérisé deux formes de la CODH produite en absence ou en
présence de CooC, désignées CooS et CooSC, respectivement.
CooS a un faible contenu en nickel et est presque inactive. Aucune signature du site actif
n’est détectée par résonance électromagnétique électronique (RPE), ce qui renforce l’idée que
CooC est requis pour promouvoir in vivo la construction d’un site actif fonctionnel.
CooSC contient du nickel en quantité substoechiométrique, catalyse l’oxydation réversible du
CO en CO2, mais n’est pas totalement active après purification. En effet, son activité n’est
maximale qu’après incubation en présence de nickel exogène dans des conditions réductrices.
Une caractérisation par RPE des échantillons de CooSC a cependant montré que le site actif est
dans l’état Cred1 et/ou Cred2 aussi bien avant qu’après activation par le nickel.
Nos résultats ont donc permis de mettre en évidence un processus d’activation inattendu de
la CODH qui n’implique apparemment pas de changement structural du site actif. Ce mécanisme d’activation est indétectable par RPE et requiert le nickel exogène dont le rôle reste à
déterminer.
∗
Intervenant
‡
¶Auteur
[email protected]
Auteurcorrespondant:
correspondant:
§
Auteur correspondant:
¶
†
35
COMMUNICATION ORALE
Can, Mehmet and Armstrong, Fraser A. and Ragsdale, Stephen W., Structure, Function, and
Mechanism of the Nickel Metalloenzymes, CO Dehydrogenase, and Acetyl-CoA Synthase, Chem.
Rev., 114, 4149–4174, 2014
Holger Dobbek, Vitali Svetlitchnyi, Lothar Gremer, Robert Huber, and Ortwin Meyer. Crystal
structure of a carbon monoxide dehydrogenase reveals a [Ni-4Fe-5S] cluster. Science, 293(5533)
:1281–1285, August 2001.
Heo, J., Staples, C. R., Tesler, J., and Ludden, P. W. Rhodospirillum rubrum CO Dehydrogenase. Part 2. Spectroscopic Investigation and Assignment of Spin-Spin Coupling Signals. J Am
Chem Soc 121, 11045-11057, 1999
Rajeev, Lara and Hillesland, Kristina L. and Zane, Grant M. and Zhou, Aifen and Joachimiak,
Marcin P. and He, Zhili and Zhou, Jizhong and Arkin, Adam P. and Wall, Judy D. and Stahl,
David A. Deletion of the Desulfovibrio vulgaris Carbon Monoxide Sensor Invokes Global Changes
in Transcription. Journal of Bacteriology, 21(194):5783-5793, Nov 2012
36
COMMUNICATION ORALE
Study of the regulatory behavior of
Plasmodium falciparum IMP-specific
5’-nucleotidase (PfISN1)
Loic Carrique
∗ 1
, Lionel Ballut 1 , Sébastien Violot 1 , Nushin Aghajari†
1
1
Institut de biologie et chimie des protéines (IBCP) – Université Claude Bernard - Lyon I (UCBL),
CNRS : UMR5086, Bases Moléculaires et Structurales des Systèmes Infectieux, Equipe
Biocristallographie et Biologie Structurale des Cibles Thérapeutiques – 7 Passage du Vercors 69367
LYON CEDEX 07, France
Approximately 300 million people worldwide are affected by malaria. Plasmodium falciparum
induces a lethal form of malaria which causes more than 1-2 million deaths worldwide annually,
with a large number of victims being children. It has a massive impact on human health in
that it is the world’s second biggest killer after tuberculosis. Despite intense efforts an effective
vaccine is still not available.
Proteins involved in the purine salvage pathway are mandatory for regulating the pool of nucleotides during the inner red blood cells development of the parasite. Many of these proteins are
active in transient- or stable oligomeric states which are dependent on time and on the osmolyte
concentration present in the cells. One of these is an IMP-specific 5’-nucleotidase (Pf ISN1),
for which enzymatic studies have shown that the protein is active in an oligomeric form with
an allosteric regulation (Srinivasan & Balaram, 2007). Using different techniques, Dynamic
Light Scattering (DLS), Size Exclusion Chromatography (SEC) and Multi-Angle Light Scattering (SEC-MALS), we were able to conclude that the protein adopts a tetrameric state when
active, but also an octameric conformation. Screening of crystallization conditions have so far resulted in crystals diffracting X-rays to medium resolution. Due to the regulatory behavior of this
enzyme, Small Angle X-ray Scattering studies combined with SEC-MALS are of high importance
in order to contribute to the understanding of the influence of the environment on the quaternary
structure formation and thereby on the nucleotidase activity. Results obtained so far from the
combined use of biochemical- and biophysical approaches for the understanding of the reaction
mechanism of this important parasite enzyme from the purine salvage pathway will be presented.
References:
Bharath Srinivasan and Hemalatha Balaram, ISN1 nucleotidases and HAD superfamily protein
fold: in silico sequence and structure analysis, In Silico Biology 7, 0019 (2007)
∗
†
Intervenant
37
COMMUNICATION ORALE
Cellular redox signaling by thiol peroxidase :
Mechanisms responsible for the specificity of
the redox relay H2O2/Orp1/Yap1 in S.
cerevisiae
Antoine Berweiler 1 , Alexandre Kriznik 1 , Guy Branlant 1 , Sophie
Rahuel-Clermont ∗† 1
1
IMoPA UMR 7365 CNRS-Université de Lorraine (IMOPA) – CNRS : UMR7365, Université de
Lorraine – Biopôle, campus Biologie-Santé, 9 Avenue de la Forêt de Haye, CS 50184 54505 Vandœuvre-lès-Nancy, France
Thiol-peroxidases, described as antioxidant enzymes, have also been associated with peroxidedependent cell signaling as sensor and relay of the H2O2-mediated signal (1). One of the best
documented examples of such a mechanism is the activation of the transcription factor Yap1, a
key regulator of the transcriptional peroxide stress response in Saccharomyces cerevisiae, which
depends on the formation of intramolecular disulfide bonds catalyzed by the thiol peroxidase
Orp1 (2 ; 3). In this mechanism, it has been proposed that the relay occurs via the oxidation
of Orp1 peroxidatic Cys as a sulfenic acid intermediate which reacts with Yap1 to form a mixed
disulfide species (3). In addition, the Ybp1 protein has been identified as an essential partner for
the activation of Yap1 by Orp1 (4). The intrinsic reactivity of Orp1 sulfenic acid species could
potentially result in competition between Yap1 and other thiols, such as the regeneration Cys
of Orp1 or other cellular thiols. This raises the question of the specificity of Yap1 activation by
H2O2/Orp1. To address this question, and to elucidate the role of Ybp1 in this mechanism, we
have used an approach based on the kinetic characterization of the two reactions in competition:
the peroxydatic cycle of Orp1 and the reaction withYap1, using rapid kinetic stop flow and quench
flow techniques. From the study of the impact of Ybp1 on these kinetics and the characterization
of the protein-protein interactions between the three partners by fluorescence anisotropy and
microcalorimetry, we propose that Ybp1 and Yap1 recruit Orp1 within a ternary complex, which
restrains intramolecular disulfide formation within Orp1 and allows the reaction between Orp1
sulfenic intermediate and Yap1.
∗
†
Intervenant
38
COMMUNICATION ORALE
Amyloid properties of a conserved domain in
the N-Term domain of the Androgen Receptor
- Implications for new therapeutic routes Julia Asencio Hernández
1,2
, Marc-André Delsuc
∗ 2
1
NMRTEC (NMRTEC) – NMRTEC – Boulevard Sébastien Brandt, Bioparc, Bat. B 67400
Illkirch–Graffenstaden, France
2
Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) – CNRS : UMR7104, Inserm :
U964, université de Strasbourg – Parc D’Innovation 1 Rue Laurent Fries - BP 10142 67404 ILLKIRCH
CEDEX, France
The prostate cancer is under a strict hormonal androgen control, and develops initially as an
androgen-dependent disease that relies on the Androgen Receptor (AR) for growth and progression. The AR protein, in contrast with other nuclear receptors, has the peculiarity of presenting a
very large N-Terminal Domain (NTD) of about 560 residues, which is flexible with a high degree
of intrinsic disorder. The NTD has gain recent interest because of potential new anti-prostate
cancer molecules targeting this domain[1].
We have recently shown [2], that a short domain of the NTD, highly conserved across all vertebrate species (fig. 1), is able to readily form amyloid fibrils upon dimerisation by disulfide bridge
formation around Cys240. Unlike most amyloid systems, the fiber formation is reversible, and
depend only onthe redox state of the peptide. Peptides as short as 11-residues as well as longer
peptides, obtained from the AR primary sequence are able to trigger the fibrillation.
This points to the fact that this highly conserved domain could be involved in the regulation
of the activity of AR, through a control of the aggregation state. The possibility that amyloid
formation is a general mechanism for the regulation of the activity of a protein has already been
proposed[3]. The AR protein is already known to aggregates into amyloid fibers[4]. This aggregation property is usually associated to the presence of a poly-glutamine tract (polyQ), known
to be involved in several pathologies[5].
Biophysical experiments, in-vivo experiments, and sequence analysis are presented, and confort the link between protein aggregation / redox state and prostate cancer pathology. If this
hypothesis proves true, it could form a possible target for prostate cancer therapy, and in general
for regulating the AR activity. Pre-screening of potential interactive molecules are currently in
progress.
J.K.Myung, C.A.Bañuelos, J.Garcia Fernandez, N.R.Mawji, J.Wang, A.H.Tien, Y.C.Yang, I.Tavakoli,
S.Haile, K.Watt, I.J.McEwan, S.Plymate, R.J.Andersen, and M.D.Sadar. An androgen receptor
N-terminal domain antagonist for treating prostate cancer. J. Clin. Invest. 123, 2948–2960
(2013).
∗
Intervenant
39
COMMUNICATION ORALE
J.Asencio-Hernández, C.Ruhlmann, A.McEwen, P.Eberling, Y.Nominé, J.Céraline, J-P.Starck
and M-A.Delsuc (2014) ChemBioChem 15(16):2370-3. Reversible Amyloid Fiber Formation in
the N Terminus of Androgen Receptor.
F.Chiti, and C.M.Dobson (2006) Annu. Rev. Biochem. 75, 333–366. Protein misfolding, functional amyloid, and human disease.
T.Jochum, M.E. Ritz, C.Schuster, S.F.Funderburk, K.Jehle, K.Schmitz, F.Brinkmann, M.Hirtz,
D.Moss, and A.C.B.Cato Toxic and non-toxic aggregates from the SBMA and normal forms of
androgen receptor have distinct oligomeric structures. Biochim Biophys Acta 1822, 1070–1078
(2012).
R.Kumar, H.Atamna, M.N.Zakharov, S.Bhasin, S.H.Khan, and R.Jasuja, Role of the androgen
receptor CAG repeat polymorphism in prostate cancer, and spinal and bulbar muscular atrophy.
Life Sciences 88, 565–571 (2011).
40
Ingénierie enzymatique et évolution
dirigée
41
CONFERENCE PLENIERE
Design and optimization of artificial enzymes:
nearer to nature
Donald Hilvert
1
∗† 1
Laboratory of Organic Chemistry – ETH Zürich, Zurich, Suisse
Protein design is a challenging problem. We do not fully understand the rules of protein
folding, and our knowledge of structure-function relationships in these macromolecules is at best
incomplete. Nature has solved the problem of protein design through the mechanism of Darwinian
evolution. From primitive precursors, recursive cycles of mutation, selection and amplification of
molecules with favorable traits have given rise to all of the many thousands of gene products in
every one of our cells. An analogous process of natural selection can be profitably exploited in
silico and in the laboratory on a human time scale to create, characterize and optimize artificial
catalysts for tasks unimagined by Nature. Recent progress in combining computational and
evolutionary approaches for enzyme design will be discussed, together with insights into enzyme
function gained from studies of the engineered catalysts.
∗
†
Intervenant
42
COMMUNICATION ORALE
Design computationnel de protéines avec une
fonction d’énergie MMGBSA
Thomas Gaillard ∗† 1 , Nicolas Panel 1 , David Mignon 1 , Thomas Simonson 1
1
Laboratoire de Biochimie (BIOC) – CNRS : UMR7654, Polytechnique - X – Laboratoire de Biochimie
Ecole Polytechnique 91128 Palaiseau, France
Le design de protéines a pour but la conception de nouvelles protéines ou la modification de
protéines existantes pour atteindre une fonction donnée. Les approches computationnelles sont
une aide précieuse pour le design de protéines, en aidant à rationaliser les prédictions et guider
les tests expérimentaux. Le design computationnel de protéines (CPD) a stimulé d’importants
efforts méthodologiques et a donné lieu à des succès spectaculaires comme la création d’une
protéine présentant un nouveau repliement ou encore l’ingénierie de sites actifs d’enzymes. La
principale difficulté du CPD réside dans le nombre astronomique de séquences et conformations
possibles, de l’ordre de (20x10)^100 pour une protéine de 100 acides aminés. Des algorithmes
d’exploration efficaces sont donc nécessaires. Un autre élément clé du succès du CPD est la
fonction d’énergie utilisée pour évaluer et trier les séquences et conformations.
Notre approche du CPD [1-10] se fonde sur un modèle atomique de la structure de la protéine et
sur une fonction d’énergie de mécanique moléculaire. Un élément important est le traitement du
solvant, représenté comme un continuum diélectrique à l’aide d’un terme de Generalized-Born,
complété par un terme proportionnel à la surface accessible au solvant. Les éléments clés de notre
implémentation sont : 1) le squelette de la protéine est maintenu fixe, 2) l’espace des conformations des chaînes latérales est réduit à une librairie discrète de rotamères, 3) la fonction d’énergie
est décomposée en paires d’interactions précalculées. La première étape consiste à calculer une
matrice des interactions entre chaque paire de rotamères. Dans l’étape suivante, l’espace des
séquences et conformations est exploré à l’aide d’un algorithme d’optimisation. L’évaluation
de l’énergie dans cette deuxième étape est rapide grâce au précalcul de la matrice d’énergie.
L’implémentation de notre procédure de CPD sera d’abord présentée, puis les applications à la
prédiction de conformations de chaînes latérales et au design de séquences entières seront abordées.
A. Lopes, A. Alexandrov, C. Bathelt, G. Archontis, T. Simonson, Proteins (2007), 67, 853-867.
M. Schmidt am Busch, A. Lopes, N. Amara, C. Bathelt, T. Simonson, BMC Bioinformatics
(2008), 9, 148.
M. Schmidt am Busch, A. Lopes, D. Mignon, T. Simonson, J. Comput. Chem. (2008), 29,
1092-1102.
∗
†
Intervenant
43
COMMUNICATION ORALE
M. Schmidt am Busch, D. Mignon, T. Simonson, Proteins (2009), 77, 139-158.
A. Lopes, M. Schmidt am Busch, T. Simonson J. Comput. Chem. (2010), 31, 1273-1286.
M. Schmidt am Busch, A. Sedano, T. Simonson, PLOS One (2010), 5, e10410.
N. Amara, C. Aubard, P. Plateau, T. Simonson, G. Archontis, Proteins (2011), 79, 3448-3468.
M. Schmidt am Busch, A. Lopes, D. Mignon, T. Gaillard, T. Simonson, in J. Zeng, R. Zhang,
H. Treutlein (Ed.), Quantum Simulations of Materials and Biological Systems. Springer Verlag,
New York, 2012.
T. Simonson, T. Gaillard, D. Mignon, M. Schmidt am Busch, A. Lopes, N. Amara, S. Polydorides, A. Sedano, K. Druart, G. Archontis, J. Comput. Chem. (2013), 34, 2472-2484.
T. Gaillard, T. Simonson, J. Comput. Chem. (2014), 35, 1371-1387.
44
COMMUNICATION ORALE
Cost Function Network Optimization: exact
algorithms towards Computational Protein
Design
Seydou Traore ∗ 1 , Kyle Roberts 2 , David Allouche 3 , Bruce Donald 2 ,
Isabelle André 1 , Thomas Schiex 3 , Sophie Barbe† 1
1
Université de Toulouse; INSA,UPS,INP; LISBP (LISBP) – Institut National des Sciences Appliquées
(INSA) - Toulouse, Institut national de la recherche agronomique (INRA) : UMR792, CNRS :
UMR5504 – 135 Avenue de rangueil 31077 Toulouse cedex 04, France
2
Department of Biochemistry, Department of Computer Science, Department of Chemistry – Duke
University, Durham, NC, États-Unis
3
Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA) – Institut national de
la recherche agronomique (INRA) : UR875 – Chemin de Borde Rouge, 31320 Castanet Tolosan, France
Computational protein design (CPD) aims at finding a set of sequences that can accommodate a given protein scaffold and accomplish some wanted biochemical properties. Other the last
decade, it has demonstrated is potential to adequately capture fundamental aspects of molecular
recognition and holds great interest for applications ranging from medicine, biotechnology and
synthetic biology and nanotechnologies (see for example [1]).
CPD is a challenging optimization problem. In the simplest form, the CPD problem assumes
a fixed protein backbone and, for each type of amino acid considered at a given position, allows the side-chains to move only among a set of discrete and low-energy conformations, called
rotamers. Hence, the optimization problem consists in identifying combinations of rotamers at
designable specified positions such that an objective function is minimized (global minimum
energy conformation or GMEC). This problem has been proven to be NP-hard [2]. The traditional exact approach is the combination of the Dead-End Elimination preprocessing and the A*
search algorithm (DEE/A*). However, the DEE/A* framework becomes impractical for complex CPD instances, one of the main challenges being the exponential size of the conformational
and sequence protein space that has to be handled. Because of these difficulties, meta-heuristic
approaches including sampling methods (mainly based on the Monte Carlo algorithm) and population based approaches (such as the Genetic Algorithm) were privileged. However, only exact
approaches can ensure that discrepancies between CPD predictions and experimental results
come exclusively from the inadequacies of the biophysical model and not from the optimization
algorithm. This is an important property in design cycles involving several runs of CPD modelling, computational optimization, and experimental testing. In addition, exact approaches may
stop before meta-heuristic approaches because they can determine that the optimum is reached.
Finally, empirical studies showed that the accuracy of meta-heuristics tends to degrade as the
problem size increases [3].
∗
†
Intervenant
45
COMMUNICATION ORALE
Thence, in our previous works, we showed that a recent combinatorial optimization technique, defined in the field of “Cost Function Networks” (or Weighted Constraint satisfaction) can push CPD
beyond the limit of usual tools [4-6]. The performance of the Cost Function Network (CFN) optimization was compared to several other well-known optimization strategies toward the GMEC
identification problem. These methods included 0/1 Linear and Quadratic Programming, 0/1
Quadratic Optimization, Weighted Partial MaxSAT, Graphical Model optimization problems
and the commonly used CPD dedicated framework Dead-End Elimination/A* (DEE/A*). The
CFN framework outperformed all these methods both in terms of number of instances solved
but also in terms of runtime performance.
Following this methodology, we have developed and implemented new CFN-based search strategies and heuristics within the OSPREY CPD-dedicated software [7]. The performance of the
algorithms was examined using a benchmark set of 30 CPD instances. These new methods bring
important speedups compared to the well-known DEE /A* framework for solving numerous CPD
problems. Among the new developments, we have introduced a new Best-First search strategy
using the CFN lower bound as heuristics. This method led to an important speedup compared
to the traditional A* algorithm. A new Side Chain Positioning (SCP)–based upper bounding
heuristics have need developed for preprocessing pruning efficiency. This heuristic was extended
to define a new branching scheme which incrementally performs SCP-based upper bounding
during search. The new branching scheme allowed to more efficiently find the GMEC but more
importantly, it enables to enumerate sub-optimal solutions over a larger energy range.
These results open the door to the exact solving of CPD optimization problems with runtime
performances that compete against those of heuristics while guaranteeing accuracy. Beyond
providing a design framework and computational tools to facilitate the optimization of highly
combinatorial design cases, our approach is also applicable to design strategies that integrate
more flexibility with respect to backbone, and continuous sidechain modelling [8].
Acknowledgments
This work has been funded by the “Agence Nationale de la Recherche”, references ANR 10-BLA0214 and ANR-12-MONU-0015-03. We thank the Computing Center of Region Midi-Pyrénées
(CALMIP, Toulouse, France) and the GenoToul Bioinformatics Platform of INRA-Toulouse for
providing computing resources and support. S. Traoré was supported by a grant from the INRA
and the Region Midi-Pyrénées. K.E. Roberts and B.R. Donald supported by NIH Grant 2-R01GM-78031-5.
References
Khare,S.D. et al. Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis. Nat. Chem. Biol., 8, 294–300. 2012.
Pierce,N.A. and Winfree,E. (2002) Protein Design is NP-hard. Protein Engineering, 15, 779–782.
Voigt,Christopher A. et al. Trading accuracy for speed: A quantitative comparison of search
algorithms in protein sequence design. Journal of Molecular Biology, 299, 789–803, 2000.
D. Allouche, J. Davies, S. de Givry, G. Katsirelos, T. Schiex, S. Traoré, I. André, S. Barbe, S.
Prestwich, B. O’Sullivan. Computational Protein Design as an Optimization Problem. Artificial
Intelligence Journal, March 2014.
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Traoré S, Allouche D, André I, de Givry S, Katsirelos G, Schiex T and Barbe S. A new framework
for computational protein design through Cost function optimization. Bioinformatics, 2013.
Traoré S, Allouche D, André I, de Givry S, Katsirelos G, Barbe S and Schiex T. Computational Protein Design as a Cost Function Network Optimization Problem. CP2012 Proceedings.
18th International Conference on Principles and Practice of Constraint Programming, Québec,
Canada, Oct, 8-12, 2012– Proceeding.
P. Gainza et al. OSPREY: Protein design with ensembles, flexibility, and provable algorithms.
Methods Enzymol. 2013;523:87-107.
Hallen,M.A. et al. Dead-end elimination with perturbations (DEEPer): a provable protein design
algorithm with continuous sidechain and backbone flexibility. Proteins, 81, 18–39. 2013.
47
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Computational approach to enzyme design
David Rinaldo
1
∗ 1
Schrödinger GmbH (SCH) – Dynamostraße 13 68165 Mannheim, Allemagne
Enzyme design is an important area of ongoing research with a broad range of applications
in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we
develop a computational protocol using an approach that combines molecular dynamics, docking,
and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focus is
to understand the molecular basis of substrate recognition and catalysis. We apply the method
to a variety of enzymes and substrates and in most cases correctly identify the mutations that
yield the greatest improvements in enzyme activity. Also, we confirm that the computationally
predicted structures reproduce experimental crystal structures, when available. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can
aid in the redesign of enzymes with improved catalytic efficiency.
∗
Intervenant
48
Les enzymes comme cibles
thérapeutiques
49
CONFERENCE PLENIERE
Enzymes as target class for approved drugs
The case of secretory phospholipase A2
(sPLA2) family of proteins as novel
therapeutic targets
Dominique Douguet
1
∗† 1
Institut de pharmacologie moléculaire et cellulaire – CNRS : UMR7275, Université Nice Sophia
Antipolis (UNS) – CNRS-IPMC 660 Route des lucioles 06560 VALBONNE, France
To facilitate drug discovery, there is a need to analyze the current landscape of approved
drugs in terms of 3D chemical structures concomitantly with pharmacodynamics and pharmacokinetics data. We have addressed this question by constructing the public e-Drug3D database
which contains FDA-approved drugs. e-Drug3D provides ready-to-screen electronic collections
to assist in drug repurposing and fragment-based drug discovery. A short global review will be
presented with a particular focus on enzyme inhibitors. Over the years, secretory phospholipase
A2 (sPLA2) family of proteins has been shown to participate in several pathologies like neurodegenerative, inflammatory, cardiovascular, metabolic, infectious, and neoplastic diseases in
humans. However, while the extracellular localization of sPLA2 isoforms makes them feasible
targets for new therapeutics, such drug developments have failed. Here, we will make an overview
of the structure-based design of inhibitors of human sPLA2s.
∗
†
Intervenant
50
COMMUNICATION ORALE
Développement d’antibactériens dirigés contre
la ThyX de Mycobacterium tuberculosis
Kamel Djaout
1
∗† 1
, Marten Vos 1 , Hubert F. Becker 1 , Hannu Myllykallio
1
Laboratoire d’optique et biosciences (LOB) – Polytechnique - X, Inserm : U1182, CNRS : UMR7645 –
Route de Saclay 91128 PALAISEAU CEDEX, France
La tuberculose est un est problème de santé publique mondial, avec plus de 1.5 million de
morts en 2013 (OMS). L’apparition et la propagation de souches multi et extensivement résistantes de Mycobacterium tuberculosis, bactérie causant la tuberculose, met en exergue le besoin
urgent de développer nouvelle molécules thérapeutique. ThyX est une cible intéressante dans
M. tuberculosis : elle est essentielle à la survie de la bactérie et ne présente pas d’homologie
de structure ou de mecanisme avec son équivalent humain, ThyA. De plus, ThyX se retrouve
surexprimée dans les souches de Mycobacterium tuberculosis résistantes aux molécules déjà sur
le marché. ThyX est une thymidylate synthase flavine-dépendante. Elle catalyse la synthèse de
novo de désoxythymidine monophosphate, un précurseur essentiel de l’ADN. Nous avons étudié
le mécanisme réactionnel détaillé de la ThyX de Mycobacterium tuberculsosis et montrons des
différences majeures de réactivité comparé aux autres ThyX décrites. Nous avons aussi criblé
pour des inhibiteurs spécifiques de ThyX Mtb. Les molécules issues de ce crible présentent d’une
part un nouveau mode d’inhibition des ThyX, ciblant les sous-sites de fixation d’au moins deux
substrats; et d’autre part un pouvoir antibactérien sur Mycobacterium smegmatis et Mycobacterium tuberculosis, avec des CMI variant de 5 à 20 µg/ml. Nous mettons aujourd’hui en place
des outils computationnels (modèles QSAR et pharmacophore) afin de cribler in silico pour de
meilleurs candidats d’inhibition de la ThyX de Mycobacterium tuberculosis.
∗
†
Intervenant
51
COMMUNICATION ORALE
Molecular dynamics study of ligand
recognition by DXR: implications for the
design of new antibiotic compounds
Fanny Krebs
∗ 1
, Roland Stote 1 , Annick Dejaegere
1
1
IGBMC – CNRS : UMR7104, Inserm : UMRU964, université de Strasbourg – Biocomputing group Structural Biology Genomics Dept. - IGBMC 1 rue Laurent Fries 67404 Illkirch-Graffenstaden Cedex,
France
Most eubacteria, including pathogenic ones, use the 2-C -methyl-D-erythritol 4-phosphate
(MEP) pathway to synthesize the basic units that lead to the production of isoprenoids, which
are important secondary metabolites that have diverse roles in different biological processes.
Since the MEP pathway is not present in human, enzymes of this pathway are promising targets
for the development of new antibiotic drugs. The second enzyme of the MEP pathway is 1-desoxyD-xylulose 5-phosphate reductoisomerase (DXR), which catalyzes the conversion of the 1-desoxyD-xylulose 5-phosphate (DXP) via an intra-molecular rearrangement in presence of a metallic
cation (Mg2+, Mn2+), followed by a reduction involving the cofactor, nicotinamide adenine
dinucleotide phosphate (NADPH). X-Ray structures studies identified large-scale conformational
changes of active site loops that likely regulate ligand recognition. To investigate the mechanism
of ligand binding to the enzyme active site, we used molecular dynamics simulations. Analysis
of the simulations focused on the structural and functional impact of important active site
residues. Understanding the interactions between ligands and the protein target as a function of
conformation can contribute to the development of new therapeutic compounds.
∗
Intervenant
52
COMMUNICATION ORALE
Discovery of Oligopyridyl scaffold molecules as
potent Mcl-1 inhibitors
Jana Sopkova-De Oliveira Santos ∗ 1 , Jade Fogha 1 , Anne Sophie
Voisin-Chiret 1 , Ronan Bureau 1 , Laurent Poulain 2 , Céline Gloaguen
1
2
CERMN FR CNRS INC3M – Université de Caen Basse-Normandie : EA4258 – bd Becquerel 14032
Caen, France
2
BioTICLA Unit EA4656, Comprehensive Cancer Center François Baclesse – Université de Caen
Basse-Normandie : EA4656 – 14032 Caen, France
B-cell lymphoma 2 (Bcl-2) family proteins are crucial regulators of the intrinsic mitochondrial pathway of apoptosis and comprise both pro-apoptotic and anti-apoptotic proteins.(a) For
instance, anti-apoptotic proteins Bcl-xL and Mcl-1 induce cell survival until they are inhibited
by pro-apoptotic proteins (Puma, Noxa, Bim). In ovarian cancer, as in many others, these proteins expression is deregulated and play a causative role in chemoresistance. Bcl-xL and Mcl-1
anti-apoptotic proteins cooperate to protect tumor cells against apoptosis, and their concomitant
inhibition leads to massive apoptosis.(b) Whereas a potent Bcl-xL inhibitor have been discovered
(ABT-737 by Abbot Laboratories), Mcl-1 protein inhibition is still not completely resolved. As
interaction among Bcl-2 family proteins occurs through -helices, our laboratory has developed
a new family of compounds able to mimic -helix side chain distribution (abiotic foldamers) using as structural chemical units pyridine(c) and/or phenyl(d). The designed and synthesized
oligopyridines potentially targeting the Mcl-1 hydrophobic pocket were evaluated by their capacity to inhibit Mcl-1 in live cells and to sensitize ovarian carcinoma cells to Bcl-xL-targeting
strategies.(e) The structureactivity relationships were established and we focused our attention
on MR29072, named pyridoclax as a very promising compound inhibiting specifically Mcl-1. ,(f)
Its ability to bind directly to Mcl-1 was confirmed by surface plasmon resonance assay.
(a) Cory, S.; Adams, J. M. The Bcl2 family: regulators of the cellular life-or-death switch. Nat.
Rev. Cancer 2002, 2(9), 647-656.
(b) Brotin, E.; Meryet-Figuière, M.; Simonin, K.; Duval1, R. E.; Villedieu, M.; Leroy-Dudal1, J.;
Saison-Behmoaras, E.; Gauduchon, P.; Denoyelle, C.; Poulain, L. Bcl-xL and MCL-1 constitute
pertinent targets in ovarian carcinoma and their concomitant inhibition is sufficient to induce
apoptosis. Int. J. Cancer 2010, 126, 885-895.
(c) Sopková-de Oliveira Santos, J.; Voisin-Chiret, A.-S.; Burzicki, G.; Sebaoun, L.; Sebban,
M.; Lohier, J.-F.; Legay, R.; Oulyadi, H.; Bureau, R.; Rault, S. Structural Characterizations of
Oligopyridyl Foldamers, -Helix Mimetics. J. Chem. Inf. Model. 2012, 52, 429-439
(d) Perato, S.; Fogha, J.; Sebban, M.; Voisin-Chiret, A.-S.; Sopkova-de Oliveira Santos, J.;
Oulyadi, H.; Rault, S. Conformation Control of Abiotic -Helical Foldamers. J. Chem. Inf.
Model.2013, 53(10), 2671-2680.
∗
Intervenant
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COMMUNICATION ORALE
(e) Gloaguen, C. ; Voisin-Chiret, A. S. ; Sopkova-de Oliveira Santos, J. ; Fogha, J. ; Gautier, F. ; De Giorgi, M. ; Burzicki, G. ; Perato, S. ; Pétigny-Lechartier, C. ; Simonin-Le Jeune,
K. ; Brotin, E. ; Goux, D. ; N’Diaye, M. ; Lambert, B. ; Louis, M. H. ; Ligat,L. ; Lopez, F.
; Juin, P. ; Bureau, R. ; Rault, S. ; Poulain, P. J. Med. Chem., 2015, Just Accepted Manuscript.
(f) Poulain L., Voisin-Chiret A. S., Sopkova- de Oliveira Santos J., Bureau R., Burzicki G., De
Giorgi M., Perato S., Fogha J., Rault S., Juin Ph., Gautier F. Mcl-1 modulating compounds for
cancer treatment. EP14305309.8. 2014.
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Into the intimacy of an irreversible inhibition:
SMD(QM/MM) simulations of the fibroblast
growth factor receptor-1 (FGFR1) kinase
domain.
Yan Li , François Maurel , Patricia Busca , Guillaume Prestat , Laurence
Legeai-Mallet , Rhuizheng Zhang , Rongjing Hu , Michel Delamar , Florent
Barbault ∗ 1
1
ITODYS (ITODYS) – CNRS : UMR7086, Université Paris Diderot - Paris 7, Université Sorbonne
Paris Cité (USPC) – 15 rue Jean de Baïf 75205, France
The Fibroblast Growth Factor Receptor 1 (FGFR1) binds to Fibroblast Growth Factor (FGF)
peptide ligands [1]. This association induces signaling pathways which appears to play critical
roles in the cell development [1]. FGFR1, like the others FGFRs, has been studied in the context
of tumor formation and progression [2]. FGFR1 has been found being amplified in squamous cell
lung cancer specimens [3], especially of patients who smoke. Therefore, the inhibition of FGFR1
mainly aims to provide a therapeutic solution for smoker cancers.
Inhibition of the tyrosine kinase domain of FGFR1 is an appealing approach to obstruct the biological role of this receptor. However, inhibitors which compete with ATP generally suffer from
low selectivity. One way to overcome this drawback is to transform a reversible inhibitor to an
irreversible one by adding a reactive moiety. This ”haute couture” strategy requires a meticulous
design that cannot be performed by serendipity; Computational studies play thus here a pivotal
role.
The compound FIIN (fig. 1) is an irreversible inhibitor of FGFR1 [4] which makes a covalent bond
to the Cys486 of its kinase domain through a Michael addition’s. We explored its reactivity with
molecular simulations. Firstly, we performed structural analyses through molecular dynamics
simulations of FGFR1 covalently linked with FIIN (FGFR1-FIIN) and FGFR1 associated with
FIIN (FGFR1/FIIN). From this last trajectory we extracted 100 conformations where the sulfur
of Cys486 is close to the reactive alkene. To study the reactivity of the FIIN compound, we realized QM/MM simulations where the Self-Consistent-Charge Density-Functional Tight-Binding
method (SCC-DFTB) was chosen for the QM part [5]. We used the SMD technique [6] with
a LCOD reaction pathway for 1ns (fig. 1) and obtained three different mechanisms. Only one
mechanism is conceivable and works in two steps. The definitive free energy profile (fig. 1) explains the selectivity of FIIN and provides meaningful information for future drug-development
of irreversible kinase inhibitors.
Reference: [1] Koziczak M, et al. 2004 Oncogene 23, 3501–8 [2] Powers CJ, et al. 2000 Endocr. Relat. Cancer 7, 165–97 [3] Weiss J, et al. 2010 Sci. Transl. Med. 15:62ra93 [4] Zhou W,
∗
Intervenant
55
COMMUNICATION ORALE
et al. 2010 Chem. Biol. 26, 285-95 [5] Frauenheim T, et al. 1998 Mat. Res. Soc. Symp. Proc.
491, 91 [6] Isralewitz B, et al. 1997 Biophys. J. 73, 2972-9
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PatchSearch: a fast method for flexible
recognition of protein binding sites
Inès Rasolohery
1
∗† 1
, Gautier Moroy 1 , Frédéric Guyon
1
Molécules Thérapeutiques in silico (MTi) – Université Paris Diderot - Paris 7 – France
Inès Rasolohery, Gautier Moroy and Frédéric Guyon.
Specific recognition of a therapeutic molecule by its target protein has raised a lot of issues in
the pharmaceutical field. Indeed, non-specific interactions may lead to significant side effects. In
order to determine whether or not a ligand is able to interact specifically with its target protein,
we have developed a new method called PatchSearch. A patch is a set of atoms belonging to the
protein surface and it can also be a binding site. The aim of our program is to identify similar
patches in different protein surfaces.
PatchSearch is based on a classic strategy of clique detection in a correspondence or product
graph [1]. A clique of the product graph is a group of both geometrical and physicochemical
consistent links between binding site atoms and protein surface atoms, and consequently providing a possible structural alignment. PatchSearch uses this kind of clique approach adapting
it to protein structures. First, PatchSearch computes all maximum cliques of a reduced size
graph constructed with a small distance tolerance criterion (in practice, less than 1Å). Then,
the best clique is selected depending on its structural similarity score; this clique gives the rigid
core of the query patch. Finally, this clique is enriched with all compatible links from the clique
neighborhood. The resulting quasi-clique includes therefore both rigid and flexible parts of the
query binding site.
We first assessed PatchSearch ability to recognize protein binding sites specific to a same ligand.
We used a dataset proposed by Kahraman and coworkers [2], consisting of 100 protein structures complexed with one of nine ligand types (AMP, ATP, FAD, FMN, Glucose, Heme, NAD,
Phosphate or Steroid). We performed a ROC analysis and obtained an AUC value of 0.89 with
patches extracted from this dataset, suggesting that PatchSearch is efficient to identify specific
patches in proteins interacting with a same ligand. We also note that this AUC value is equivalent to or greater than the best results obtained by other programs [3] on the same dataset,
namely 0.77 to 0.89.
We have evaluated PatchSearch efficiency to find a patch from different conformations of a
same protein. For that purpose, we used a benchmark, set up by Gunasekaran and coworkers [4],
formed by 98 protein structures in both holo (in complex with ligand) and apo (unbound) forms.
When binding sites have undergone some large conformational changes, quasi-cliques approach
allows recognition of the whole patches, including flexible parts of the patches.
∗
†
Intervenant
57
COMMUNICATION ORALE
We have also applied PatchSearch on patches interacting with indometacin to mine for similar
patches into the complete set of human protein structures. This non-steroidal anti-inflammatory
drug commonly used to reduce fever and pain is well-known to lead to many side effects.
1. S.B. Seidman: Network structure and minimum degree. Social Networks 5,269-287 (1983).
2. A. Kahraman, R.J. Morris, R.A. Laskowski and J.M. Thornton: Shape Variation in Protein Binding Pockets and their Ligands. J. Mol. Biol. 368, 283-301 (2007).
3. B. Hoffmann, M. Zaslavskiy, J.P. Vert and V. Stoven: A new protein binding pocket similarity
measure based on comparison of clouds of atoms in 3D: application to ligand prediction. BMC
Bioinformatics. 11:99 (2010).
4. K. Gunasekaran and R. Nussinov: How Different are Structurally Flexible and Rigid Binding
Sites Sequence and Structural Features Discriminating Proteins that Do and Do not Undergo
Conformational Change upon Ligand Binding. J. Mol. Biol. 365, 257-273 (2007).
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COMMUNICATION ORALE
PockDrug-Server: a new web server for
predicting pocket druggability on holo and apo
proteins
Hiba Abi Hussein ∗† 1 , Alexandre Borrel , Colette Geneix , Michel Petitjean
, Leslie Regad , Anne-Claude Camproux‡
1
Recherche de molécules à visée thérapeutique par approches In Silico (MTI) – Inserm : U973,
Université Paris VII - Paris Diderot – Batiment Lamarck 35 rue Hélène Brion 75205 PARIS CEDEX 13,
France
Therapeutical molecules bind to preferred sites of action, which are in the majority of cases
pockets located within proteins or at their surface. Therefore, estimation and characterization
of pockets is a major issue in drug target discovery. Among the molecules, “drug-like molecules”
are small molecules with particular properties as of small size, able to cross the digestive tract.
Predicting protein pocket’s ability to bind drug-like molecules with high affinity, i.e., druggability,
is a key step of compound clinical progression projects. Currently computational druggability
prediction models are attached to one unique pocket estimation method despite pocket estimation
uncertainties. Here, we present “PockDrug-Server” that predicts pocket druggability, efficient on
both; estimated pockets guided by the ligand proximity (extracted by proximity to a ligand
from a holo protein structure using several thresholds) and estimated pockets not guided by the
ligand proximity (based on amino atoms that form the surface of potential binding cavities).
PockDrug-Server is based on a statistical model corresponding to a combination of 7 linear
discriminant analysis model using 9 pocket descriptors to provide a mean druggability. It provides
consistent druggability results using different pocket estimation methods. It is robust with
respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that
are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using
different estimation methods and outperformed recent druggability models for apo pockets. It
can be carried out from one or a set of apo/holo proteins. PockDrug-Server is publicly available
at: http://pockdrug.rpbs.univ-paris-diderot.fr.
∗
Intervenant
‡
†
59
60
CONFERENCE PLENIERE
Combining molecular dynamics and Markov
state modelling to understand ligand transport
in enzymes
Jochen Blumberger
∗† 1
, David De Sancho 2 , Po-Hung Wang 3 , Adam
Kubas 1 , Robert Best 4
1
University College London - London’s Global University – Gower Street - London, WC1E 6BT,
Royaume-Uni
2
University of Cambridge (UK) – The Old Schools, Trinity Lane, Cambridge CB2 1TN, Royaume-Uni
3
RIKEN – 2-1 Hirosawa, Wako, Saitama 351-0198, Japon
4
National Institutes of Health (NIH) – 9000 Rockville Pike Bethesda, Maryland 20892 USA, États-Unis
The diffusion of small ligands within enzymes and their binding to enzyme active sites are
ubiquitous molecular processes in biology. The oxygen we breath diffuses to the catalytic site
of cytochrome c oxidase where it combines with electrons to produce water and cellular energy.
Atmospheric CO2 and N2 diffuse to the buried catalytic sites of proteins before being converted
into precursors of biomass or renewable energy sources. In all these examples a small molecule
must travel from the solvent to the small catalytic site, that is embedded in a large and densely
packed heterogeneous protein matrix. Here we would like to understand how the ligand finds
its way to the target. To this end we have recently developed a multiscale molecular simulation
approach, where MD simulation and Markov state modelling are combined to shed light on
the kinetics of ligand diffusion and binding [1]. First applications to wild-type and mutant
hydrogenase enzymes[1,2] have given diffusion rates that are in good agreement with experimental
measurements [3] validating the Markov state model used. For the proteins studied so far we
have found that the ligands move within multiple hydrophobic cavities or ‘tunnels’ towards a
central cavity from where the ligand makes the final transit to the catalytic site [1,2,4]. The
functional role of the protein can be compared to the one of a funnel guiding the small molecules
towards the target. Very recently we have extended our method to calculate sensitivity maps
of the protein that identify residues (‘hot spots’) that are expected to show the greatest affect
on ligand diffusion when mutated [5]. Such in-silico predictions could help protein engineers to
optimize or control the kinetics of ligand, substrate or inhibitor diffusion in proteins. [1] P. Wang,
R. B. Best, J. Blumberger, J. Am. Chem. Soc. 133, 3548 (2011). [2] P. Wang, J. Blumberger,
Proc. Natl. Acad. Sci. USA , 109, 6399 (2012). [3] Leger, C. and coworkers Nat. Chem. Biol,
6, 63 (2010). [4] P. Wang, M Bruschi, L. De Gioia, J. Blumberger, J. Am. Chem. Soc. 135,
9493 (2013). [5] D. De Sancho, A. Kubas, P. Wang, J. Blumberger, R. B. Best, J. Chem. Theory
Comput. Article ASAP, DOI: 10.1021/ct5011455 (2015).
∗
†
Intervenant
61
COMMUNICATION ORALE
Structural dynamics of the retinoid X receptor
ligand binding domain by accelerated
molecular dynamics
Jérôme Eberhardt
∗ 1
, Roland Stote 1 , Annick Dejaegere
1
1
Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) – CNRS : UMR7104, Inserm :
U964, université de Strasbourg – Parc D’Innovation 1 Rue Laurent Fries - BP 10142 67404 ILLKIRCH
CEDEX, France
Nuclear Receptor proteins are ligand-dependent transcription activators that form one of
the major signal transduction pathways in metazoans, by contributing to cellular communication networks involved in apoptosis, cell growth and differentiation, homeostasis (Altucci et al.
2001). Retinoic acid 9-cis, a metabolite of vitamin A, is a ligand of the retinoid X receptor
(RXR) subclass of the nuclear receptor superfamily. This subclass is comprised of three RXR
(, and ). RXRs are multi-domains proteins with a central DNA-binding domain (DBD) linked
to C-terminal Ligand-binding domain (LBD). The protein is known to undergo large conformational changes in the course of activation.
RXR is also a phosphoprotein and the phosphorylation of Ser260 in the LBD affects the transcriptional regulation of RXR dimer complexes. Aberrant phosphorylation of Ser260 is strongly associated with the development of various cancers, in particular hepatocellular carcinoma (HCC), a
primitive liver cancer (Matsushima-Nishiwaki et al. 2001, Macoritto et al. 2008). Structurally,
Ser260 is located near the ligand-binding pocket, in a loop between Helix 1 and 3 of the LBD. In
earlier work, we showed that phosphorylation of the RAR LBD affects structural dynamics in a
way that can subsequently modulate functionality (Samarut et al. 2011, Chebaro et al. 2013).
The specific aim of this project is to understand how phosphorylation of the RXR LBD influences
protein structure and dynamics by molecular dynamics simulations. However, conformational
sampling by molecular dynamics simulations is, in itself, a challenging task and can often call for
the application of new methods of enhanced sampling. In this work, we explored the ensemble
of conformations of ligand bound and apo RXR by applying accelerated molecular dynamics
(aMD) (Hamelberg et al. 2004). aMD is a new computational technology that permits more
extensive conformational sampling. In this first application to nuclear receptor proteins, e.g.
RXR, extensive validation was required. We developed and applied an aMD protocol to the
unphosphorylated RXR receptor, taking advantage of the large amount of experimental information available for this receptor. Besides providing a means to validate our method, this study
provided additional interpretation of published experimental data. Our application of this technique yielded very promising results for enhanced conformational sampling of nuclear receptor
proteins. We will present results from this work as well as provide future perspectives, which
involve the study of phosphorylated forms of RXR as well as implications for the development
of new therapeutic compounds for the treatment of HCC.
∗
Intervenant
62
COMMUNICATION ORALE
Turning glycoside hydrolases into
transglycosidases: an experimental and
theoretical study of the internal water
dynamics in the Thermus thermophilus
-glycosidase
Benoît David
1
∗ 1
, Charles Tellier 1 , Yves-Henri Sanejouand
1
université de nantes (ufip) – CNRS : UMR6286 – 2 rue de la houssinière 44322 Nantes cedex 03, France
Mostly known for their ability to hydrolyze glycosidic linkages, numerous glycoside hydrolases are also able to catalyze reverse hydrolysis (transglycosylation) which can be harnessed for
the synthesis of complex oligosaccharides. Although hydrolysis usually prevails over the reverse
reaction, transglycosylation propensity has already been increased through mutagenesis and directed evolution experiments [1, 2]. However, little is known about the regulation of the balance
between both activities.
A previous experimental study on the Thermus thermophilus (Ttgly) -glycosidase using deuterium exchange mass spectrometry has shown that amide hydrogens from buried parts of the
protein were able to exchange with the solvent [3]. The discovery via molecular dynamic (MD)
simulation of a potential water channel connecting the bulk to the active site along this peptide
has supported a possible role of internal water dynamics on the hydrolytic acivity of Ttgly [3].
Using an improved simulation protocol, new MD simulations up to 500 ns have been conducted
in order to extensively probe the internal water dynamics in Ttgly. Analysis of the internal water
molecules trajectories allowed to characterized a total of six new potential water channels.
Guided by sequence alignements, mutagenesis experiments conducted on vicinal residues along
the largest channel allowed the identification of mutations influencing the balance between hydrolysis and transglycosylation. Within this context, it is tempting to speculate that this water
channel could be involved in supplying water molecules to the active site and potentially regulating the hydrolytic activity of Ttgly.
1. Feng, H.-Y., Drone, J., Hoffmann, L., Tran, V., Tellier, C., Rabiller, C., and Dion, M.
(2005). Converting a Glycosidase into a Transglycosidase by Directed Evolution. Journal of
Biological Chemistry 280, 37088–37097.
2. Teze, D., Hendrickx, J., Czjzek, M., Ropartz, D., Sanejouand, Y.-H., Tran, V., Tellier,
C., and Dion, M. (2014).Semi-rational approach for converting a GH1 glycosidase into a transglycosidase. Protein Engineering Design and Selection 27, 13–19.
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Intervenant
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COMMUNICATION ORALE
3. Teze, D., Hendrickx, J., Dion, M., Tellier, C., Woods, V.L., Tran, V., and Sanejouand,
Y.-H. (201 3 ).Conserved Water Molecules in Family 1 Glycosidases: A DXMS and Molecular
Dynamics Study. Biochemistry 52, 5900–5910.
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Description théorique d’un phénomène de
”substrate channeling” dans la biosynthèse des
flavonoïdes.
Julien Diharce
∗ 1
, Jérôme Golebiowski 1 , Sébastien Fiorucci 1 , Serge
Antonczak† 1
1
Institut de Chimie de Nice (ICN) – CNRS : UMR7272, Université Nice Sophia Antipolis [UNS],
Université Nice Sophia Antipolis (UNS) – Faculté des Sciences Parc Valrose 28 Avenue Valrose 06108
Nice cedex 2, France
Dans tous les systèmes vivants, la concentration d’entités biologiques fonctionnelles (protéines, enzymes, peptides...) est très élevée. Dès lors, des structures multienzymatiques transitoires peuvent se créer et exister suffisamment longtemps pour permettre l’apparition de nouveaux systèmes fonctionnels appelés métabolons. Dans de telles structures impliquant un ensemble d’enzymes engagées séquentiellement dans un chemin de biosynthèse, le produit d’une
réaction enzymatique est transféré directement dans le site actif de l’enzyme suivante où il devient
substrat. Ce transfert direct diminue grandement les temps de diffusion lors du processus global
ainsi que les transferts énergétiques impliqués lors des phénomènes de désolvatation/solvatation.
L’effet catalytique général est ainsi optimisé. Ce phénomène est désigné sous le nom de “ Substrate Channeling ”.
Il est désormais admis que la création de tels complexes est un élément essentiel des mécanismes
de biosynthèse de molécules naturelles[1]. De récents travaux[2] mettent en avant la formation
d’un tel complexe supramoléculaire lors de la production de flavonoïdes, molécules naturelles
antioxydantes dont font partie les anthocyanes et les proanthocyanidines. Nous avons mis en
place de nombreuses méthodes théoriques pour décrire les interactions à différents niveaux de
complexité. Des méthodes de docking protéine-protéine en résolution gros grain ont permis de
proposer des structures multi-enzymatiques alors que des procédures de dynamique moléculaire
en résolution tout-atome ont mené à une description fine des interactions entre substrat, cofacteur et enzyme.
Lors de ce travail, a été décrit le comportement dynamique de structures multienzymatiques
impliquant trois enzymes en interaction avec une membrane cellulaire. Nous détaillerons plus
spécifiquement le phénomène de diffusion du métabolite d’un site actif au suivant et mettrons en
avant l’importance de la nature des interactions protéine/protéine sur la stabilité du complexe.
L’analyse de la diffusion du métabolite montre clairement qu’il reste constamment en interaction avec les structures enzymatiques et que, dans cette configuration, le nombre de molécules
d’eau dans son environnement est fortement réduit comparativement à une solvatation complète.
1. Møller, B.L., Dynamic Metabolons. Science, 2010. 330(6009): p. 1328-1329.
2. Crosby, K.C., et al., FEBS Letters, 2011. 585(14): p. 2193-2198.
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L’allostérie dynamique de la protéine CAP
révélée par les forces inter-atomiques
Maxime Louet
1
∗ 1
Molécules Thérapeutiques In silico (MTi) – Université Paris Diderot - Inserm UMR-S 973 – Bât
Lamarck A, 35 Rue Hélène Brion 75205 PARIS CEDEX 13, France
La protéine Activatrice des Catabolites (Catabolite Activator Protein – CAP) est un cas
d’école pour l’allostérie des protéines. L’activation et la liaison à l’ADN de cet homodimère
requiert la liaison de deux Adénosine Mono-Phosphate cycliques (AMPc) qui se lient de manière
anti-cooperative. Ce facteur de transcription qui joue un grand rôle dans la régulation de nombreux gènes au sein des procaryotes, tel que l’opéron lactose, a fait l’objet de plusieurs études
expérimentales ayant montré une perte notable de son entropie conformationnelle après la liaison
du second ligand. Il a donc été proposé que la cause de cette anti-cooperativité est essentiellement entropique et non due à un changement conformationnel. Cependant, les acides-aminés
clés initiateurs de la modification de la dynamique globale de CAP sont encore inconnus. Nous
présenterons nos résultats, à l’échelle atomique, concernant la régulation allostérique de la protéine CAP, par des méthodes de Dynamique Moléculaire et d’Analyse de Distribution de Force
(Force Distribution Analysis – FDA) [1]. Il sera montré que nos simulations reproduisent la perte
entropique observée expérimentalement. Cette entropie a été calculée de manière plus précise
grâce à un outil développé au laboratoire se basant sur la covariance des forces et non des coordonnées [2]. A l’aide de la FDA nous avons également pu observer un chemin de communication
allostérique au sein de la protéine CAP, d’une part entre les deux sites de fixation des AMPc et
d’autre part avec des sites plus distants tels que les domaines de liaison à l’ADN. Nos résultats
suggèrent que l’anti-coopérativité n’est pas uniquement entropique mais également enthalpique,
notamment par la rotation de chaînes latérales bloquant le site de fixation du deuxième AMPc.
1. Costescu and Gräter. Time-resolved force distribution analysis. BMC Biophysics 2013, 6:5
2. Hensen, Gräter and Henchman. Macromolecular Entropy Can Be Accurately Computed from
Force. J.Chem.Theory Comput. 2014, 10: 4777-4781
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Assessing the crowding of Membrane Proteins
at the Mesoscale
Matthieu Chavent
1
∗ 1
, Anna Duncan 1 , Jean Hélie 1 , Tyler Reddy 1 , Mark
Sansom 1
Department of Biochemistry (Oxford, UK) (SBCB) – South Parks Road, Oxford, OX1 3QU,
Royaume-Uni
With recent improvements in both experimental and computational techniques it is now
possible to gain insight into increasingly complex biomembrane systems. Nevertheless, at the
frontier between these two fields lies a twilight zone which we can refer to as the mesoscale. This
zone ranges from a few dozen of nanometres to micrometres in size and from microseconds to
millisecond in time. In this zone, proteins may adopt specific spatial organisations within a cell
membrane impacting on a number of biological processes. For example, with our collaborators,
we have combined molecular dynamics and microscopy to reveal the presence of “islands” of ~ 0.5
µm dimensions formed by the aggregation of porins in bacterial outer membranes. This phenomenon has an essential role in the turnover of proteins at the surface of gram negative bacteria
[1]. Nevertheless, despite increasing efforts to cover this mesocale zone, it remains difficult to
compare models and experimental observations. Furthermore, the complex dynamic interactions
between lipids and proteins need to be better understood on a larger scale to take into account
possible emergent phenomena which are not visible for smaller (nanoscale) system sizes. This is
particularly true now with the rise of very large and complex models such as plasma membranes
[2,3] or flu virion [4]. Here, we would like to present our different attempts to model and characterize large systems to assess the dynamical crowding of membrane proteins and how it has
driven the development of new visualization programs to improved the understanding of these
dynamical systems [5]. We will also discuss how a better characterization of these systems may
be used to pave the way of simpler mathematical models to create systems of the micrometre
scale.
1. Rassam, P. et al. Supramolecular assemblies underpin turnover of outer membrane proteins
in bacteria. Nature, accepted.
2. Ingólfsson, H. I. et al. Lipid organization of the plasma membrane. J. Am. Chem. Soc.
136, 14554–14559 (2014).
3. Koldsø, H., Shorthouse, D., Hélie, J. & Sansom, M. S. P. Lipid Clustering Correlates with
Membrane Curvature as Revealed by Molecular Simulations of Complex Lipid Bilayers. PLoS
Comput Biol 10, e1003911 (2014).
4. Reddy, T. et al. Nothing to Sneeze At: A Dynamic and Integrative Computational Model of an
Influenza A Virion. Structure (London, England : 1993) (2015). doi:10.1016/j.str.2014.12.019
5. Chavent, M. et al. Methodologies for the analysis of instantaneous lipid diffusion in md
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simulations of large membrane systems. Faraday discussions 169, 455–475 (2014).
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New way to fight against antibiotic resistance:
reconstruction of a three-component efflux
pump
Kaouther Ben Ouirane
∗† 1
, Isabelle Broutin 2 , Catherine Etchebest
1
1
Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB) – Université
Paris Diderot, Sorbonne Paris Cité, INTS, Inserm : U1134, Université Paris Diderot - Paris 7,
Laboratoire d’excellence GrEx – 6 rue Alexandre Cabanel, 75739 Paris cedex 15, France
2
Laboratoire de cristallographie et RMN biologiques (LCRB) – CNRS : UMR8015, Université Paris V Paris Descartes – Faculté de Pharmacie 4 Avenue de l’Observatoire 75270 PARIS CEDEX 06, France
Efflux pumps are present in Gram-negative bacteria and are a key mechanism to resist to
antibiotics through expulsing them to the extracellular medium preventing them to reach their
target. Thus, inhibition of this machinery can be an efficient strategy to fight against antibiotic resistance. Efflux pumps are active as macrocomplexes of three protein partners: MexB, a
protein of the inner membrane, OprM, protein of the outer membrane and MexA, a periplasmic protein. Although each component has been well characterized, complex assembly remains
misunderstood. The elucidation of their assembly mechanism, in particular in Pseudomonas
aeruginosa, would identify key regions essential for complex function.
We propose a new and original strategy to prevent the assembly of the efflux pump: This strategy consists in designing new drugs (”complex blockers”) avoiding the protein/protein recognition
and/or rigidifying the different protein partners preventing their required structural adaptability
for assembly.
Using normal mode analysis and molecular dynamics simulations (coarse grain and all atoms) we
characterized the conformational changes as well as the flexibility and dynamics of all partners:
MexB and OprM in lipid bilayer environment. The reconstitution of functional complex is an
intricate task, due to the particularity of its organization. In fact, efflux pump components adopt
asymmetric conformations and function according to complex rotational mechanism. Another
particularity is the uncertainty related to the periplasmic protein (MexA) stoechiometry. Due
to the highly flexible nature of those proteins, especially mexA, we showed that rigid docking
strategy is inadequate for this system. We developed then a new strategy to reconstitute the
pump that consisted in using CG simulations to study protein-protein interactions instead of
using rigid docking.
We found that mexA auto-assembles with another molecule forming a stable dimmer for may
hundreds of nanoseconds. Interestingly, our collaborators demonstrated that mexA associates
into dimers and that it was the functional unit prerequisite for pump assembly. So now we are
using the same original strategy based on successive molecular dynamic simulations for solving
the problem of assembly between the other efflux pump partners.
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COMMUNICATION ORALE
Étude de l’assemblage des protéines
membranaires par simulation de dynamique
moléculaire à gros grains et méthodes de
Forces de Biais Adaptatif.
Jean-Pierre Duneau
1
∗ 1
, Marlon Sidore 1 , James Sturgis
1
Laboratoire d’ingénierie des systèmes macromoléculaires (LISM) – CNRS : UMR7255, Aix-Marseille
Université - AMU – 31 Chemin Joseph Aiguier 13402 MARSEILLE CEDEX 20, France
L’assemblage des protéines membranaires est le produit d’interactions complexes impliquant
des contacts hiérarchisés au sein de multiples chaînes polypeptidiques et d’une grande diversité
de lipides. Alors que les méthodes de biologie structurale donnent une vue plus précise des interactions mises en jeu au niveau purement protéique, encore très peu de données sont disponibles
pour prendre la mesure du rôle de l’environnement lipidique.
Dans le travail présenté, nous avons mis en œuvre des approches de modélisation de dynamique
moléculaire à gros grains pour étudier la gamme et la portée des perturbations structurales et
dynamiques induites par la présence de protéines sur différents lipides. Nos résultats montrent,
la structuration d’un annulus lipidique dynamique au sein duquel les plus grands excès de densité
correspondent à des zones d’ancrage privilégiées mais largement échangeables. Au delà de cette
zone, une relaxation plus complexe qu’une simple relaxation élastique se met en œuvre jusqu’à
de longues distances (> 40 Å).
Aussi pour rendre compte de l’impact énergétique de ces altérations à longues portées sur
l’association des protéines, nous avons développé une méthode de simulation utilisant des Forces
de Biais Adaptatif (ABF) suivant des coordonnées qui permettent d’explorer l’espace conformationnel d’association de protéines suivant les orientations relatives de monomères. Nos simulations montrent que même à longues distances (80 Å) des biais d’orientation relative existent. La
relation avec les perturbations de densité lipidique doit encore être établie.
Ces travaux ont bénéficié d’un accès aux moyens de calcul du CINES au travers de l’allocation
de ressources 2014-077044 attribuée par GENCI.
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Tinker-HP : Dynamique moléculaire
polarisable haute performance
Christophe Narth
1
∗ 1
, Jean-Philip Piquemal
1
Laboratoire de chimie théorique (LCT) – CNRS : UMR7616, Université Pierre et Marie Curie
(UPMC) - Paris VI – 4 place Jussieu, CC-137, F-75252 Paris, France
La dynamique moléculaire est aujourd’hui un outil puissant au service de la biochimie et
pharmacologie, permettant de réaliser bon nombres d’études telle que des analyses conformationnelles, effet de solvatation, énergie libre ou encore simulation d’évènements rares.
Cela a été rendu possible par l’accélération des codes de mécanique moléculaire classique, permettant ainsi d’atteindre des échelles de temps biologiques.
D’autre part, afin de reproduire de manière plus réaliste les interactions intermoléculaires, des
méthodes plus raffinées, dites polarisables ou encore de seconde génération, ont été développées,
telles que AMOEBA[1] ou SIBFA[2]. Basées sur les multipoles, elles permettent de prendre en
compte l’anisotropie des interactions électrostatiques, critique dans le cas de liaisons halogène
(“sigma-hole“) ou d’empilement de benzènes (“stacking“). A cela s’ajoute la décomposition des
termes d’énergies par la méthode SIBFA, reproduisant ainsi les énergies références (en chimie
quantique) dû aux interactions non-liées.
Jugés trop souvent comme plus coûteux, ces programmes ont pris du retard par rapport à la
large utilisation des champs de forces classiques.
Nous présentons ici une nouvelle implémentation du potentiel SIBFA au sein de Tinker-HP
(open-source) mêlant rigueur méthodologique par la non-additivité de ses termes énergétiques
couplées à une parallèlisation hybride offrant alors la possibilité de réaliser les premières dynamiques moléculaires SIBFA, ainsi que ses premières applications comme un nouveau modèle
d’eau.
Ponder, Jay W., et al. ”Current status of the AMOEBA polarizable force field.” The journal of
physical chemistry B 114.8 (2010): 2549-2564
Cisneros, G. A., et al. ”Design Of Next Generation Force Fields From AB Initio Computations:
Beyond Point Charges Electrostatics.” Multi-scale Quantum Models for Biocatalysis. Springer
Netherlands, 2009. 137-172.
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CONFERENCE PLENIERE
Illustrative Molecular Visualizations
Tobias Isenberg
1
∗ 1
AVIZ (INRIA Saclay - Ile de France) – INRIA – DIGITEO, Bat. Claude Shannon - Université de
Paris-Sud, Bâtiment 660, 91190 Gif-sur-Yvette, France
Illustrative visualization is a relatively recent development within the larger research field
of visualization. Illustrative visualization takes inspiration, in particular, from methods that
have been used in traditional illustration for decades and centuries. Illustrative visualization
techniques thus make use of concepts such as abstraction and emphasis as well as depiction style
to facilitate the creation of better visualization. In particular in the context of the visualization
of molecular data, several illustrative visualization techniques have been created. I will present
a selection of these, both those created by my own research group and those of colleagues. For
example, I will discuss an approach for the abstraction of visualizations of molecular structures
in which a continuous abstraction space between structural abstraction, abstraction through
spatial perception, and abstraction by means of ”illustrativeness” is created. Users can navigate
this space to adjust visualization to their specific needs. Other techniques, for example, use
different molecular surface representations at different scale levels based on current view of the
data and support a seamless transition between them. With the talk I thus hope to introduce the
audience to new visualization approaches and hope to begin a discussion on potential application
fields of this work for specific scientific tasks and questions.
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Modélisation de novo de structures protéiques
par contacts évolutifs
Fabrice Allain
∗† 1
, Benjamin Bardiaux 1 , Mickael Nilges
1
1
Institut Pasteur - CNRS UMR 3528 – Institut Pasteur de Paris, Centre National de la Recherche
Scientifique - CNRS – Département de Biologie Structurale et Chimie 25,28 rue du Docteur Roux,
75015 PARIS, France
Malgré les progrès constants des techniques de biologie structurale, la différence entre le nombre de structures et de séquences protéiques disponibles ne cesse de croître depuis l’arrivée des
technologies de séquençage à haut débit. Les méthodes de prédiction structurale ont prouvées
ces dernières années leur efficacité pour combler ce fossé(1,2). La croissance des informations
génomiques a également rouvert la voie des techniques modélisant les protéines à partir des
variations des séquences homologues. La conservation d’une fonction au cours de l’évolution
contraint en effet les résidus en contact dans la structure à suivre la même trajectoire évolutive.
Le but de ces méthodes est donc de détecter des paires de résidus covariants dans les alignements
des séquences homologues d’une protéine cible et déterminer à terme l’architecture tridimensionnelle via des méthodes de modélisation. De récentes approches(3,4) ont ainsi démontrées qu’il
était possible de prédire les contacts natifs mais aussi de déduire des conformations relativement
proche de celle de référence à partir de ces informations.
Malheureusement, ces méthodes présentent encore plusieurs limitations dont la détection des
contacts faux positifs. Ces difficultés présentent des similitudes avec la résolution de structure
en RMN (Résonnance Magnétique Nucléaire). La spectroscopie par RMN permet de détecter des
couples de noyaux proches dans l’espace pour déterminer les conformations. Le logiciel ARIA
(Ambiguous Restraints for Iterative Assignment)(5,6). intègre la détection des faux positifs utilisant en particulier le concept de contraintes de distances ambiguës et un processus itératif. Dans
ce contexte, nous utilisons la méthodologie d’ARIA à partir des contacts évolutifs pour prédire
des structures de protéine.
Les premiers tests réalisés sur un jeu de données type avec le protocole d’ARIA montrent des
résultats encourageant. Cependant, l’apparition de structures en miroirs topologiques nécessitera
une augmentation du nombre de contacts évolutifs utilisés pour obtenir des structures plus juste.
1. Kryshtafovych, A., Fidelis, K. and Moult, J. (2014) Proteins, 82: 164–174. doi: 10.1002/prot.24448
2.Tai, C.-H., Bai, H., Taylor, T. J. and Lee, B. (2014) Proteins, 82: 57–83. doi: 10.1002/prot.24470
3. Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander,
C. (2011) PloS one, 6(12), e28766.
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4. Michel, M., Hayat, S., Skwark, M. J., Sander, C., Marks, D. S., & Elofsson, A. (2014)
Bioinformatics, 30 (17), i482-i488.
5. Rieping, W., Habeck, M., Bardiaux, B., Bernard, A., Malliavin, T. E., & Nilges, M. (2007)
Bioinformatics, 23 (3), 381-382.
6. Bardiaux, B., Malliavin, T., & Nilges, M. (2012). In Protein NMR Techniques (pp. 453-483)
Humana Press.
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Integrating the Solvent Accessible Surface
Distance with cross-links-based modeling
methods improves the conformational
sampling of protein assemblies
Mathias Ferber
1
∗
, Guillaume Bouvier , Michael Nilges
1
Unite de Bioinformatique Structurale , CNRS UMR 3825 and Institut Pasteur (IP, CNRS UMR 3825)
– Institut Pasteur de Paris – 28 rue du docteur Roux 75724 Paris, France
Cross-linking mass spectrometry (XL-MS) is increasingly used for structural modeling of
multi-subunit protein complexes. Here we use the log-harmonic potential as a formulation of
spatial restraints that realistically reflects the experimentally observed cross-linking distances in
solution. In combination with a Bayesian modeling framework that iteratively adjusts weights
during optimization, conflicting restraints and conformational heterogeneity are appropriately
dealt with. In addition, we developed a new geometric approach to quickly estimate the Solvent
Accessible Surface Distance (SASD) between two residues in the context of protein assemblies.
SASD allows to mimic the behavior of cross-linkers by bridging two atoms without penetrating
the protein. We demonstrated on previously published systems that our computation method
efficiently eliminates false positives while remaining compatible with experimental cross-linking
data. At each step of the conformational sampling, SASD is computed to estimate data violation
and enhance the Bayesian restraints re-weighting scheme. We obtain faster convergence towards
meaningful conformations.
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COMMUNICATION ORALE
A Detailed Data-Driven Protein-Protein
Interaction Potential Accelerated By Polar
Fourier Correlation
Emilie Neveu
∗† 1
, Sergei Grudinin 1 , Dave Ritchie 2 , Petr Popov
1
1
NANO-D (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann) – Institut
polytechnique de Grenoble (Grenoble INP), Université Joseph Fourier - Grenoble I, CNRS : UMR5224,
Laboratoire Jean Kuntzmann, INRIA – Inria GIANT DRT/LETI/DACLE Batiment 51C - Minatec
Campus 17 rue des Martyrs 38054 Grenoble Cedex, France
2
CAPSID (INRIA Nancy - Grand Est / LORIA) – INRIA, CNRS : UMR7503, Université de Lorraine
– France
In structural biology, medicine, and drug design, to find proteins that can act as inhibitors
for viruses or bacteria, one has to know the structure of the complex to be formed. The Protein Data Bank [1] can help but only a small fraction of its proteins are complexes. Without
good prior knowledge of the binding sites, computational prediction methods become necessary.
Generally, existing methods first explore the six degrees of freedom search space of rigid body
motions to locate position of one protein with respect to another one, typically using a very
simple energy function. Then, they locally enhance the search space (adding flexibility) while
using a more precise energy function. However, important solutions may be missed right at the
first step. Our research focuses on improving the first stage predictions. We believe it will also
improve the subsequent refinement searches.
We present an algorithm that uses a more precise energy while exploring the whole search space
of rigid-body motions. It combines a very fast FFT-accelerated exhaustive search with a detailed
data-driven approximation to the binding free energy. More precisely, we are using protein geometry represented through 3-D Gauss-Laguerre expansion coefficients, the Spherical Polar Fourier
transform to rapidly compute energy overlap integrals [2], and a convex optimization technique
to learn the interaction potential. We describe the chemistry and the geometry of a protein
with 20 3D grids for each protein in a complex and 210 pairwise distance-dependent interaction
potentials that have been learned from the structures of known protein-protein interfaces using
a convex optimization technique. The method runs in 5-10 minutes on a modern laptop for a
mid-size protein complex.
When tested on a set of 195 bound non-homologues protein hetero-dimer complexes, the method
achieves correct rank-1 solutions in over 50% of cases, and it produces a correct solution within
the top 10 solutions in 66% of cases. Here, a “correct solution” means one with a ligand rootmean-squared deviation (RMSD) of less than 5 Å from the native solution. We are currently
adapting the method to predict the structures of protein complexes starting from their unbound
structures.
1. H.M. Berman et al. The protein data bank. Acta Cryst. (2002), D58, 899-907.
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2. D.W. Ritchie, D. Kozakov, and S. Vajda, Accelerating and Focusing Protein-Protein Docking Correlations Using Multi-Dimensional Rotational FFT Generating Functions, Bioinformatics
(2008), 24 (17): 1865-1873.
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Fragment-based modeling of ssRNA-protein
complexes
Isaure Chauvot De Beauchene
1
∗† 1
, Sjoerd De Vries 1 , Martin Zacharias‡
1
Technische Universität München (TUM) – James-Franck-Strasse 1, 85748 Garching, Germany,
Allemagne
Rationale. Protein-RNA recognition supports a large variety of fundamental cellular functions. It is also a crucial therapeutic target : Abnormal or impaired protein-RNA interactions
are responsible for neurodegenerative diseases [1] and cancers [2] ; many viral infections use the
recognition of the viral RNA by proteins of the host [3]. Moreover, RNA’s non-toxicity and the
high specificity of protein-RNA interactions confer an immense potential to the synthesis of RNA
aptamers as modulators of proteins [4]. An atomistic prediction and description of protein-RNA
complexes is essential to the rational conception of modulators of protein-RNA binding and their
function [5]. However the experimental resolution of structures of these complexes is extremely
difficult . Many interfaces are still uncharacterized at atomic scale, and require computational
modeling.
State of the art. However, protein-RNA docking is hampered by the high flexibility of RNA.
Therefore, compared to protein-protein and protein-ligand docking, protein-RNA docking methods are lagging behind. Particularly challenging are single-stranded RNAs (ssRNAs) and singlestranded loops in RNAs, which encounter nonlinear conformational rearrangement over binding,
such as base flipping. Yet these parts carry the specificity of recognition in most cases. The lack
of methodology for modeling ssRNAs limits the accuracy of all current protein-RNA docking
methods [6] [7].
Method. We present an original and effective fragment-based approach to tackle this problem, capable of accurate prediction of the structure of a ssRNA-protein complex, starting from
the structure of the protein and the sequence of the RNA. The protocol is as follows: (i) The
RNA sequence is cut in overlapping trinucleotides ; (ii) each trinucleotide is represented by a
sequence-specific ensemble of conformers in an exhaustive fragment library built from all experimental protein-RNA complex structures; (iii) each conformer ensemble for the RNA sequence
is docked on the protein; (iv) the compatible poses are selected and assembled in a realistic
conformation, on a RMSD criteria of the overlapping nucleotides .
Results. We tested our method on four sequences of ssRNA (6 to 11 nucleotides) bound to
proteins containing two RNA-recognition motifs (RRMs). Without any information on specific
contacts nor on the RNA structure, our method can accurately identify the RNA binding site
on the protein within 10 Å precision. In addition, we tested two different regimes with limited
information. First, by selecting the best conformers of our library for each fragment prior to
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docking, we could approximate the bound conformation of the RNA with ~ 1.5 Å RMSD on
heavy atoms. Second, by assuming that at least one nucleotide interacts with each RRM in a
canonical manner [8], without any knowledge on the RNA structure, the bound conformation
was approximated with a precision of 2 to 3 Å RMSD. For both regimes, alternative incorrect
structures were also generated, and future research will focus on identifying the correct structure (scoring). Still, this precision has never been reached so far by any protein-RNA docking
method. If our method proves generalizable further, this will constitute a major methodological
breakthrough in RNA-protein docking.
1. T. Vanderweyde, K. Youmans, L. Liu-Yesucevitz, and B. Wolozin, Gerontology, vol. 59,
no. 6, pp. 524–533, 2013.
2. M. Derrigo, A. Cestelli, G. Savettieri, and I. Di Liegro, Int. J. Mol. Med., vol. 5, no.
2, pp. 111–134, Feb. 2000.
3. A. Ranji and K. Boris-Lawrie, RNA Biol., vol. 7, no. 6, pp. 775–787, Dec. 2010.
4. D. Peer and J. Lieberman, Gene Ther., vol. 18, no. 12, pp. 1127–1133, Dec. 2011.
5. S. Kandil, S. Biondaro, D. Vlachakis, A.-C. Cummins, A. Coluccia, C. Berry, P. Leyssen,
J. Neyts, and A. Brancale, Bioorg. Med. Chem. Lett., vol. 19, no. 11, pp. 2935–2937, Jun.
2009.
6. S. Fulle and H. Gohlke, J. Mol. Recognit. JMR, vol. 23, no. 2, pp. 220–231, Apr. 2010.
7. N. Morozova, J. Allers, J. Myers, and Y. Shamoo, Bioinforma. Oxf. Engl., vol. 22, no.
22, pp. 2746–2752, Nov. 2006.
8. C. Maris, C. Dominguez, and F. H.-T. Allain, FEBS J., vol. 272, no. 9, pp. 2118–2131, May
2005.
80
COMMUNICATION ORALE
ORION : improving remote homology
detection using a structural alphabet
Yassine Ghouzam 1 , Guillaume Postic , Alexandre De Brevern 1 ,
Jean-Christophe Gelly ∗† 1
1
Biologie Intégrée du Globule Rouge - Equipe Dynamique des Systèmes et des Interactions des
Macormolécules Biologiques (DSIMB) – Université Paris VII - Paris Diderot, Institut National de la
Transfusion Sanguine (INTS), Inserm : U1134, Laboratoire d’excellence GrEx – 6 rue Alexandre
Cabanel 75739 Paris cedex 15, France
Protein structure prediction is crucial to apprehend protein functions. In this important
field, the most successful strategies are comparative modeling methods. Given a protein target
sequence with unknown structure, comparative modeling consists to build its structure from an
evolutionary related protein with a known structure used as a template. Comparative modeling
have mainly two steps (i) identify a suitable protein template candidate and (ii) building target
structure from the template.
The first stage is the main limiting step. Indeed many potential interesting templates are still
unrecognizable using classical sequence alignment methods due to far evolutionary relationship
between template and target proteins. Peculiar approaches are then specifically then developed
for identify such far relationship. Such approaches are based on profile-profile sequence alignment, obtained from homologs sequences on both target and template. Nonetheless even with
such strategies many potential interesting templates remain still undetected. Besides, structural features (e.g secondary structures) are three to ten times more conserved than sequence
(Illergård et al. 2009). Therefore integration of structural data, mainly as secondary structure,
can effectively boost the finding of distant relationship between proteins (Koretke et al. 1999).
However, secondary structure states characterize local conformation only in three main states
(helix, strand and coil) and are limited for describing the loops, which constitutes 45% of the
local structure of the proteins.
We have developed ORION, a new generation of profile-profile method combining both aminoacids profile and a new local conformation structural information. The key element of our method
relies on Protein Blocks (PBs), a structural alphabet of 16 states of five residues long, able to
finely describe local conformations and depict protein structure in term of a sequence constituted
of 16 letters alphabet (de Brevern et al. 2000). Contrary to secondary structure, limited to only
3 states, it can finely approximate local conformation and can catch all transitions in protein
structures. PBs are already been used successfully in many applications such as structural alignment and local structure prediction (for review: Joseph et al. 2010).
The major steps of ORION are as follows: Starting from a query sequence, a sequence profile is computed using PSI-BLAST. This profile is also used for PB prediction with LOCUSTRA
∗
†
Intervenant
81
COMMUNICATION ORALE
software (Zimmermann and Hansmann, 2008). Using dynamic programming, sequence profile
and predicted structure of the query are then combined to search in a fold library of pre-computed
sequence profiles and assigned PB built from structural alignments of families in HOMSTRAD
databank (Mizuguchi et al, 1998).
We showed that including PBs information increase both specificity and sensitivity in searching
a remote homology template on a large benchmark based on HOMSTRAD databases (Figure
1). Moreover ORION systematically outperforms PSI-BLAST (Altschul SF et al, 1997) and
HHsearch (Söding, 2005) one of the top leading method and winner of ninth Critical Assessment
of Structure Prediction (CASP) in template-based modeling category, and detects around 10%
more templates at fold and superfamily level.
We also confronted our method to HHsearch and PSI-BLAST on previous CASP competitions
(CASP 8, 9 and 10) experiments and demonstrated that at 12 % of False Positive Rate (FPR)
ORION achieved a True Positive Rate (TPR) of 45 %, 2 times more than HHsearch and retrieved
8.6% more homologs than HHsearch in the top 10.
Finally, we also successfully assessed a pre-version of ORION in CASP 11 experiment, the last
edition of the international competition dedicated to assess the state of the art method in protein
prediction in which we participated in 2014.
References:
Illergård K, Ardell DH, Elofsson A. Structure is three to ten times more conserved than sequence–
a study of structural response in protein cores. Proteins. 2009 Nov 15;77(3):499-508
Koretke KK, Russell RB, Copley RR, Lupas AN. Fold recognition using sequence and secondary
structure information. Proteins. 1999;Suppl 3:141-8.§ de Brevern AG, Etchebest C, Hazout
S. Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.
Proteins. 2000 Nov 15;41(3):271-87.
Joseph AP, Agarwal G, Mahajan S, Gelly JC, Swapna LS, Offmann B, Cadet F, Bornot A,
Tyagi M, Valadié H, Schneider B, Etchebest C, Srinivasan N, De Brevern AG. A short survey
on protein blocks. Biophys Rev. 2010 Aug;2(3):137-147.
Zimmermann O, Hansmann UH. LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach. J Chem Inf Model. 2008 Sep;48(9):1903-8.
Mizuguchi K, Deane CM, Blundell TL, Overington JP. HOMSTRAD: a database of protein
structure alignments for homologous families. Protein Sci. 1998 Nov;7(11):2469-71.
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST
and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997
Sep 1;25(17):3389-402.
Söding J. Protein homology detection by HMM-HMM comparison. Bioinformatics. 2005 Apr
1;21(7):951-60.
82
COMMUNICATION ORALE
SAMSON: Software for Adaptive Modeling
and Simulation Of Nanosystems
Nadhir Abdellatif , Svetlana Artemova , Marc Aubert , Jocelyn Gate ,
Sergei Grudinin , Leonard Jaillet , Khoa Nguyen , Mohamed Yengui ,
Stephane Redon ∗ 1
1
INRIA Rhône-Alpes (INRIA Grenoble Rhône-Alpes) – INRIA – ZIRST 655 Avenue de l’Europe
Montbonnot 38334 Saint Ismier cedex, France
SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems
Nadhir Abdellatif, Svetlana Artemova, Marc Aubert, Jocelyn Gate, Sergei Grudinin, Leonard
Jaillet, Khoa Nguyen, Mohamed Yengui, and Stephane Redon*
NANO-D, INRIA Grenoble - Rhone-Alpes, 38334 Saint Ismier Cedex, Montbonnot, France; Laboratoire Jean Kuntzmann, B.P. 53 38041 Grenoble, Cedex 9, France.
SAMSON (Software for Adaptive Modeling and Simulation Of Nanosystems), the software platform for computational nanoscience that we have been developing in the NANO-D group, is now
available on SAMSON Connect at http://www.samson-connect.net.
SAMSON integrates modeling and simulation to aid in the analysis and design of molecular
systems. For instance, an interactive quantum chemistry module (ASED-MO level of theory)
makes it possible for users to build and edit structures while interactively visualizing how the
electronic density is updated (Figure – a); interactive flexing and twisting tools allow users to
easily perform large-scale flexible deformations of e.g. proteins with a few mouse clicks (Figure
– b); interactive virtual prototyping of hydrocarbon systems may be used to edit and constrain
graphene sheets, nanotubes (Figure – c), etc.
This integration is made possible via SAMSON’s data graph, which contains all information
on models and simulators. Nanosystems are described through five types of models: structural
models (geometry and topology), dynamical models (degrees of freedom), interaction models (potential energy, forces, electronic structure), visual models (graphical representations, e.g. Figure
– d) and property models. SAMSON’s data graph relies on a signalling system that may be
used to develop adaptive algorithms. For instance, an adaptively restrained state updater may
control, at each time step of a simulation, which degrees of freedom should be updated in a
dynamical model [1]. In turn, an interaction model may request from a dynamical model the list
of positions that have been updated since the last simulation step, to incrementally update the
potential energy, forces and electronic structure [2, 3, 4].
Most important, SAMSON has an open architecture: a Software Development Kit allows developers to extend SAMSON’s functionality by developing SAMSON Elements, i.e. new modules
∗
Intervenant
83
COMMUNICATION ORALE
for SAMSON such as new interaction models, editors (e.g. procedural generators), apps, wrappers or interfaces to existing software, connectors to web services, etc. The SAMSON Connect
website (http://www.samson-connect.net) is open for developers and users to easily distribute
and install SAMSON Elements.
We will present the architecture of SAMSON and its general design principles, as well as the
SAMSON SDK and SAMSON Connect.
S. Artemova and S. Redon, Physical Review Letters, 109:19, 2012
M. Bosson et al, Journal of Computational Physics, 231:6, 2012
M. Bosson et al, Journal of Computational Chemistry, 34:6, 2013
R. Rossi et al, Bioinformatics, 23:13, 2007
84
COMMUNICATION ORALE
Integration of Chemical and Biological
(’omics’) Data for the Prediction of Cancer
Cell-Line Sensitivity
Isidro Cortes ∗† 1 , Gerard J.p. Van Westen 2 , Guillaume Bouvier 3 , Mickael
Nilges 1 , John P. Overington 2 , Andreas Bender 3 , Therese Malliavin 1
1
2
Unite de Bioinformatique Structurale – Institut Pasteur de Paris, CNRS : UMR3528 – 28 rue du
docteur Roux 75724 Paris, France
European Bioinformatics Institute [Cambridge] (EMBL-EBI) – EMBL-EBI, Wellcome Trust Genome
Campus, Hinxton, Cambridgeshire, CB10 1SD, UK, Royaume-Uni
3
Centre for Molecular Science Informatics – Department of Chemistry, University of Cambridge,
Lensfield Road, CB2 1EW, Cambridge, Royaume-Uni
Cultured cell-lines have proved versatile disease models for cancer drug discovery [1]. In
the last decades, large-scale multi-omics initiatives have catalogued the somatic alterations of
cancer cell-line panels coupled with their pharmacological response to thousands of compounds
[1,2,3]. Although these cell-line collections have proved valuable to identify genomic markers of
drug sensitivity [2,3], the question now arises how these pharmacogenomic data can be meaningfully mined to discover cancer-specific drugs. Here, we propose the simultaneous modelling
of chemical and cell-line information in single machine learning models to predict with error
bars the growth inhibition 50% bioassay end-point (GI50) of 17,142 compounds screened against
59 cancer cell-lines from the NCI60 panel. The integration of these different, yet complementary, streams of information is often termed Proteochemometrics (PCM) or pharmacogenomic
modelling (PGM) [4]. PCM is a computational method to simultaneously model the bioactivity of multiple ligands against multiple biomolecular targets, and therefore permits to explore
the selectivity and promiscuity of ligands [4]. In typical PGM models, each compound-cell-line
interaction is numerically encoded by the concatenation of compound and cell-line descriptors,
which are related in single machine-learning models to a specific biological readout of interest.
To describe the compounds we used Morgan fingerprints, whereas to describe the cell-lines, we
downloaded and curated the following cell-line profiling data: gene expression, DNA copy-number
variation, protein abundance, exome sequencing, miRNA abundance, and cell-line fingerprints.
We benchmarked their predictive signal by training one model on each cell-line profiling data and
on compound fingerprints, finding that protein, gene transcript, and miRNA abundance provide
the highest predictive signal, significantly outperforming DNA copy-number variation or exome
sequencing data (Tukey’s HSD, P < 0.05). The performance of the best model on the test
set led to mean R20 and RMSE values of 0.83 and 0.40 pGI50 units, respectively. Comparable
mean RMSE values were obtained when interpolating and extrapolating compound bioactivities
to novel cell-lines (RMSE=0.43), and when interpolating compound bioactivities to structurally
similar compounds (RMSE=48-0.65). Nevertheless, the mean RMSE value increased till 0.83
when extrapolating to
∗
†
Intervenant
85
COMMUNICATION ORALE
chemically dissimilar compounds, indicating that careful attention should be paid to not overstep
the boundaries of the
models applicability domain. Finally, we demonstrate that the predicted bioactivities can be
used to predict growth inhibition patterns across the NCI60 panel for compounds excluded from
the training set (e.g. methotrexate), and that significant drug-pathway associations are consistent with the experimental data published in the literature. Conceptually, this approach could
also be extended to select personalized treatments on the basis of patients genomic makeup.
JN Weinstein. Drug discovery: Cell lines battle cancer. Nature 2012, 483, 544–545.
J Barretina, G Caponigro, N Stransky et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012, 483, 603–607.
Garnett, MJ, EE Edelman, SJS Heidorn et al. Systematic identification of genomic markers of
drug sensitivity in cancer cells. Nature 2012, 483, 570–575.
I Cortes-Ciriano, QU Ain, V Subramanian et al. Polypharmacology Modelling Using Proteochemometrics (PCM): Recent Methodological Developments, Applications to Target Families,
and Future Prospects. Med. Chem. Commun., 2015, 6, 24-50.
86
Session poster GT Enzymes/GGMM
87
Communication par affiche
Poster P1
Les thioltransférases : production et
élimination de sulfure d’hydrogène ?
Jean-Christophe Lec ∗ 1 , Séverine Boutserin 1 , Hortense Mazon 1 , François
Talfournier ∗ † 1 , Sandrine Boschi-Muller ∗ ‡ 1
1
UMR 7365 CNRS-Université de Lorraine (IMoPA) – CNRS : UMR7365, Université de Lorraine – 9
Avenue de la Foret de Haye, 54505 Vandoeuvre les Nancy, France
Les thioltransférases sont des enzymes ubiquitaires de la famille structurale des rhodanèses
qui catalysent le transfert de soufre d’un substrat donneur vers un substrat accepteur, via la
formation d’un intermédiaire persulfure sur la cystéine catalytique. Elles se répartissent en
deux familles selon la nature du substrat donneur et leurs rôles physiologiques proposés : les
3-mercaptopyruvate sulfurtransférases (3-MST) utilisent le 3-mercaptopyruvate (3-MP) et sont
impliquées dans la production de sulfure d’hydrogène, alors que les thiosulfate sulfurtransférases
(TST) utilisent le thiosulfate (TS) et assurent des fonctions variées comme la détoxication du
cyanure, la biosynthèse des centres Fe-S ou le catabolisme d’H2S au niveau mitochondrial. Afin
de caractériser les mécanismes moléculaires associés aux rôles physiologiques proposés pour les
thioltransférases, l’identification des bases moléculaires responsables de la spécificité structurale a
été abordée en évaluant l’efficacité catalytique des deux étapes de transfert de soufre en présence
de différents substrats donneurs et accepteurs. Les résultats obtenus sur des enzymes d’origine
humaine et bactérienne montrent que, contrairement à ce qui est décrit dans la littérature, le
3-MP est un meilleur substrat donneur que le TS pour les deux familles d’enzymes. De plus,
elles présentent des spécificités différentes pour le substrat accepteur : la thiorédoxine pour les
3-MST et le glutathion et le sulfite pour les TST.
∗
Intervenant
‡
†
88
Poster P2
Rôle fonctionnel d’une cavité interne
hydrophobe dans l’urate oxydase
Nathalie Colloc’h
∗ 1
, Thierry Prangé
2
1
Imagerie et stratégies thérapeutiques des pathologies cérébrales et tumorales (ISTCT) – Université de
Caen Basse-Normandie, CNRS : UMR6301, CEA – GIP CYCERON UMR 6301- CNRS Boulevard
Henri Becquerel BP 5229 14074 CAEN CEDEX, France
2
Laboratoire de cristallographie et RMN biologiques (LCRB) – CNRS : UMR8015, Université Paris V Paris Descartes – Faculté de Pharmacie 4 Avenue de l’Observatoire 75270 PARIS CEDEX 06, France
L’urate oxydase est un homo-tetramère de 135 kDa qui possède une cavité hydrophobe enfouie
dans chaque monomère et proche du site actif. Les structures cristallographiques sous pression
modérée de gaz inerte (10-30 bar) ou sous haute pression hydrostatique (1500 bar) ont montré
que la présence d’un gaz ou l’application de la pression entrainent tous deux une rigidification de
la cavité et induisent une inhibition du mécanisme catalytique. La flexibilité de la cavité semble
donc essentielle pour l’activité catalytique de cette enzyme. Elle peut donc être considérée comme
un site allostérique qui donnerait de la flexibilité au site actif voisin, et aurait également un rôle
de réservoir transitoire pour l’oxygène sur son chemin d’accès vers le site actif.
∗
Intervenant
89
Poster P3
Benzene-induced leukemogenesis: Irreversible
inhibition of PTPN2, a tumor suppressor
phosphatase involved in leukemia by the
hematotoxic metabolite benzoquinone
Romain Duval ∗ , Linh Chi Bui 1 , Laeticia Durant , Cécile Mathieu 1 , Emile
Petit , Jean-Marie Dupret , Jan Cools , Christine Chomienne , Fabien
Guidez , Fernando Rodrigues-Lima† 1
1
Unité de Biologie Fonctionnelle et Adaptative (BFA- RMCX) – CNRS : UMR8251, Université Paris
VII - Paris Diderot – UFR Sciences du Vivant - 4 rue Marie-Andrée Lagroua Weill-Hallé 75205 Paris
Cedex 13, France
Benzene (BZ) is a chemical compound of industrial and toxicological interest classified as a
class I human carcinogen. Environmental and occupational exposure to BZ lead to bone marrow
malignancies such as leukemia. The leukemogenic effects of BZ relies on its metabolization in
bone marrow cells into reactive metabolites, in particular benzoquinone (BQ) that can react
with macromolecules (arylation) and/or induce oxidative stress. Although BZ is well recognized
as a leukemogenic chemical, most of the key molecular and cellular mechanisms underlying its
hematotoxicity are not fully understood.
PTPN2 is a protein tyrosine phosphatase (PTP) mainly expressed in hematopoietic cells and
playing a key role in the homeostasis of the hematopoietic system. In particular, this PTP is
an important modulator of growth factors and JAK/STAT signaling pathways. Loss of function
analyses in patients with mutation/deletion of the PTPN2 gene and knock-out mouse models
indicate that PTPN2 acts as a tumor suppressor in haematologic disorders such as leukemia.
We found that BQ, the prime hematotoxic metabolite of BZ, is an irreversible inhibitor of
human PTPN2. Kinetic and biochemical analyses using purified PTPN2 indicated that the
irreversible inhibition of the enzyme by BQ is mainly due to arylation of its active site cysteine.
Exposure of immortalized human hematopoietic cells (Jurkat T and THP-1 lines) to BQ leads
to the irreversible inhibition of endogenous PTPN2 activity with a concomitant over activation
of JAK/STAT signaling pathway. Irreversible BQ-dependent inhibition of PTPN2 in cells was
found to be mainly due to overoxydation of its catalytic cysteine into sulfinic and/or sulfonic
forms.
In Vivo experiments conducted in mice confirmed that exposure to BZ leads to irreversible
inhibition of PTPN2 in bone marrow and spleen cells.
Our data provide the first mecanistic evidence that irreversible inhibition of PTPN2, a tumor
suppressor tyrosine phosphatase, may contribute to benzene-dependent leukemogenesis.
∗
†
Intervenant
90
Poster P4
Cellular redox signaling by thiol peroxidase :
Mechanisms responsible for the specificity of
the redox relay H2O2/Orp1/Yap1 in S.
cerevisiae
Antoine Bersweiler 1 , Hortense Mazon 1 , Alexandre Kriznik
Branlant 1 , Sophie Rahuel-Clermont† 1
1
∗ 1
, Guy
Lorraine – Biopôle, campus Biologie-Santé 9 Avenue de la Forêt de Haye, CS 50184 54505
Vandœuvre-lès-Nancy Cédex, France
Thiol-peroxidases, described as antioxidant enzymes, have also been associated with peroxidedependent cell signaling as sensor and relay of the H2O2-mediated signal (1). One of the best
documented examples of such a mechanism is the activation of the transcription factor Yap1, a
key regulator of the transcriptional peroxide stress response in Saccharomyces cerevisiae, which
depends on the formation of intramolecular disulfide bonds catalyzed by the thiol peroxidase
Orp1 (2 ; 3). In this mechanism, it has been proposed that the relay occurs via the oxidation
of Orp1 peroxidatic Cys as a sulfenic acid intermediate which reacts with Yap1 to form a mixed
disulfide species (3). In addition, the Ybp1 protein has been identified as an essential partner
for the activation of Yap1 by Orp1 (4).
The intrinsic reactivity of Orp1 sulfenic acid species could potentially result in competition between Yap1 and other thiols, such as the regeneration Cys of Orp1 or other cellular thiols. This
raises the question of the specificity of Yap1 activation by H2O2/Orp1. To address this question,
and to elucidate the role of Ybp1 in this mechanism, we have used an approach based on the
kinetic characterization of the two reactions in competition: the peroxydatic cycle of Orp1 and
the reaction withYap1, using rapid kinetic stop flow and quench flow techniques. From the study
of the impact of Ybp1 on these kinetics and the characterization of the protein-protein interactions between the three partners by fluorescence anisotropy and microcalorimetry, we propose
that Ybp1 and Yap1 recruit Orp1 within a ternary complex, which restrains intramolecular disulfide formation within Orp1 and allows the reaction between Orp1 sulfenic intermediate and Yap1.
(1) Fourquet S., Huang, M., D’Autreaux, B., Toledano, M.B. (2008), Antiox. Redox Sign.
10, 15565-76.
(2) Delaunay, A., Isnard, A.D., Toledano, M.B. (2000) EMBO J. 19, 5157-66.
(3) Delaunay, A., Pflieger, D., Barrault, M.B., Vinh, J., Toledano, M.B. (2002) Cell 111,
471-81.
∗
†
Intervenant
91
Poster P4
(4) Veal, E.A., Ross, S.J., Malakasi, P., Peacock, E., Morgan, B.A. (2003) J. Biol. Chem.
278, 30896-904.
92
Poster P5
Molecular basis of the alteration of brain
glycogen metabolism by dithiocarbamate
pesticides: impairment of brain glycogen
phosphorylase
Cécile Mathieu ∗ 1 , Linh Chi Bui 1 , Solène Boitard 2 , Onnik Agbulut 2 ,
Jean-Marie Dupret 1 , Fernando Rodrigues-Lima 1
1
Unité de Biologie Fonctionnelle et Adaptative (BFA- RMCX) – CNRS : UMR8251, Université Paris
VII - Paris Diderot – UFR Sciences du Vivant - 4 rue Marie-Andrée Lagroua Weill-Hallé 75205 Paris
Cedex 13, France
2
Adaptation Biologique et Vieillissement – CNRS : UMR8256, Université Pierre et Marie Curie
(UPMC) - Paris VI – Campus de Jussieu - 4 place Jussieu 75005 Paris, France
Dithiocarbamates (DTC) are organosulfur compounds widely used as fungicide. Acute exposure is mainly a concern for agricultural and industrial workers but large population can be
exposed through residues in food and water. The high reactivity of these chemical lead to numerous toxic effects and have been linked to Parkinson’s disease. Different biological processes
have been suggested to be altered by DTC including glycogen (gln) metabolism.
Gln is a glucose polymer found primarily in liver, muscles and brain where it represents the main
storage-form of glucose. In brain, Gln is mainly found in astrocytes and appears to be critical
for certain neurological processes such as learning and long term memory consolidation. Brain
glycogen phosphorylase (bGP) is the key enzyme involved in mobilization of Gln. This enzyme
is thighly regulated by both phosphorylation and binding of allosteric effectors.
In order to better understand the molecular mechanisms of neurotoxicity linked to DTC, we
studied the impact of Thiram (TH), a well known DTC, on bGP. Incubation of mouse brain
extracts with TH impaired glycogen mobilization which is concomitant with the inhibition of
bGP. Studies on recombinant human bGP showed that TH and its metabolites were potent inhibitors of bGP. Chemical-labelling, electrophoretic and mass spectrometry analyses suggested
that inhibition of purified bGP by TH occured through modification of critical cysteines residue
and formation of intramolecular/intermolecular disulfide bonds.
Altogether our data indicate that certain DTC inhibit the glycogenolytic activity of bGP through
the modification of key cysteine residues with subsequent impairment of Gln catabolism. This
may lead to altered energy metabolism or toxic accumulation of Gln in astrocytes. More broadly,
owing to the important role of Gln in neurological processes, impairment of bGP functions in
brain could contribute to the neurotoxic effects of DTC.
∗
Intervenant
93
Poster P6
Réactivité de la carbon monoxide
dehydrogenase à nickel avec l’oxygène
Meriem Merrouch ∗† 1 , Jessica Hadj-Said 1 , Lilith Domnik 2 , Holger Dobbek
2
, Sébastien Dementin 1 , Claude Léger 1 , Vincent Fourmond 1
1
Bioénergétique et Ingénierie des Protéines (BIP) – CNRS : UMR7281, Université de la Méditerranée Aix-Marseille II – 31 Chemin J. Aiguier, 13402 Marseille Cedex 20, France
2
Institut für Biologie, Strukturbiologie/Biochemie – Humboldt-Universität zu Berlin, 10099 Berlin,
Allemagne
Les Carbon Monoxide Dehydrogenases (CODH) catalysent l’oxydation réversible du CO en
CO2 selon la réaction:
CO + H2O CO2 + 2e- + 2H+
Le site actif des CODH isolées d’organismes anaérobies est constitué d’un centre [Ni-4Fe-4S]
[1, 2]. Ces enzymes sont réputées être extrêmement sensibles à l’O2, cependant le mécanisme
d’inactivation par l’oxygène n’est pas connu [3]. Nous avons étudié par électrochimie directe
[4] l’effet de l’oxygène sur deux CODH : la CODH II de Carboxydothermus hydrogenoformans
(Ch) et la CODH de Desulfovibrio vulgaris Hildenborough (Dv ). Nos résultats ont montré que
l’oxygène interagit rapidement avec les sites actifs des deux CODH, cependant l’inactivation est
réversible, principalement après une étape de réduction de l’enzyme. Nous montrons également
que la réactivité des deux enzymes vis-à-vis de l’O2 est différente: la fraction de Dv CODH
réactivée après exposition à l’O2 est beaucoup plus importante que celle de la Ch CODH. En
particulier, dans les conditions de concentration d’O2 utilisées pendant notre étude, l’enzyme de
Dv est réactivée presque complètement, ce qui n’est pas le cas pour l’enzyme de Ch.
Nos travaux apportent pour la première fois des données mécanistiques concernant l’inhibition
des CODH par l’O2 et montrent que la réactivité des CODH vis-à-vis de l’O2 n’est pas la même
d’une espèce à l’autre, comme c’est le cas pour d’autres enzymes à Nickel, comme les hydrogénases.
1. Jeoung, J.-H., Dobbek, H. Science 2007, 318, 1461-1464.
2. Drennan, C., Doukov, T., Ragsdale, S. Journal of Biological Inorganic Chemistry 2004,
9, 511-515.
3. Can, M., Armstrong, F. A., Ragsdale, S. W. Chem. Rev. 2014, 114, 4149-4174.
4. Léger, C., Bertrand, P. Chem. Rev. 2008, 108, 2379-2438.
∗
†
Intervenant
94
Poster P7
Regulation of thiol peroxidases in
peroxide-dependent redox signaling:
Thioredoxin vs. glutathione in reduction of
hyperoxidized 2-Cys peroxiredoxin by
Sulfiredoxin
Samia Boukhenouna 1 , Hortense Mazon 1 , Guy Branlant 1 , Christophe
Jacob 1 , Michel Toledano 2 , Sophie Rahuel-Clermont ∗† 1
1
2
Lorraine – Biopôle, campus Biologie-Santé 9 Avenue de la Forêt de Haye, CS 50184 54505
Vandœuvre-lès-Nancy Cédex, France
Laboratoire Stress Oxydants et Cancer (LSOC) – CEA – CEA, iBiTecS, LSOC, Gif-sur-Yvette Cedex,
France., France
Due to its oxidant properties, hydrogen peroxide (H2O2) can act both as a toxic and as a
cellular messenger. Typical 2-Cys-peroxiredoxins (Prx) are thiol peroxidases sensitive to H2O2mediated hyperoxidation of the catalytic Cys to the sulfinic acid state. This unique property is
involved in regulation of Prx functions in peroxide-dependent redox signaling and is reversed by
ATP-dependent reduction by Sulfiredoxin (Srx).
Srx catalytic mechanism involves a unique enzymatic chemistry, which leads to the formation of a
thiolsulfinate intermediate PrxSO-SSrx that must be reduced to complete the cycle. To evaluate
the mechanism and efficiency of the cellular redox systems thioredoxin (Trx) and glutathione
(GSH) in Srx recycling, we combined in vitro steady state and single turnover kinetic analyses
of the reaction monitored by enzymatic coupled assay, Prx intrinsic fluorescence, SDS-PAGE
or reverse phase chromatography coupled to mass spectrometry for the characterization of intermediate species, and in vivo approaches using S. cerevisiae strains depleted in Trx or GSH.
In the case of S. cerevisiae, owing to the presence of an additional Cys residue, Srx recycling
involves the formation of an oxidized Srx intermediate that is efficiently reduced by Trx. On the
contrary, in the case of Srx lacking this resolving Cys48 as in mammals or plants (1-Cys Srxs),
using S. cerevisiae Srx mutants lacking Cys48 as a model, we show that GSH reacts directly with
the thiolsulfinate intermediate on the Srx moiety, releasing glutathionylated Srx as a catalytic
intermediate. Unexpectedly, the results suggest that GSH binds to the thiolsulfinate complex,
thus allowing efficient and non rate-limiting reduction. Total cellular depletion of GSH impacted
the recycling of Srx, confirming in vivo that GSH is the physiologic reducer of 1-Cys Srx.
∗
†
Intervenant
95
Poster P8
Computer-aided engineering of a
transglycosylase for the glucosylation of an
unnatural disaccharide of relevance for
bacterial antigen synthesis
Alizée Verges 1 , Emmanuelle Cambon 1 , Sophie Barbe 1 , Stéphane
Salamone 2 , Yann Le Guen 2 , Claire Moulis 1 , Laurence Mulard 2 , Magali
Remaud-Siméon 1 , Isabelle André ∗ 1
1
Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés (LISBP) – Institut National des
Sciences Appliquées [INSA], Centre national de la recherche scientifique - CNRS (France), Institut
national de la recherche agronomique (INRA) – 135 Avenue de rangueil 31077 Toulouse cedex 04, France
2
Institut Pasteur, Unité de chimie des biomolécules – CNRS : UMR3523, Institut Pasteur de Paris – 28
rue du Docteur Roux 75724 Paris Cedex 15, France
The exploration of chemo-enzymatic routes to complex carbohydrates has been hampered
by the lack of appropriate enzymatic tools having the substrate specificity for new reactions.
Here, we used a computer-aided design framework to guide the construction of a small, diversity controlled library of amino acid sequences of an a-transglucosylase, the sugar binding subsites of which were re-engineered to enable the challenging 1,2-cis -glucosylation of a partially
protected -linked disaccharide – allyl (2-deoxy-2-trichloroacetamido--D-glucopyranosyl)-(12)--Lrhamnopyranoside, a potential intermediate in the synthesis of Shigella flexneri cell-surface
oligosaccharides. The target disaccharide is not recognized by the parental wild-type enzyme
and exhibits a molecular structure very distinct from that of the natural -(1,4)-linked acceptor.
A profound reshaping of the binding pocket had thus to be performed. Following the selection of
23 amino acid positions from the first shell, mutations were sampled using RosettaDesign leading
to a subset of 1,515 designed sequences, which were further analyzed by determining the amino
acid variability among the designed sequences and their conservation in evolutionary related
enzymes. A combinatorial library of 2.7 10^4 variants was finally designed, constructed and
screened. One mutant showing the desired and totally new specificity was successfully identified
from this first round of screening. Impressively, this mutant contained 7 substitutions in the first
shell of the active site leading to a drastic reshaping of the catalytic pocket without significantly
perturbing the original specificity for sucrose donor substrate.
This work illustrates how computer-aided approaches can undoubtedly offer novel opportunities
to design tailored carbohydrate-active enzymes of interest for glycochemistry or synthetic glycobiology.
This work was supported by the ANR Project GLUCODESIGN (ANR-08-PCVI-002-02).
A.V., E.C., S.B. contributed equally to the work.
∗
Intervenant
96
Poster P8
References :
Verges A., Cambon E., Barbe S., Salamone S., Le Guen Y., Moulis C., Mulard L.A., RemaudSiméon M., André I. 2015. Computer-aided engineering of a transglucosylase for the glucosylation
of an unnatural disaccharide of relevance for bacterial antigen synthesis.ACS Catalysis. 5:11861198
97
Poster P9
Computational Enzyme Design through
deterministic search and counting methods
Clément Viricel 1 , Seydou Traore 1 , David Simoncini 2 , David Allouche 2 ,
Simon De Givry 2 , Isabelle André 1 , Thomas Schiex 2 , Sophie Barbe ∗† 1
1
Sciences Appliquées [INSA], Centre National de la Recherche Scientifique - CNRS, Institut national de
la recherche agronomique (INRA) – 135 Avenue de rangueil 31077 Toulouse cedex 04, France
2
Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA) – Institut national de
la recherche agronomique (INRA) : UR875 – Chemin de Borde Rouge, 31320 Castanet Tolosan, France
Enzymes are powerful catalysts, which can be used for a wide range of innovative, efficient
and eco-compatible processes that can open the way to many biotechnological applications. However, enzymes available in nature have evolved to operate under physiological conditions on a
narrow range of substrates and they often do not display the physico-chemical properties required
for their practical use in industrial-scale biocatalytic processes. Enzyme engineering has become
a key technology to modulate both the structure and the function of polypeptides and generate
enzymes displaying desired properties/activities. The integration of computational methods in
enzyme engineering strategies has grown interest to increase the chances of success to tailor the
wanted enzymes, while reducing the human and financial costs.
Structure-based Computational Protein Design (CPD) aims at guiding evolution of proteins on
relevant sequence space regions to achieve the desired function and thus reduce the size of mutant
libraries to build and screen experimentally. Starting from an exponentially large space of possible sequences, CPD seeks to identify those that fold into stable three-dimensional structures and
possess the desired functional properties. Despite notable achievements in the field, numerous
limitations still need to be addressed in these computational methods to improve their efficiency,
predictability and reliability.
In this work, we have adapted and extended several cutting-edge deterministic combinatorial optimization techniques to solve various CPD problems. In particular, we have developed novel enzyme design approaches based on Cost Function Networks (CFN) (combining Depth First Branch
and Bound search with Local Consistency properties) to search the sequence-conformation space
to identify provably the optimal solution in regard to the design objective function (namely
enzyme or enzyme-substrate complex stability in this work) [1-4]. These approaches are able
to handle highly complex enzyme design problems that previously could not be solved exactly.
Compared to deterministic methods commonly used in CPD, the CFN-based approaches speed
up the search process by several orders of magnitude. Hence, the optimal solution is provably
found for enzyme design problems with large sequence-conformation spaces. The CFN-based
approach has also been extended to enable the generation of ensembles of near-optimal solutions
that could be used for the rational design of sequence libraries.
∗
†
Intervenant
98
Poster P9
Moreover, the efficiency of the CFN optimization techniques has also been exploited to develop a scoring and search method aiming to optimize the enzyme-substrate binding affinity [5].
It combines a statistical mechanics-derived ensemble-based approach with the speed and the
completeness of CFN algorithms to compute approximate enzyme-substrate binding constants
with deterministic guarantees.
Most recent developments and their application on various CPD problems will be presented.
1- Allouche D, Davies J, de Givry S, Katsirelos G, Schiex T, Traoré S, André I, Barbe S,
Prestwitch S, O’Sullivan B. 2014. Computational Protein Design as an optimization Problem.
Artificial Intelligence 212:59-79.
2- Traoré S, Allouche D, André I, de Givry S, Katsirelos G, Schiex T, Barbe S. 2013. A New
Framework for Computational Protein Design through Cost Function Network Optimization.
Bioinformatics 29:2129-36
3- Allouche D, Traore S, André I, de Givry S, Katsirelos G, Barbe S, Schiex T. 2012. Computational Protein Design as a Cost Function Network Optimization Problem. In, Proceedings of the
18th international conference on Principles and Practice of Constraint Programming, Québec,
Canada.
4- Traoré S, Roberts K-E, Allouche D, Donald B-R, André I, Schiex T, Barbe S. Fast search
algorithms for Computational Protein Design. Submitted
5- Viricel C, Simoncini D, Allouche D, de Givry S, Barbe S, Schiex T. 2015. Approximate Counting with Deterministic guarantees for Affinity Computation. In, proceedings of the international
conference on Modelling, Computation and Optimization in Information Systems and Management Sciences, MCO 2015, Metz, France: Advances in Intelligent Systems and Computing Springer.
99
Poster P10
Towards the development of an in silico tools
for kinetics prediction
Abdennour Braka
∗ 1,2
, Stéphane Bourg 1,2,3 , Norbert Garnier 1 , Samia
Aci-Sèche 2 , Pascal Bonnet 2
1
Centre de biophysique moléculaire (CBM) – CNRS : UPR4301 – Rue Charles Sadron 45071
ORLEANS CEDEX 2, France
2
3
Fédération de Recherche Physique et Chimie du Vivant – CNRS : FR2708 – Rue Charles Sadron,
45071 Orléans Cedex 2, France
Molecular docking plays an increasing important role in drug discovery programs. While
docking tools can accurately predict the binding mode of a ligand into the active site of a protein, the evaluation of binding affinity is still a major issue. One of the most important limitations
is the treatment of protein flexibility during docking. The simplistic rigid model of ligand/protein
complex is inadequate to represent the full motion of a protein structure during ligand binding.
Moreover, most scoring functions are often correlated with activity-based data without taking
into account the kinetics of ligand binding (koff and kon).
This project aims to develop a new predictive method to improve the docking performance
by considering protein flexibility. Protein kinases are promising therapeutic targets, which are
highly flexible. In this study, we focus on the development of a novel in silico method using the
p38 Map kinase as an example.
To represent the motion of the protein, thousands of conformations were generated using molecular dynamics simulations (MD). We compared the performance of classical MD (cMD) with two
biased methods: an in house restrained MD method (rMD) (1) and the Replica-Exchange MD
(REMD) (2). As expected, the biased methods (rMD, REMD) widely outperform the cMD for
the protein conformational sampling. We then used a clustering method based on k-means to
select a representative subset of binding site conformations. A multiconformational docking is
actually ongoing using this representative subset for the protein; and the ligands were extracted
from the Directory of Useful Decoys (DUD-E) (3) database.
Later, we envisage taking into account the association pathway of the ligand in order to predict
kon association rate constant. The prediction of such biochemical data could be very useful for
preclinical compound selection and optimization.
References
(1) Aci-Sèche, S.; Garnier, N.; Genest, D.; Bourg, S.; Marot, C.; Morin-Allory, L.; Genest,
∗
Intervenant
100
Poster P10
M. A Restrained Molecular Dynamics Empirical Approach for Generating a Small Set of
Structures Representative of the Internal Flexibility of a Receptor. QSAR Comb. Sci.
2009, 28, 959–968.
(2) Sugita, Y.; Okamoto, Y. Replica-Exchange Molecular Dynamics Method for Protein
Folding. Chemical Physics Letters 1999, 314, 141–151.
(3) Mysinger, M. M.; Carchia, M.; Irwin, J. J.; Shoichet, B. K. Directory of Useful Decoys,
Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. J. Med. Chem.
2012, 55 (14), 6582–6594.
101
Poster P11
In silico experiments on biomolecules through
the Static Modes: An efficient approach to
predict and design function
Marie Brut
∗ 1
1
Laboratoire d’analyse et d’architecture des systèmes [Toulouse] (LAAS) – Institut National
Polytechnique de Toulouse - INPT, Université Paul Sabatier (UPS) - Toulouse III, CNRS : UPR8001,
Institut National des Sciences Appliquées [INSA] - Toulouse – 7 Av du colonel Roche 31077
TOULOUSE CEDEX 4, France
A highly accurate understanding of the molecular mechanisms that govern biological processes is required to accelerate medical and pharmaceutical innovation. Molecular modeling
and simulation have become indispensable tools to address this question and provide new insight where information is not available through experimentation alone. However, predicting
the structure-activity relationship that determines biomolecular function remains an outstanding challenge for computational investigations. In this competitive background, the Static Mode
method developed at LAAS-CNRS, is an innovative approach to account for biomolecular flexibility in the prediction of structure/activity relationship. Thanks to the pre-calculation of the
intrinsic deformations of biomolecules submitted to custom designed stimuli, this method allows
the user to design specific actions applied on single or multi-atom sites, and to anticipate the
induced structural/mechanical changes. We will show how the Static Modes can be used to
efficiently localize allosteric sites and to decipher allosteric mechanisms with atomic precision.
We will also present selected results on various enzymes to propose a guideline for efficient,
predictive and custom in silicoexperiments: ligand-induced accommodation, mutation effects,
domain communication... In particular, we will focus on the case of Ras oncoproteins and will
discuss how this low-cost computational approach offers unprecedented possibilities to explore
Ras biomechanical properties and to mechanically simulate the activation/inactivation process.
Our goal is to acquire a unique understanding of the biomolecular processes that govern Ras
biological function and mutation effects, and consequently, how to reverse them by performing
the screening of mutations impact in view of guiding mutation experiments.
∗
Intervenant
102
Poster P12
Proteus: dessin de protéine en backbone
flexible
Karen Druart
∗† 1
, David Mignon 1 , Edouard Audit 2 , Georgios Archontis‡
3
, Thomas Simonson§ 1
1
Laboratoire de Biochimie (BIOC) – CNRS : UMR7654, Polytechnique - X – Laboratoire de Biochimie
Ecole Polytechnique 91128 Palaiseau, France
2
Maison de la Simulation (MDLS) – CEA – USR 3441 bât. 565 CEA Saclay 91191 Gif-sur-Yvette
cedex, France
3
Theoretical and Computational Biophysics Group – Department of Physics University of Cyprus P.O
BOX 20537 1678 Nicosia Cyprus, Chypre
Le dessin computationnel de protéine (CPD) est une méthode qui permet de prédire les
mutations pertinentes à réaliser pour conférer aux enzymes de nouvelles propriétés. Un logiciel
de CPD assez complet, Proteus, a été developpé dans l’équipe de Thomas Simonson à l’Ecole
Polytechnique (Simonson et al, 2013); il s’appuie sur un modèle de mécanique moléculaire fourni
par le programme XPLOR. Brièvement, XPLOR précalcule les énergies d’interaction entre toutes
les paires de résidus de la protéine. Ces calculs sont réalisés en maintenant le backbone fixe, sur
lequel les rotamères d’une librairie discrète sont positionnés. Les énergies sont ensuite relues
par un autre programme (“proteus”), qui permet une exploration Monte Carlo de l’espace des
séquences et conformations. Cependant, la rigidité du backbone limite l’espace conformationnel
de la protéine et diminue en principe la qualité du modèle. Très récemment, nous avons ajouté la
possibilité de prendre en compte un ensemble de conformations du backbone, au lieu d’une seule,
avec la possibilité pendant le Monte Carlo de passer d’une conformation à l’autre. Pour obtenir
un taux d’acceptation raisonnable, le saut de backbone est suivi par une courte relaxation dans
l’espace des rotamères. Le changement global, saut de backbone puis relaxation rotamérique, est
accepté ou rejeté selon un test de Metropolis généralisé, qui garantit en principe une exploration
selon une distribution de Boltzmann des séquences et des structures. Les premiers résultats
obtenus sur 3 systèmes tests (tyrosyl-ARNt synthétase, domaines SH3 et SH2) seront présentés.
∗
Intervenant
‡
§
†
103
Poster P1
Molecular dynamics with excited normal
modes reveals the role of the activation loop of
Cyclin-Dependent Kinases in their open/closed
conformational equilibrium
Nicolas Floquet ∗ 1 , Mauricio G.s. Costa 2 , Paulo R. Batista , Pedro
Renault 3 , Paulo M. Bisch , Florent Raussin , Jean Martinez , May C.
Morris , David Perahia 4
1
Institut des Biomolécules Max Mousseron (IBMM) – CNRS : UMR5247, Université de Montpellier –
Faculté de Pharmacie, 15 Av. Charles Flahault, BP 14491, 34093 Montpellier, Cedex 05, France
2
Programa de Computação Científica, Fundação Oswaldo Cruz – 21949900, Rio de Janeiro, Brésil
3
Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro – 21949-901, Rio
de Janeiro, Brésil
4
Laboratoire de Biotechnologie et Pharmacologie génétique Appliquée (LBPA) – CNRS : UMR8113,
École normale supérieure (ENS) - Cachan – 61 AVENUE DU PRESIDENT WILSON 94235 CACHAN
CEDEX, France
Cyclin-dependent kinases are central for timely regulation of cell cycle progression and constitute attractive pharmacological targets. Characterization of the structural and dynamic features
of these kinases is essential to gain in-depth insight into their structure-activity relationships.
Structural studies of CDK2/Cyclin A have yielded a wealth of information concerning the conformations of the monomeric and heterodimeric forms of this kinase. There is however much
less structural information available for other CDK/Cyclin complexes, including CDK4/Cyclin
D complexes. In this study, we performed Normal Modes Analyses on CDK2/Cyclin A and
CDK4/Cyclin D1 X-ray structures, which display a different position of the Cyclin partner relative to the CDK, CDK2/Cyclin A being in a “closed” conformation in contrast to the “open”
conformation of CDK4/Cyclin D1. Interestingly, we observed that the lowest frequency NM
computed for each of these complexes described the transition between the “ open ” and the “
closed ” conformations. Exploration of this motion with an explicit representation of the solvent
using the Molecular Dynamics with Excited Normal Modes (MDeNM) method confirmed that
the closed conformation was the most stable for the CDK2/Cyclin A complex, in agreement
with all available structures in the PDB. On the contrary, we clearly show that an open closed
equilibrium may exist in CDK4/Cyclin D1, with closed conformations resembling that captured
for CDK2/Cyclin A. Using the same approach, the putative roles of both the phosphoryl group
on Thr160 and of the conformation of the T-loop on this conformational equilibrium were investigated. These results provide, for the first time, a dynamic view of Cyclin-dependent kinases
and reveal the existence of intermediate conformations which have not yet been characterized for
CDK members other than CDK2, and which will be useful for the design of inhibitors, targeting
critical conformational transitions.
∗
Intervenant
104
Poster P14
Design raisonné de procédés enzymatiques
pour la dégradation améliorée de biopolymères
Marc Gueroult
1
∗ 1
, Pablo Alvarez 1,2 , Emilie Amillastré
Duquesne 1 , Sophie Barbe ∗
, Alain Marty
1,3
, Isabelle André
1,2
, Sophie
∗ † 1
1
Sciences Appliquées [INSA], Institut national de la recherche agronomique (INRA) : UMR792, CNRS :
UMR5504 – 135 Avenue de rangueil 31077 Toulouse cedex 04, France
2
Toulouse White Biotechnology (TWB) – Institut national de la recherche agronomique (INRA) :
UMR1337, CNRS : UMS3582 – 3 rue des satellites F-31400 Toulouse, France
3
Carbios – Carbios – biopole Clermont Limagne rue Emile Duclaux, F-63360 St Beauzire, France
Le recyclage des plastiques d’origine biosourcée est un enjeu économique d’importance de nos
jours. Au sein du laboratoire, des approches génomiques ont permis d’isoler une serine protéase
qui montre une activité hydrolytique importante vis-à-vis de certains biopolymères couramment
utilisés dans l’industrie, tel que l’acide polylactique (PLA). Cependant, l’activité de dégradation
de cette enzyme n’est pas suffisante pour envisager son utilisation dans des procédés industriels.
Ainsi, pour améliorer son activité catalytique, une approche d’ingénierie enzymatique a été
développée au sein de notre équipe. Elle s’appuie sur des techniques de modélisation moléculaire
pour d’une part mieux comprendre les déterminants moléculaires impliqués dans la reconnaissance du polymère par l’enzyme et d’autre part, pour proposer des modifications à apporter
notamment par des mutations d’acides aminés, qui permettraient d’améliorer les performances
catalytiques de l’enzyme et contrôler la nature des produits obtenus.
Dans le cadre de ces travaux, nous avons mis en place une stratégie de modélisation moléculaire multi-échelles pour étudier différentes facettes du procédé enzymatique de dégradation des
polymères et guider de façon raisonnée l’ingénierie de ces enzymes. Ces approches combinent
l’utilisation de modèles atomiques et de modèles “ gros grain ” à des outils dédiés à l’étude des
interactions moléculaires pour modéliser l’ensemble du processus enzymatique de dégradation
des polymères. L’étude réalisée pour l’amélioration du procédé enzymatique de dégradation du
PLA sera rapportée.
Financement : Cette étude a été financée dans le cadre du Projet FUI ThanaplastTM en collaboration avec la société CARBIOS
∗
†
Intervenant
105
Poster P1
Ligand-Based QSAR studies on some
Cis-Stilbenes as Cyclooxygenase Inhibitor
Safia Kellou-Taïri
1
∗ 1
, Zohra Bouakouk-Chitti
Université des Sciences et de la Technologie Houari Boumediene [Alger] (USTHB) – BP 32 EL ALIA
16111 BAB EZZOUAR ALGER, Algérie
Cyclooxygenase (COX), which is the key enzyme in the synthesis of prostaglandins (PGs)
from arachidonic acid, exists in two isoforms: COX-1 and COX-2. COX-1 is constitutively expressed in a variety of cell types and is mainly involved in the synthesis of cytoprotective PGs in
gastrointestinal (GI) tract, whereas COX-2 is expressed during inflammatory conditions. Nonsteroidal anti-inflammatory drugs (NSAIDs) act through the inhibition of COX. All classical
NSAIDs are capable of inhibiting both isoforms; however, they are found to be bound more
tightly to COX-1. As a result, those drugs are associated with a high risk of adverse effects on
the GI tract as well as on the renal function. Owing to these problems, a novel NSAIDs generation has been developed by increasing the selectivity of COX-2. However, the use of specific
COX-2 selective drugs was a failure owing to their cardiovascular side effects. Alternative to
those drugs are natural products, which offer a great hope for the identification of bioactive lead
compounds and their development into drugs.
Stilbenes are a group of phytochemicals that can be commonly found in higher plants with a
wide global distribution. Experimental studies have indicated their multiple chemopreventive
properties and anti-inflammatory activity [1, 2]. Moreover, various laboratories are interested in
the design, synthesis, and biological evaluation of cis-Stilbene derivatives. All the world’s major
pharmaceutical and biotechnology companies use computational design tools. The ligand-based
computer-aided drug discovery (LB-CADD) approach involves the analysis of ligands known to
interact with a target of interest.
The studies presented in this work describe a series of cis-stilbene analogs selected in literature for their anti-inflammatory effect. We used the Canvas module of Schrödinger software
[3], which allows for novel QSAR methodologies to provide predictive and interpretable models.
The results obtained will be used in understanding the possible structural modification of these
molecules to improve their inhibitory potency.
References:
A. Cassidy, B. Hanley, R. M. Lamuela Raventos. J. Sci. Food Agric. 2000; 80:1044-1062.
P. Fresco, F. Borges, C. Diniz, M. P. Marques. Med. Res. Rev. 2006; 26:747–766.
Canvas, Version 2.2, Schrödinger, LLC, NY2014.
∗
Intervenant
106
Poster P
FOCUSED LIGAND LIBRARIES FOR
COVALENT DOCKING ON SERINE
ACTIVE PROTEINS
Virginie Martiny
1
∗ 1,2
, Edithe Selwa 1 , Hanna Debiec 2 , Pierre Ronco 2 ,
Bogdan Iorga 1
Institut de Chimie des Substances Naturelles, CNRS UPR 2301, LabEx LERMIT – CNRS : UPR2301
– Avenue de la Terrasse, Bat. 27, 91198 Gif-sur-Yvette, France
2
Laboratoire « Des maladies rénales rares aux maladies fréquentes, remodelage et réparation »,
INSERM
UMRS 1155 − −Inserm : U M RS1155 − −HpitalT enon, 4ruedelaChine, 75970P arisCedex20, F rance
The use of covalent inhibitors has known a very important development in recent years [1-4].
Currently, 30 % of the molecules approved for clinical use present a covalent mechanism [3]. The
covalent docking technique also became more and more popular, being now implemented in most
major software packages (GOLD, Glide, AutoDock, ICM, MOE, etc.) and on several dedicated
web servers (DOCKovalent, Covalent Dock Cloud, etc.). A major limitation that hampers the
use of covalent docking on large scale is the availability of chemical libraries with ligands prepared
in a particular format, compatible with this technique. Indeed, the existing chemical libraries
cannot be used directly in covalent docking and require a special preparation for this specific
purpose.
In this work, we have generated focused ligand libraries, compatible with the GOLD docking
program, suitable for covalent docking on serine active proteins. For this purpose, we collected
from the literature and available databases (PDB, DrugBank, etc.) all known structural patterns
allowing a covalent binding with a serine active residue. In order to avoid the side effects due to
non-specific interactions, an additional filter was applied and we kept only the functional groups
with a low to moderate electrophilic reactivity. A number of 18 structural patterns were thus
selected, then translated into SMARTS strings that were further used for substructure search in
the ZINC databank ( 22 millions compounds). In this way, we identified commercially available
molecules possessing the pattern of interest and constituted one focused library per pattern. All
identified compounds were converted into a form suitable for covalent docking using SMIRKS
reactions and in-house developed scripts based on the CACTVS Chemoinformatics Toolkit [5].
These focused libraries of covalent ligands are currently being used for virtual screening campaigns on serine active proteins of therapeutic interest.
Singh J., Petter R.C., Baillie T.A., Whitty A. The resurgence of covalent drugs. Nat. Rev. Drug
Discov. 2011;10(4):307-17
Bachovchin D.A., Cravatt B.F. The pharmacological landscape and therapeutic potential of serine hydrolases. Nat. Rev. Drug. Discov. 2012;11(1):52-68
∗
Intervenant
107
Poster P1
Johnson D.S., Weerapana E., Cravatt B.F. Strategies for discovering and derisking covalent, irreversible enzyme inhibitors. Future Med. Chem. 2012;2(6):949-964
Maryanoff B.E., Costanzo M.J. Inhibitors of proteases and amide hydrolases that employ an
alpha-ketoheterocycle as a key enabling functionality. Bioorg. Med. Chem. 2008;16(4):1562-95
Ihlenfeldt W.D., Takahashi Y., Abe H., Sasaki S. I. Computation and management of chemical
properties in CACTVS: An extensible networked approach toward modularity and compatibility.
J. Chem. Inf. Comp. Sci. 1994;34:109-116 (www.xemistry.com).
108
Poster P1
Inhibition sélective des ADN glycosylases :
simulation par dynamique moléculaire et
docking pour la recherche et la conception
d’inhibiteurs
Charlotte Rieux ∗ 1 , Franck Coste 1 , Françoise Culard 1 , Stéphane Goffinont
1
, Stéphane Bourg 1,2 , Bertrand Castaing 1 , Norbert Garnier 1
1
2
Centre de biophysique moléculaire (CBM) – CNRS : UPR4301 – Rue Charles Sadron 45071
ORLEANS CEDEX 2, France
Les organismes vivants sont constamment soumis aux effets de facteurs endogènes ou exogènes tels que des agents chimiques (oxydants et alkylants) ou physiques (radiations UV et
gamma) qui peuvent endommager leur ADN. Pour survivre sans contrepartie aux effets néfastes
de ces lésions de l’ADN, des mécanismes moléculaires de réparation ont été mis en place, notamment le système de réparation par excision de base (BER). Les ADN-glycosylases sont les
enzymes qui participent au BER en détectant les bases altérées de l’ADN (oxydées, alkylées
et/ou dégradées) telle que la 8-oxoguanine (une des lésions extrêmement mutagène) [1].
Bien qu’impliqué dans la stabilité génétique des organismes, le système BER peut cependant induire une résistance aux traitements par radiothérapie. En effet, ce type de traitement entraine
des lésions oxydatives sur l’ADN qui peuvent être prises en charge par le BER. La recherche
d’inhibiteurs des enzymes du BER présente donc un enjeu thérapeutique dans la lutte contre le
cancer.
Dans ce cadre, nous avons choisi de cibler les ADN glycosylases. Ces enzymes initient le système
BER. Notre étude se focalise en particulier sur les ADN glycosylases de la superfamille H2TH
(Hélice 2 Tours Hélice) telles que les protéines bactériennes Fpg, Nei et les protéines humaines
hNeil1, hNeil2 et hNeil3 qui réparent les pyrimidines et purines oxydées [1]. Dans un premier
temps, nous nous sommes intéressés aux mécanismes d’interaction de ces enzymes libres ou complexées à l’ADN endommagé, avec des ligands de types nucléobases récemment identifiés comme
des inhibiteurs. Pour cela nous utilisons principalement des simulations de dynamique moléculaire et des méthodes d’amarrage moléculaire. Les résultats préliminaires seront alors confrontés
aux nombreuses données structurales et fonctionnelles de l’inhibition de ces enzymes disponibles
dans la littérature [2, 3]. L’objectif à plus long terme de cette étude est de mettre en place
une méthode de tri moléculaire virtuel pour cribler des banques de molécules synthétiques ou
naturelles afin de faciliter la recherche d’inhibiteurs.
Prakash, A.; Doublié, S.; Wallace, S.S. (2012)
∗
Intervenant
109
Poster P1
The Fpg/Nei Family of DNA Glycosylases : Substrates, Structures and Search of Damage.
Prog Mol Biol Transl Sci. 110, 71-91.
Biela, A.; Coste, F.; Culard, F.; Guerin, M.; Goffinont, S.; Gasteiger, K.; Cieśla, J.; Winczura,
A.; Kazimierczuk, Z.; Gasparutto, D.; Carell, T.; Tudek, B.; Castaing, B. (2014)
Zinc finger oxidation of Fpg/Nei DNA glycosylases by 2-thioxanthine: biochemical and X-ray
structural characterization. Nucleic Acids Res. 42(16), 10748-10761.
Jacobs, A.C.; Calkins, M.J.; Jadhav, A.; Dorjsuren, D.; Maloney D.; Simeonov, A.; Jaruga, P.;
Dizdaroglu, M.; McCullough, A.K.; Lloyd, R.S. (2013)
Inhibition of DNA glycosylases via small molecule purine analogs. PLoS One. 8(12), e81667
110
Poster P1
Spectroscopic Fingerprints of the Structural
Fluctuations of a Single Amino Acid
Fatima Barakat
1
∗ 1
, Patrice Delarue† 1 , Patrick Senet‡
1
Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB) – CNRS : UMR6303, Université de
Bourgogne – 9 avenue Alain Savary, BP 47870, 21078 Dijon Cedex, France
Thanks to the nanoscience revolution, nowadays it is possible to study a single amino-acid
experimentally by using the Surface Enhanced Raman Spectroscopy. In contrast to bulk Raman
measurements that average over an ensemble of molecules, single-molecule spectroscopy on a
single amino acid produces a set of high-resolution heterogeneous spectra. Several reasons might
be the cause of the heterogeneity of the spectra as the experimental noise, the amino-acid interactions with the SERS nanodevice and the (intrinsic) conformational fluctuations of the amino
acid. To examine the latter hypothesis from a theoretical point of view, classical infrared spectra
as well as Raman spectra were computed for blocked hydrated amino-acids. One thousand conformations of hydrated amino-acids were selected every 100 ps from classical molecular dynamics
trajectories of 100 ns of duration at 300 K in explicit solvent (water). Normal mode analysis
was performed for every conformation and the infrared and Raman spectra were computed. The
calculations produce heterogeneous ensembles of infrared and Raman spectra, similar to those
measured in SERS. The fluctuations of the spectra relative to the average spectrum were studied
by using principal component analysis. The role of the hydration on the fluctuations was clearly
identified. A correlation between the amino-acid conformational fluctuations and the fluctuations of the spectra was rationalized by calculating an effective local force constant on each of the
atom. The present work strongly supports that the origin of the time-dependent fluctuations of
the Raman spectra observed in SERS are mainly due to the multiple minima of the free-energy
surface of a single amino acid in solution.
∗
Intervenant
‡
†
111
Poster P1
Unravelling the structure/activity relationship
between collagen derived peptides and the v3
integrin
Eléonore Lambert 1 , Manuel Dauchez 1,2 , Sylvie Pasco-Brassart 1 ,
Stéphanie Baud ∗† 1,2
1
UMR CNRS/URCA 7369 (MEDyC), Reims (1) – Université de Reims - Champagne Ardenne, CNRS :
UMR7369 – Moulin de la Housse, 51687 Reims Cedex 2, France
2
Plateau de Modélisation Moléculaire Multiéchelle (P3M), Reims (2) – Université de Reims Champagne Ardenne, CNRS : UMR7369 – Moulin de la Housse, 51687 Reims Cedex 2, France
The Extra Cellular Matrix (ECM) is a connective tissue formed by a complex network of
macromolecules. Upon degradation by Matrix MetalloProteinases (MMP), elastases or collagenases, the ECM releases bio-active fragments named matrikines. In particular, the NC1 domain
of the a4 chain of collagen IV, named tetrastatin was demonstrated to inhibit in vitro melanoma
cell proliferation and invasion and in vivo tumor growth through V3 integrinbinding. The interaction with the integrin V3 was demonstrated by Surface Plasmon Resonance [1].
The aim of the present in silico study is to gain insight into the molecular and atomistic aspects
of the interaction between tetrastatin-derived peptides and the V3 integrin. Since it was possible
to identify the tetrastatin minimal active sequence QKISRCQVCVKYS (corresponding to the
peptide named QS-13), we are characterizing V3/QS-13 interactions using multiple molecular
docking experiments and statistical analyses.
We are then able to identify five “preferred areas of interactions” as well as the key residues
implicated in the interactions. In addition, our results demonstrate the important role played
by the presence of a disulfide bridge in QS-13 peptide.
1. Brassart-Pasco, S., Sénéchal, K., Thevenard, J., Ramont, L., Devy, J., Di Stefano, L., DupontDeshorgue, A., Brézillon, S., Feru, J., Jazeron, J. F., Diebold, M. D., Ricard-Blum, S., Maquart,
F. X., and Monboisse, J. C. (2012). Tetrastatin, the nc1 domain of the 4(iv) collagen chain: a
novel potent anti-tumor matrikine. PLoS One, 7(4)
∗
†
Intervenant
112
Poster P
Mécanisme d’activation du récepteur
T1R2-T1R3 impliqué dans la perception du
goût sucré.
Jean-Baptiste Cheron
∗ 1
, Jérôme Golebiowski 1 , Serge Antonczak 1 ,
Sébastien Fiorucci† 1
1
Institut de Chimie de Nice (ICN) – CNRS : UMR7272, Université Nice Sophia Antipolis [UNS],
La stimulation chimique du récepteur T1R2-T1R3 est à l’origine de la perception du goût
sucré. Ce Récepteur Couplé aux Protéines G (RCPG) de classe C est un hétérodimère caractérisé par un grand domaine N-terminal appelé Venus Flytrap Domain (VFD) lié au domaine à
7 segments transmembranaire (7TM) par un domaine riche en cystéines (CRD). Il a été montré
que les molécules influençant l’activité du récepteur interagissent avec les différents domaines [1]
: les agonistes naturels lient le site orthosterique localisé dans le VFD de T1R2, les modulateurs
allostériques interagissent avec le domaine à 7TM de T1R3 et les protéines à pouvoir sucrant
lient le CRD de T1R3. Malgré la connaissance de ces sites de liaison, les mécanismes moléculaires
impliqués dans la transmission du signal d’activation du VFD de T1R2 à la partie cytosolique
du domaine à 7TM reste à élucider. Dans cette perspective d’étude in silico, l’obtention de la
structure 3D du récepteur est nécessaire pour caractériser la relation structure-activité impliquée
dans la perception du goût sucré et in fine définir de nouveaux édulcorants. L’absence de données structurales sur le récepteur T1R2-T1R3 requière une étape de modélisation par homologie
à l’aide de contraintes expérimentales : i) les données cristallographiques disponibles du récepteur métabotropique au glutamate (RCPG de classe C) [2,3] ii) les régions d’interactions des
domaines à 7TM [4] iii) les résidus clés définis par mutagenèses dirigées [5,6]. Les simulations de
dynamiques moléculaires et les calculs de modes normaux sont utilisés pour décrire les processus
moléculaires impliqués dans la liaison des ligands et le mécanisme d’activation du récepteur.
Références:
Behrens M. et al., Angew. Chem. Int. Ed, 2011, 50, 2220-2242.
Wu H. et al., Science, 2014, 344 (6179), 58-64.
Doré A.S. et al., Nature, 2014, 511, 557–562.
Xue L. et al., Nat. Chem. Biol., 2015, 11, 134-140.
Zhang F. et al., PNAS, 2010, 107(10), 4752-4757.
∗
†
Intervenant
113
Poster P
Caractérisation du paysage énergétique de
biomolécules flexibles avec un couplage
d’algorithmes stochastiques
Juan Cortes
1
∗ 1
LAAS-CNRS (LAAS-CNRS) – CNRS : UPR8001 – France
La caractérisation du paysage énergétique de biomolécules flexibles est essentielle pour l’analyse
de ses propriétés physico-chimiques et ses fonctions biologiques. Les peptides sont un cas d’étude
particulièrement intéressant, car ces molécules exploitent leur flexibilité structurale pour moduler leur fonction. Malgré leur petite taille (par rapport aux protéines), la modélisation des
peptides reste un sujet difficile à cause de l’existence d’une multitude d’états métastables et de
transitions possibles entre eux. Nous proposons une approche basée sur le couplage d’algorithmes
stochastiques. Premièrement, une version simple de l’algorithme Basin Hopping est utilisée pour
échantillonner des régions de basse énergie de manière à identifier un ensemble d’états métastables. Ensuite, plusieurs variantes d’un algorithme issu de la robotique, appelé Transition-based
Rapidly-exploring Random Tree (T-RRT), sont proposées pour calculer de manière efficace les
chemins de transition entre ces états. L’approche est illustrée sur la met-enképhaline.
∗
Intervenant
114
Poster 3
Modeling methods for protein structure
prediction, mechanistic and docking studies
Serge Crouzy
1
∗† 1
Laboratoire de Chimie et Biologie des Métaux (LCBM) – CEA, Université Joseph Fourier - Grenoble
I, CNRS : UMR5249 – 17, rue des Martyrs 38054 GRENOBLE CEDEX 9, France
Nous nous efforçons, dans notre petit groupe, de répondre à des problèmes expérimentaux
par des modèles théoriques et des simulations. Il s’agit soit de modèles structuraux basés sur
la mécanique ou la dynamique moléculaire, soit de mécanismes réactionnels basés sur les calculs
d’énergie libre ou la chimie thèorique. Depuis les modèles “ gros-grain ” jusqu’à une description
électronique, nos modèles couvrent une large gamme de temps (ps à ms) et d’espace (nm à µm).
Nous présentons brièvement cinq résultats récents couvrant ces domaines à l’exception du niveau
électronique.
1- La structure X de NccX, un senseur transmembranaire de métal, a été déterminée à une résolution de 3,12 Å en détergent. Cette structure ne correspond pas au modèle basé sur le domaine
périplasmique largement caractérisé de son homologue le plus proche CnrX. Des simulations de
dynamique moléculaire “ gros-grain ” de NccX immergée dans une bicouche de phospholipides
modèles ont permis d’isoler le segment transmembranaire de la protéine malencontreusement
attaché à une région hydrophobe dans la structure en détergent.2- Les Récepteurs Couplés aux
Canaux Ioniques (ICCR) sont des protéines artificielles construites à partir d’un récepteur couplé à la protéine G et d’un canal ionique, utilisés comme biocapteurs moléculaires. Le canal
potassique rectifiant entrant Kir6.2 a ainsi été couplé avec succès au récepteur muscarinique M2
ou dopaminergique D2. Le mécanisme moléculaire expliquant la différence de comportement
(courant ionique à travers le canal) entre les 2 ICCR après liaison de leur activateur respectif reste à élucider. En particulier, il était important de déterminer comment l’activation de
l’acétylcholine M2 par son agoniste déclenche la modulation du canal Kir6.2 via la liaison M2Kir6.2. Nous avons développé et validé une approche computationnelle permettant de construire
des modèles de M2-Kir6.2 qui a produit deux modèles possibles correspondant à deux orientations
différentes de M2. 3- La protéine FUR (régulateur de l’absorption ferrique) est un régulateur
transcriptionnel global spécifique des bactéries. C’est une cible de choix pour le développement
de molécules visant à réduire la virulence des bactéries sans développer des résistances. Des peptides anti-FUR de 13 acides aminés ont été identifiés, capables d’interagir avec FUR d’Escherichia
coliet ainsi inhiber sa liaison à l’ADN conduisant à sa pathogénicité. Une combinaison originale
d’approches théoriques (“ docking ” et DM) et expérimentales a été utilisée pour étudier le mécanisme d’inhibition de FUR par ces peptides.4- L’ATPase à cuivre humaine ATP7A (Menkès)
est essentielle pour l’homéostasie intracellulaire du cuivre. Le domaine N-terminal de la protéine
se compose de six séquences répétitives de 60-70 acides aminés (Mnk1-Mnk6) qui se replient
en domaines de liaison métallique individuels (MBD) liant un ion Cu(I) grâce à deux résidus
cystéine. La structure de chaque MBD est connue par RMN et nous nous sommes intéressés
∗
†
Intervenant
115
Poster P
à la stabilité et la dynamique de chaque MBD isolé dans sa forme apo et holo ainsi qu’à leurs
interactions avec la métallochaperone soluble Hah1 qui délivre le cuivre à ATP7A. Nous avons
paramétrisé le métal dans ces protéines et réalisé des simulations de DM dans ce but.5- Les
interactions entre la -cyclodextrine, (BCD) et le chlorure de p-toluènesulfonyl (TsCl) ont été
étudiées en utilisant des simulations de DM, dans le vide, se rapprochant de l’environnement de
la cavité hydrophobe de la CD, et en présence d’eau. Dans les deux cas, les chemins d’énergie
adiabatique minimale, et les potentiels de force moyenne (profils d’énergie libre) pour l’insertion
des TsCl le long d’une coordonnée de réaction perpendiculaire au plan de la CD, ont été calculés
pour les deux orientations possibles de TsCl. Les résultats montrent une entrée préférentielle de
TsCl dans la cavité de CD par la face à hydroxyles primaires en excellent accord avec les données
de RMN.
1- Ziani W, Maillard AP, Petit-Härtlein I, Garnier N, Crouzy S, Girard E. and Covès J. J
Biol Chem. 289 31160-31172 (2014).
2- Sapay N., Estrada A., Moreau C., Vivaudou M. and Crouzy S. Proteins 82 1694-1707 (2014)
3- Cissé C., Mathieu S.V., Abeih M.B., Flanagan L., Vitale S., Catty P., Boturyn D., MichaudSoret I.. and Crouzy S. ACS Chem Biol . 9 2779-2786 (2014)
4- Arumugam K. and Crouzy S. Biochemistry. 51 8885-8906 (2012)
5- Law H., Benito J.M., Garcia Fernandez J.M., Jicsinszky J., Crouzy S. and Defaye J. J Phys
Chem B. 115, 7524-7532. (2011)
116
Poster P
Mitochondrial membrane fusion:
computational modelling of mitofusins
Dario De Vecchis
1
∗† 1
, Antoine Taly 1 , Marc Baaden 1 , Jérôme Hénin
1
Institut de biologie physico-chimique (IBPC) – CNRS : FRC550 – 13 Rue Pierre et Marie Curie 75005
PARIS, France
Mitochondria are highly dynamic organelles that continuously alter their shape, ranging
from a filamentous network to a collection of isolated fragments. This process is controlled
by fusion-fission equilibrium which is a largely unexplored topic despite its importance in cell
function, evolution and ageing. Fusion of mitochondrial outer membranes (MOM) involves mitofusins, large GTPases of the Dynamin-Related Proteins (DRPs) superfamily, one of which is
Fzo1. Although several hypotheses have been proposed, many aspects of Fzo1 function are yet
debated, and many important questions remain unanswered. Recent data indicate that the
GTPase domain of Fzo1 would induce a conformational switch from a closed to an open state
concomitant with mitochondrial tethering. Such a conformational switch resembles that of the
bacterial dynamin-like protein (BDLP), a close relative of Fzo1, whose structure is known in two
conformational states: binding of GTP analogues by BDLP results in a switch from a compact
structure, seen with bound GDP, to an open conformation that stimulates BDLP oligomerization
and favours its ability to shape the morphology of liposomes. BDLP structural properties and
Fzo1 behaviour during MOM fusion suggest that mitofusins may promote membrane fusion by
switching from closed to open conformation, although the molecular fusion mechanism remains
unknown. In this project we use homology modelling approach and atomistic simulations in
close link to experiment to study Fzo1 based on the open and closed BDLP crystal structures.
The refined models may generate hypotheses for the conformational transition between open and
closed forms and predict the phenotype of interesting mutations to be tested and performed by
Mickael Cohen lab, as a part of the collaboration. Since sequence and function of mitofusins are
conserved throughout evolution, our approach may unravel novel functional domains and provide fundamental insights about molecular mechanisms by which mitofusins and more generally
DRPs, catalyse membrane fusion.
∗
†
Intervenant
117
Poster P
UnityMol: Simulation et Visualisation
Interactive à des fins d’Enseignement et de
Recherche
Sébastien Doutreligne ∗ 1 , Cédric Gageat 1 , Tristan Cragnolini 1 , Antoine
Taly 1 , Samuela Pasquali 1 , Marc Baaden 1 , Philippe Derreumaux 1
1
Laboratoire de biochimie théorique (LBT) – CNRS : UPR9080, Université Paris VII - Paris Diderot –
13 Rue Pierre et Marie Curie 75005 PARIS, France
Nous présentons UnityMol, une base logicielle pour développer des solutions de visualisation,
analyse et exploration de données biologiques. Une des originalités de UnityMol est son implémentation à travers un moteur de jeu vidéo. Nous venons de connecter ce logiciel de visualisation
à des simulations de dynamique moléculaire pour interagir directement avec la molécule d’intérêt.
Cette manipulation apporte une nouvelle dimension très concrète et intuitive à l’exploration de
la structure des biomolécules. Ici nous présentons nos essais sur les molécules d’ARN. Nous
utilisons un moteur de simulation qui implémente le modèle gros-grain HiRE-RNA développé
au laboratoire. Une des applications possibles est l’enseignement. A présent, UnityMol a été
utilisé avec des groupes d’étudiants de Licence en Biologie et en Bioinformatique, soit un total
de près de 100 personnes. Ces expériences créent un contact concret avec les simulations et
leur fonctionnement et nous permettent de tester et valider nos choix techniques pour ensuite
proposer un espace d’expérimentation virtuel pour la recherche.
∗
Intervenant
118
Poster P
Study of TIMP-1/LRP-1 interaction in neurons: a new
approach based on protein dynamic
Laurie Verzeaux *1, Nicolas Belloy
2, 3
, Hervé Emonard 1, Manuel Dauchez
2, 3
, Laurent Martiny 1,
Nicolas Etique 1 and Emmanuelle Charpentier 1
1 – Unité MeDyC UMR CNRS URCA 7369, Equipe “Protéolyse et cancer”, Reims
2 – Unité MeDyC UMR CNRS URCA 7369, Equipe “Vieillissement matriciel et Remodelage vasculaire”, Reims
3 – Plateau de Modélisation Moléculaire Multi-échelle (P3M), Maison de la Simulation de Champagne-Ardenne
(MaSCA), Reims
TIMP-1, a member of the Tissue Inhibitor of Metalloproteinases (TIMPs) family, has been first
characterized for its ability to inhibit Matrix Metalloproteinases (MMPs) and can also act as a cytokine by
binding to cell surface complex receptors. In mouse cortical neurons, we have recently identified the LDL
receptor-related protein-1 (LRP-1) as a new receptor for TIMP-1. The binding of TIMP-1 to LRP-1
reduces neurite outgrowth and modulates growth cone morphology. The characterization of TIMP-1/LRP1 interaction could help to develop new therapeutic strategies in neurodegenerative diseases. Whether the
TIMP1 structure is well characterized, the lack of structural data about LRP1 doesn’t allow the use of
molecular docking algorithms. Based on the observation that ligand/receptor interaction requires
conformational changes of both partners, we have hypothesized that understanding the intrinsic dynamic
of TIMP-1 could help us to highlight regions that govern its interaction with LRP-1. Normal Modes
Analysis (NMA) and/or Molecular Dynamics (MD) simulations are usefulness in silico methods to study
protein dynamics. In this study, we used these theoretical methods in conjunction with experimental data
to identify residues or regions that might be involved in TIMP-1/LRP-1 interaction and signaling.
Normal Mode Analysis led us to identify a movement of the N-terminal domain relative to the
C-terminal domain which could be assimilated to a clamp movement. In these modes, study of residues
deformation energy has highlighted a cavity which could govern this movement and potentially its
interaction with LRP-1. Molecular dynamic studies of TIMP-1 and particularly carbon α atomic
fluctuation representation have also designed this cavity as essential for the TIMP-1 intrinsic dynamic. In
this area, three residues have been pointed out and mutated into alanine in order to disturb the movement.
The three single mutants have been produced and purified by affinity chromatography. Determination of
both Ki (inhibitory constant) and Kon (association constant) revealed that the three mutants conserved
their inhibitory effects towards MMP-1, -2, -3 and -9. As expected, the three single mutants are not able
to reduce neurite length in mouse cortical neurons. Surprisingly, surface plasmon resonance analyses
show that the three mutants are able to bind LRP-1. However, the LRP-1-mediated endocytosis, evaluated
by confocal microscopy, is altered for the three mutants.
In this study, we have identified a TIMP-1 region essential for LRP-1-mediated endocytosis and
associated biological effects in neurons. Our findings could help to design molecules that specifically
block the cytokine-like effects of TIMP-1 through LRP-1 and open new perspectives in
119 Communication par affiche
Poster P
neurodegenerative disease therapy. This work also suggests the utility of our in silico approach in other
topics where there is a lack of structural data for studying ligand-receptor interactions.
____________________________
* Intervenant
120 Communication par affiche
Poster P
SA-conf : un outil d’analyse et de comparaison
de la variabilité de séquences et de structures
d’un ensemble de conformères d’une protéine
Leslie Regad 1 , Jean-Baptiste Cheron 2 , Caroline Senac 3 , Delphine Flatters
∗ 1
, Anne-Claude Camproux 1
1
Recherche de molécules à visée thérapeutique par approches In Silico (MTI) – Inserm : U973,
Université Paris VII - Paris Diderot – Batiment Lamarck A (case 7113), 35 rue Hélène Brion 75205
PARIS CEDEX 13, France
2
Institut de Chimie de Nice (UMR 7272 CNRS) – CNRS : UMR7272, Université Nice Sophia Antipolis
– Parc Valrose, 06108 Nice Cedex 2, France
3
Laboratoire d’imagerie biomédicale – CNRS : UMR7371, Inserm : UMRS1146, Université Paris VI Pierre et Marie Curie – Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire
d’imagerie biomédicale, F-75006 Paris, France
Actuellement, plus de 100.000 structures protéiques sont disponibles dans la Protein Data
Bank (PDB). La PDB inclut souvent plusieurs conformères correspondant à une même protéine obtenue dans différentes conditions expérimentales, par exemple par cristallographie ou
RMN, complexée ou non avec un partenaire (protéine, un ligand, un ADN/ARN), sous une
forme mutante ou sauvage. Ces différents conformères présentent des variations structurales
subtiles pouvant illustrer la “ plasticité ” de la protéine. A l’heure actuelle, il n’existe pas d’outil
disponible d’analyse fine, systématique et rapide d’un ensemble de conformères. Or cette analyse permettrait (i) d’identifier les régions structurellement variables pouvant être associées à la
plasticité d’une protéine, (ii) d’aider à la détermination des régions importantes pour la fonction
biologique de la protéine.
Dans ce contexte, nous avons développé un outil, nommé SA-conf, permettant d’étudier et de
comparer un ensemble de conformères d’une protéine et analyser leur diversité de séquences et de
structure. Tout d’abord, SA-conf offre une description de chaque conformère en termes de méthode expérimentale, de résolution, de nombre et taille de(s) chaîne(s) le composant. Puis, SA-conf
construit un alignement multiple des séquences (AMS) extraites des différents conformères. Afin
de proposer une comparaison entre la variabilité des séquences et de structures locales des structures, SA-conf construit un alignement des structures locales extraites des différents conformères.
L’extraction des structures locales est basée sur un l’alphabet structural HMM-SA (Camproux
et al., 2004) qui permet de simplifier chaque conformère en une séquence de lettres structurales
(LS) où chaque LS correspond à la géométrie d’un fragment de 4 résidus, i.e. structure locale du
résidu en prenant en compte son environnement. Chaque acide aminé du AMS est ensuite remplacé par la LS correspondante produisant ainsi l’alignement des conformations locales (ACL).
L’analyse du AMS permet de caractériser les séquences du jeu de conformères en identifiant les
séquences mutantes, celles qui contiennent des insertions/délétions ou des régions non résolues
et de différencier les séquences provenant de différents organismes. L’analyse des ACL permet
∗
Intervenant
121
Poster P
d’identifier les conformères ayant des particularités structurales, les régions et positions structurellement variables, i.e positions pour lesquelles plusieurs conformations locales sont observées
sur ACL. Cette variabilité structurale des conformères peut alors être analyser/interpréter en
fonction des différentes conditions expérimentales, comme par exemple la présence d’un partenaire (ligands, protéines, ...), la présence de mutations, .... De plus, SA-conf produit des représentations graphiques qui permettent de localiser ces positions variables et conservées sur la structure
tri-dimensionnelle de la protéine.
En conclusion SA-conf est un outil rapide d’analyse et de comparaison d’un ensemble des conformères d’une protéine, en termes de séquence et structure. SA-conf permet à l’utilisateur
d’identifier les séquences mutées, et de différencier les séquences provenant de différents organismes. SA-conf est aussi un outil efficace pour étudier la diversité structurale locale des différents
conformères d’une protéine ce qui permet une première analyse rapide de la plasticité de cette
protéine. L’analyse conjointe des conformères obtenus dans différentes conditions expérimentales
et des régions variables de la protéine permet d’apporter des premiers éléments de réponse sur les
raisons expliquant cette plasticité : flexibilité intrinsèque, mutations, fixation d’un partenaire,
et ainsi d’identifier des régions importantes pour la fonction biologique de la protéine.
122
Poster P
Exploration of Large Amplitude Motions of
GPCRs and their complexes at the molecular
level
Nicolas Floquet ∗ 1 , Mauricio Costa 2 , Marjorie Damian 1 , Sophie Mary 1 ,
Jean-Alain Fehrentz 1 , Jacky Marie 1 , Jean Martinez 1 , Jean-Louis Banarès
1
, David Perahia 3
1
Institut des Biomolécules Max Mousseron (IBMM) – CNRS : UMR5247, Université de Montpellier –
Faculté de Pharmacie, 15 Av. Charles Flahault, BP 14491, 34093 Montpellier, Cedex 05, France
2
Programa de Computação Científica, Fundação Oswaldo Cruz, 21949900, Rio de Janeiro, Brazil. –
Brésil
3
Laboratoire de Biotechnologie et Pharmacologie génétique Appliquée (LBPA) – CNRS : UMR8113,
École normale supérieure (ENS) - Cachan – 61 AVENUE DU PRESIDENT WILSON 94235 CACHAN
CEDEX, France
Class-A G-Proteins Coupled Receptors (GPCRs) and G-Proteins together form protein:protein
complexes that mediate a large panel of physiological responses, and therefore constitute attractive targets for the design of new drugs. The number of available X-ray structures describing
either isolated, or complexed GPCRs has considerably increased since 2007. However, and despite
of this, the crucial question of the activation mechanism of these large protein:protein complexes
at the molecular level is still controversial. In the literature, the conformational change observed
between the isolated and the G-protein complexed receptor is still described as the holy-grail.
However, many experimental data have now evidenced the existence of G-Proteins pre-coupled
GPCRs, suggesting that the activation mechanism is a key feature of the complex and not of the
isolated, uncoupled, partners. In agreement, beyond frozen X-ray data, a better understanding of
the dynamical behavior of these large molecular assemblies is of crucial importance for the design
of more specific drugs, with more controlled effects. In a recent paper, we used Normal Modes
Analyses in vacuo, to show that the conformational diversity of GPCRs is considerably reduced
upon complexation to G-proteins [1]. These results even pointed to a unique motion possibly
being the activation motion of GPCRs in the complex. Here we used the “Molecular Dynamics
with Excited Normal Modes” (MDeNM) method to explore such a large amplitude motion of the
complex with an explicit representation of the surrounding medium (membrane+water). Among
the lowest frequencies Normal Modes of the complex, one motion was in particularly good agreement with experimental results obtained in our laboratory on the Ghrelin Receptor/Gq model
and showing the increase/decrease of up to 10 Angströms of both intra- and inter-molecular
distances in the protein:protein complex [2]. These data provide direct evidence of a mechanism
in which transition of the receptor from an inactive to an active conformation is accompanied by
a large conformational rearrangement of a preassembled receptor: G protein complex, ultimately
leading to G protein activation and signaling.
1. Louet M, Karakas E, Perret A, Perahia D, Martinez J, and Floquet N. (2013) Conformational
∗
Intervenant
123
Poster P
restriction of G-proteins Coupled Receptors (GPCRs) upon complexation to G-proteins: a putative activation mode of GPCRs? FEBS Lett. 587(16):2656-61.
2. Damian M, Mary S, Maingot M, M’Kadmi C, Gagne D, Leyris JP, Denoyelle S, Gaibelet G,
Gavara L, Garcia de Souza Costa M, Perahia D, Trinquet E, Mouillac B, Galandrin S, Galès C,
Fehrentz JA, Floquet N, Martinez J, Marie J, Banères JL. (2015) Ghrelin receptor conformational dynamics regulate the transition from a preassembled to an active receptor:Gq complex.
Proc Natl Acad Sci U S A. 112(5):1601-6.
124
Poster P
HiRE-RNA: un modèle gros grain pour la
simulation d’ARN et d’ADN
Cédric Gageat
1
∗ 1
, Samuela Pasquali 1 , Philippe Derreumaux
1
Laboratoire de biochimie théorique (LBT) – CNRS : UPR9080, Université Paris VII - Paris Diderot –
13 Rue Pierre et Marie Curie 75005 PARIS, France
Malgré que l’ARN joue de nombreux rôles au sein des cellules, nous disposons de très peu
de structures 3D hautes définitions. Du fait de la grande flexibilité des ARN, l’obtention de
structures par RMN ou rayon X reste un défi actuel. Des modèles issus de prédictions théoriques
sont donc proposés afin de combler le manque de données expérimentales. Malheureusement, les
modèles tout-atome, sont eux aussi limités par les puissances de calcul actuelles, ce qui permet
uniquement l’étude de petits systèmes.
Pour permettre l’étude de plus gros systèmes d’intérêts biologiques, un modèle gros grain haute
résolution, HiRE-RNA [1, 2], a été développé dans notre laboratoire. Ce modèle a été testé et
validé en couplage avec différentes techniques d’échantillonnage de conformations (Dynamique
moléculaire simple, avec échange de répliques, interactive, ...). Nous présentons ici notre modèle
et ses termes énergétiques au travers de ses deux spécialisations : la première pour l’ARN, la
seconde pour l’ADN. Nous présentons également l’étude d’une large molécule d’ADN (178 nucléotides) [3] ainsi que l’étude de petites molécules à la géométrie complexe : des quadruplexes.
Cragnolini T, Laurin Y, Derreumaux P, Pasquali S, The coarse-grained HiRE-RNA model for
de novo calculations of RNA free energy surfaces, fodling pathways and complex structure prediction. Submitted
Cragnolini T, Derreumaux P, Pasquali S, Coarse-grained simulations of RNA and DNA duplexes.
J Phys Chem B. 2013 ;117(27):8047-60
Kanevsky I, Chaminade F, Chen Y, Godet J, René B, Darlix J-L, Mély Y, Mauffret O, Fossé
P, Structural determinants of TAR RNA-DNA annealing in the absence and presence of HIV-1
nucleocapsid protein. Nucl. Acids Res. 2011;39 (18):8148-8162.
∗
Intervenant
125
Poster P
PEPSI-SAXS - A very fast adaptive scheme for
computation of small-angle X-ray scattering
profiles via spherical harmonics expansions
Sergei Grudinin
∗† 1
, Maria Garkavenko 2 , Andrei Kazennov 2 , Team
Nano-D 1
1
NANO-D (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann) – Institut
polytechnique de Grenoble (Grenoble INP), Université Joseph Fourier - Grenoble I, CNRS : UMR5224,
Laboratoire Jean Kuntzmann, INRIA – Inria GIANT DRT/LETI/DACLE Batiment 51C - Minatec
Campus 17 rue des Martyrs 38054 Grenoble Cedex, France
2
Moscow Institute of Physics and Technology (MIPT) – 141700, 9, Institutskii per., Dolgoprudny,
Moscow Region, Russia, Russie
Small angle scattering is an important tool in structural biology. It allow to investigate the
three-dimensional structures and structural changes of biological objects. Thanks to recent advances in experimental methods, the amount of experimental small-angle X-ray scattering data is
increasing dramatically [1,2]. Therefore, there is an urgent need for very efficient computational
tools to analyze this data.
We present PEPSI-SAXS, a new method that uses spherical harmonics expansions, adaptive
resolution, and smooth interpolation of scattering amplitudes in the reciprocal space to rapidly
compute solution scattering of macromolecules starting from their known atomic structure. The
method can either predict the intensity curve, or to fit the experimental scattering curve using
two fitting parameters, the averaged displaced volume and the contrast of the hydration layer.
Additionally, our method can automatically subtract the constant background noise from experimental curves, which improves the fitting result.
Compared to other state-of-the-art methods such as Crysol [3], FoXS [4], and SAStbx [5], PEPSISAXS uses the adaptive resolution of its expansion coefficients and a very accurate model of the
solvation shell to improve the quality of the predicted scattering curve. Additionally, fast summations based on the evaluation of the partial intensities and shared-memory parallelization
allows PEPSI-SAXS to significantly out-perform the other methods in computational efficiency.
More precisely, on a modern laptop our method runs in 5-50 times faster compared to the other
tested methods.
The PEPSI-SAXS method was exhaustively tested on all available structural models from the
BIOISIS [4] and SASDB [5] databases. Table 1 provides comparison of the four tested methods
listing the average timings and quality of fit to experimental curves for 28 models from BIOISIS
[1] and 23 models from SASDB [2]. As can be seen from the table, PEPSI-SAXS significantly
outperforms all other methods both in speed and model quality. We have also developed a
graphical user interface for the PEPSI-SAXS method as a module for the SAMSON modular
∗
†
Intervenant
126
Poster P
software package developed in our team (see Figure 1), which is available at http://www.samsonconnect.net.
http://www.bioisis.net.
Erica Valentini, Alexey G Kikhney, Gianpietro Previtali, Cy M Jeffries, and Dmitri I Svergun.
Sasbdb, a repository for biological small-angle scattering data. Nucleic acids research, pages
D357–63, 2015.
D Svergun, C Barberato, and MHJ Koch. Crysol-a program to evaluate x-ray solution scattering of biological macromolecules from atomic coordinates. Journal of applied crystallography,
28(6):768–773, 1995.
Dina Schneidman-Duhovny, Michal Hammel, John A Tainer, and Andrej Sali. Accurate saxs
profile computation and its assessment by contrast variation experiments. Biophysical journal,
105(4):962–974, 2013.
Haiguang Liu, Richard J Morris, Alexander Hexemer, Scott Grandison, and Peter H Zwart.
Computation of small-angle scattering profiles with three-dimensional zernike polynomials. Acta
Crystallographica Section A: Foundations of Crystallography, 68(2):278–285, 2012.
127
Poster P
Conformational
changesinin
themophilic
Conformational changes
thermophilic
andand
mesophilic
enymes
mesophilic
enzymes
Marina Katava
∗ 1
, Fabio Sterpone
∗
1
1
Laboratoire de Biochimie Théorique (LBT) – CNRS : UPR9080, Université Paris Diderot - Paris 7 –
13, Rue Pierre et Marie Curie - 75005 PARIS, France
We study the conformational changes that occur during the enzymatic activity of two homologous proteins from the mesophile E. coli (optimum growth at 37◦ ) and the thermophile S.
solfataricus (optimum growth at 87◦ ). The enzymatic activity is energized by GTP to GDP
conversion and is a necessary part of the polypeptide elongation process.
In the mesophile protein, activity is granted by a dramatic secondary structure to conversion occurring in a conserved region (switch I), and following the GTP/GDP catalysis [1]. This
region is the most thermally unstable region of the protein [2]. As no to conversion has been
observed in the thermophilic EF-1, we are interested in the conformational changes occurring in
the analogous portion as they will help us identify the differences between the protein molecular
mechanisms in the meso- and the thermophile.
We mimicked the conformational changes induced by the enzymatic reactions by performing
Molecular Dynamics simulations of the g-domains in different secondary structures bound to the
reactant (GTP) and product state (GDP). Results show that the protein matrix is rigid in the
/GTP form, and that it becomes more flexible in the /GDP form, which could be the trigger the
transition to the state. The thermophile shows similar behavior.
The conformational changes in the switch I fragment will be better explored by means of enhanced sampling techniques.
Jurnak et al., Structure, 1996.
Sterpone et al., J. Phys. Chem. B, 2013.
∗
Intervenant
128
Poster P
Etude de la variabilité structurale et de
l’interaction du peptide NFL-TBS.40-63 avec
la tubuline.
Yoann Laurin
∗ 1
, Philippe Savarin 2 , Charles Robert 1 , Chantal Prevost 1 ,
Sophie Sacquin-Mora 1
1
Laboratoire Biochimie Théorique CNRS UPR9080 (LBT) – CNRS : UPR9080 – IBPC, 13 rue Pierre
et Marie Curie, 75005 Paris, France
2
Université Paris 13, Sorbonne Paris Cité, CSPBAT, CNRS UMR7244 – CNRS : UMR7244 – 74 rue
Marcel Cachin, 93017 Bobigny, France
NFL-TBS.40-63 is a 24 amino acid peptide corresponding to the tubulin-binding site located
on the light neurofilament subunit, which selectively enters glioblastoma cells, where it disrupts
their microtubule network and inhibits their proliferation. We investigated its structural variability and binding modes on a tubulin hetero-dimer using a combination of NMR experiments,
docking and molecular dynamics (MD) simulations. Our results show that, while lacking a stable structure, the peptide preferentially binds on a specific single site located near the -tubulin
C-terminal end, thus giving us precious hints regarding the mechanism of action of the NFLTBS.40-63 peptide’s antimitotic activity on the molecular level. Le peptide de 24 acides aminés
NFL-TBS.40-63 est un Tubulin-Binding Site (TBS, site de liaison à la tubuline) localisé sur le
neurofilament léger. Il pénètre spécifiquement dans les cellules des glioblastomes, où il va inhiber la formation du réseau de microtubules. Nous avons étudié sa variabilité structurale et
son interaction avec un hétérodimère de tubuline grâce à des expériences de RMN, de docking et
des simulations de dynamique moléculaire. Nos résultats montrent que, malgré l’absence d’une
structure stable, le peptide interagit préférentiellement sur un site spécifique situé aux abords
de l’extrémité C-terminale de la sous unité de la tubuline. Ces observations sont de précieux
indices afin de comprendre le mécanisme de l’action antimitotique du peptide NFL-TBS.40-63
au niveau moléculaire.
∗
Intervenant
129
Poster P
Molecular basis of Olfactory Receptors: in
silico modeling and in vitro validation.
Gwenaëlle Andre-Leroux
∗† 1
1
André-Leroux (Inra MaIAGE) – Institut National de la Recherche Agronomique - INRA (FRANCE)
– Unité Mathématiques et Informatique Appliquées, du Génome à l’Environnement (MaIAGE) INRA Bat233 Domaine de Vilvert - 78352 Jouy-en-Josas, France
Adrien Aquistapace1, Véronique Martin2, Jean-François Gibrat2, Guenhaël Sanz1 and Gwenaëlle André-Leroux2
1Inra, NBO UR1197, Bât 440 & 2Inra, MaIAGE UR1404, Bât 233, Domaine de Vilvert, 78352
Jouy-en-Josas
[email protected]
G protein-coupled receptors (GPCRs) are a highly versatile family of membrane proteins with
a broad spectrum of functional behaviours in response to diffusible ligands. Upon activation by
external stimuli, which can be photons, odors, pheromones, hormones and neurotransmitters,
GPCRs trigger specific cellular responses and recruit downstream activators such as G proteins.
The human genome accounts for as much as 3% of GPCR genes which associate with numerous
biological sensing functions including olfaction, vision, taste, pain, etc. Noteworthy, most of the
human GPCRs are olfactory receptors (ORs), responsible for olfaction, expressed in the olfactory epithelium where they signal diverse odorants [1-2]. Additionally, ORs is employed to detect
steroid hormones in non-olfactory tissues, regulate serotonin secretion in the gut, cell migration
or adhesion in muscle [3-6].
Deciphering how OR functional behaviour reconciles with structural properties is a challenge,
because there is no experimental structure available for ORs and because most of them are orphans, i.e their physiological agonists are unknown. Nevertheless during the last decade, more
than 25 high-resolution X-ray structures of GPCRs have been solved, tremendously augmenting
insights into their structure/function relationships [7]. GPCRs share a conserved topology of 7
transmembranes (TM) helix bundle that shows 36 topologically equivalent positions with conserved residues or motifs, referred as Ballesteros–Weinstein (BW) numbering scheme [8]. Any
mutation in 14 out of 36 of these BW positions modifies the receptor activity, be it a loss or an
increase of activity. Although similarity of OR sequences can be very low compared to GPCRs,
these conserved motifs could be topological markers that anchor the alignments of the TMs and
participate in ligand binding. This hypothesis prompted us to investigate in silico the molecular
basis of folding and ligand binding in ORs. Experimental validations were performed on PSGR
-Prostate Specific G-Coupled Receptor- a RO for which we have an activity test and we know
some agonists.
∗
†
Intervenant
130
Poster P
From the multiple sequences alignment of non-redundant clustered ORs of the human genome,
we found out consensus motifs, with respect to GPCRs in general and within ORs in particular.
We were able to propose to mutation six residues referred as BW 3.32, 3.33, 3.36, 6.48, 6.51 and
7.39 that could form the ligand site, by analogy with the solved GPCRs [7]. Homology models
of PSGR were built using modeler [9], then agonist molecules, centered at those BW positions,
were docked with CHARMm [10]. Four of the six residues subjected to alanine mutagenesis in
PSGR result in functional impairment in vitro, thus validating our computational analysis. Ligand specificity in ORs is now addressed that discriminates agonists from antagonists. Eventually,
this work will allow a more accurate version of our GPCR automodel webservice [11].
Buck L et al. (1991) Cell 65: 175-187
Niimura Y et al. (2007) PLoS One 2: e708
Neuhaus EM et al. (2009) J Biol Chem 284: 16218-16225.
Braun T et al. (2007) Gastroenterology 132: 1890-1901
Kidd M et al. (2008) Am J Physiol Gastrointest Liver Physiol 295: G260-272
Griffin CA et al. (2009) Dev Cell 17: 649-661
Venkatakrishnan A. J et al. (2013) Nature 494: 185-194
Ballesteros J A and Weinstein H. Methods. Neurosci. 1995, 25, 366-428.
Sali & Blundell (1999) J Mol Biol 234: 779-815
Brooks B.R et al. (2009) J Comput Chem 30: 1545-1614
Launay G et al. (2012) PEDS 25: 377-386
131
Poster P
Molecular Modeling CoMFA and docking of a
Novel Series of Potent 7-Azaindole Based
Tri-Heterocyclic Compounds as CDK2/Cyclin
E Inhibitors
Christine B. Baltus 1 , Radek Jorda 2 , Christophe Marot ∗† 1 , Karel Berka
3,4
, Václav Bazgier 4,5 , Vladimír Kryštof 5 , Gildas Prié 1 , Marie-Claude
Viaud-Massuard 1
1
Génétique, Immunothérapie, Chimie et Cancer (GICC) – CNRS : UMR7292, Université François
Rabelais - Tours – Faculty of Pharmacy, 31 avenue Monge, 37200 Tours, France
2
Centre of the Region Haná for Biotechnological and Agricultural Research, Palacký University and
Institute of Experimental Botany – AS CR, Šlechtitelů 11, CZ-78371 Olomouc, République tchèque
3
Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry –
Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 77146 Olomouc, République tchèque
4
Department of Physical Chemistry, Faculty of Science, Palacký University – 17. Listopadu 1192/12,
771 46 Olomouc, République tchèque
5
Laboratory of Growth Regulators Department of Chemical Biology and Genetics – Šlechtitelů 11,
CZ-78371 Olomouc, République tchèque
From four molecules inspired by the structural features of fascaplysin, with an interesting
potential to inhibit the complex CDK2/cyclin E, we designed a new series of tri-heterocyclic
derivatives based on 1H -pyrrolo[2,3-b]pyridine (7-azaindole) and triazole heterocycles. Comparative molecular field analysis (CoMFA), based on three-dimensional quantitative structureactivity relationship (3D-QSAR) studies, was conducted on a series of 30 compounds from the
literature with high to low known inhibitory activity towards the complex CDK2/cyclin E and
was validated by a test set of 5 compounds giving satisfactory predictive r2 value of 0.92. Remarkably, it also gave us a good prediction of pIC50 for our tri-heterocyclic series which reinforce
the validation of this model for the pIC50 prediction of external set compounds.
∗
†
Intervenant
132
Poster P
Study of large-scale conformational transitions
in c-Abl at the free energy level via a
structure-based model
Ilaria Mereu
1
∗† 1
Karlsruhe Institute of Technology [Eggenstein-Leopoldshafen] (KIT) – Campus North
Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen, Allemagne
The c-Abl protein is a non-receptor tyrosine kinase of primary relevance in cancer research.
It is regulated by a complex interplay of conformational transitions that are currently difficult
to simulate in full atomistic detail for free energy calculation purposes. The computational approach of structure-based models, though, makes an efficient study of conformational transitions
of large amplitude feasible, even with limited computational resources.
We integrated a highly accurate, non-standard structure-based model (that we previously contributed to develop) with the metadynamics method for enhanced sampling. This protocol was
applied to a rich set of conformational transitions that take place in the regulation process of
c-Abl 1b (the activation loop opening, the DFG motif flip, the displacement of the SH2 and SH3
domains, the hinge motion and others) and gave us access to the free energy profile associated
to each transition.
This work compares three constructs of increasing length: the isolated kinase domain (kd),
SH2+kd and SH3+SH2+kd. The performance of the model on three constructs at the free energy level was compared with the aim of elucidating the role of each domain and conformational
transition in c-Abl activation mechanism.
∗
†
Intervenant
133
Poster P
Cartographie des interactions atomiques dans
le filament de dystrophine
Dominique Mias-Lucquin ∗ 1 , Olivier Delalande 1 , Elisabeth Le Rumeur 1 ,
Anne-Elisabeth Molza 1 , Jean-Francois Hubert 1
1
IGDR – Universite de Rennes 1, CNRS : UMR6290 – France
La dystrophine est une protéine de très grande taille (427kDa) qui a pour rôle de maintenir
la cohésion du sarcolemme au cours des cycles de contractionélongation du muscle. Le domaine
central de la protéine est constitué de 24 répétitions repliées en faisceaux d’hélices alpha de type
spectrine. Dans les hélices, la succession d’acides aminés hydrophobes tous les 3 et 4 résidus en
alternance définit les heptades qui permettent le repliement en faisceaux des trois hélices A, B
et C d’une même répétition. La récente résolution de modèles structuraux par flexible fitting
sous contraintes expérimentales de diffusion des rayons X aux petits angles (SAXS) nous permet
d’atteindre un niveau de compréhension atomique pour les propriétés structurales et dynamiques
du faisceau d’hélices qui confèrent à la dystrophine sa fonction de préservation du muscle.
Nous avons développé un outil d’analyse des interactions atomiques (Dys-CCC) qui nous permet
d’établir une cartographie précise des interactions hydrophobes, des liaisons hydrogènes et des
ponts salins, au sein du domaine central de la dystrophine. Sur la base d’une carte de proximité spatiale et de mesures de potentiels hydrophobes, Dys-CC génère différentes représentations
graphiques complémentaires pour l’étude des contacts inter-hélices.
Les premiers résultats obtenus mettent en évidence la prédominance des interactions hydrophobes
entre hélices d’une même répétition, parfois renforcées par des liaisons hydrogènes ou ponts
salins. Même si les interactions hydrophobes s’organisent principalement autour d’une heptade
de résidus, des variations existent autour de ce motif et pourraient expliquer la diversité structurale obesrvée tout au long du filament que compose le domaine central de la dystrophine.
Dys-CCC est basé sur la bibliothèque Pynteract développée dans le laboratoire. Cette API peut
également être utilisée pour traiter des interactions au sein de complexes moléculaires (contacts
intermoléculaires), comme pour l’analyse de résultats de docking.
∗
Intervenant
134
Poster P
Développement d’une stratégie multi-échelle
combinant des données expérimentales et in
silico pour la reconstruction de complexes
impliquant la dystrophine.
Anne-Elisabeth Molza ∗† 1 , Khushdeep Manga 2 , Olivier Delalande 1 ,
Nicolas Ferey 3 , Marc Baaden 4 , Chantal Prevost 4 , Sébastien Fiorucci 5 ,
Mirjam Czjzek 6 , Nick Menhart 2 , Denis Chrétien 1 , Elisabeth Le Rumeur
1
, Jean-François Hubert 1
1
IGDR, Université Rennes 1 – CNRS : UMR6290, Universite de Rennes 1 – Rennes, France
2
Illinois Institute of Technology – Chicago IL, États-Unis
3
LIMSI (3) – CNRS : UPR3251 – CNRS UPR 3251- IUT d’Orsay, Université PARIS XI, Paris, France
4
Laboratoire de Biochimie Théorique - Institut de Biologie Physico-Chimique – CNRS : UPR9080,
Université Paris VII - Paris Diderot – Institut de Biologie Physico-Chimique 13, rue Pierre et Marie
Curie, F-75005 Paris, France
5
Institut de Chimie de Nice (5) – CNRS : UMR7272, Université Nice Sophia Antipolis [UNS],
6
Station Biologique de Roscoff – CNRS : UMR8227 – Roscoff, France
La dystrophine est une très grande protéine sous-sarcolemmique de 427kDa codée le gène
DMD. Cette protéine joue un rôle essentiel dans le maintien de l’intégrité de la cellule musculaire
lors des cycles de contraction/relaxation.
La dystrophine est composée de quatre principaux domaines structuraux dont le domaine central
composé de 24 répétitions et quatre charnières. Chaque répétition est organisée en faisceau de
trois -hélices homologues à la spectrine et appelé “ coiled-coil ”.
Des mutations du gène DMD sont à l’origine des myopathies de Duchenne et de Becker, lesquelles
s’accompagnent de ruptures fréquentes de la membrane des cellules musculaires.
Malgré le rôle important que joue la dystrophine, il existe peu de données sur sa structure
et ses interactions avec ses principaux partenaires cellulaires.
Afin de pallier à ce manque, nous avons développés une approche multi-échelle combinant des
données expérimentales et des données in silico [1].
Cette méthodologie nous a permis d’une part d’obtenir des modèles tout-atome de fragments
de la dystrophine native et mutée, en se basant sur des données de diffusion des rayons X aux
petits angles (SAXS) et des méthodes de simulations interactives.
∗
†
Intervenant
135
Poster P
D’autre part, nous avons utilisés des méthodes d’amarrage moléculaires afin d’obtenir des modèles de complexes impliquant l’actine filamenteuse (actine-F) ou le domaine PDZ de la nNOS
(nNOS-PDZ) avec les répétitions R11-15 ou R16-17 de la dystrophine native ou de mutants BMD
de la protéine.
Cette stratégie a permis de reconstruire un modèle tout-atome du complexe macromoléculaire
impliquant les répétitions R11-17 de la dystrophine, l’actine-F, nNOS-PDZ et les phospholipides
membranaires.
1. Molza, A.-E. et al. (2014) Faraday Discuss. 169, 45–62
136
Poster P
Analysis of protein structure and dynamics to
better understand their functional mechanisms
Damien Monet ∗ 1 , Nathan Desdouits 1 , Michael Nilges 1 , Arnaud Blondel 1
1
Unité de Bioinformatique structurale – CNRS : UMR3528, Institut Pasteur de Paris – 28, rue du
Docteur Roux - 75724 Paris Cedex 15, France
In many cases, proteins exert their function through internal motions. Accurate understanding of protein functional mechanisms can be useful, for example, to elaborate innovative
inhibition strategies attempting to use small molecules to block protein motions. This approach
is illustrated on two targets involved in different pathologies, the Rift Valley Fever Virus envelope protein (Gc), and the nicotinic Acetylcholine Receptors (nAChRs), which both act through
large functional transitions. Analysis of cavities on molecular dynamics of Gc helped us to find
molecular levers on its action mechanism. nAChRs have been studied for decades, but solving
their structures remains a challenge. To tackle this class of targets we first needed to build high
quality comparative models for both the open and closed forms of the same protein sequence. For
that we devised a specific approach gathering and sorting the information from known structures
of many different homologuous proteins in different states to selectively build models representing the active/inactive forms. These models will be used as starting points for transition path
calculations by the POE approach developed in the laboratory.
∗
Intervenant
137
Poster P
Conformational properties and transport of
glucose by GLUT1 using in silico approaches
Matthieu Ng Fuk Chong
∗† 1
, Catherine Etchebest‡
1
1
Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB) – Université
Paris Diderot, Sorbonne Paris Cité, INTS, Inserm : UMRS1134 – 6 rue Alexandre Cabanel, 75739 Paris
cedex 15, France
Glucose is an essential source of energy for the mammalian cells. Its transport is achieved by
facilitative diffusion by the glucose transporter GLUT1 into erythrocytes and endothelial cells of
the blood brain barrier. Thus, inactivating mutation or low expression of GLUT1 is medically
important, as it will lead to a lack of glucose supply to the brain. On the other hand, the expression levels of GLUT1 are often elevated in malignant cells due to the accelerated metabolism
and high glucose requirements of the cells.
GLUT1 belongs to the family of sugar transporter, a subfamily of the major facilitator superfamily (MFS). The MFS transporters are known to alternate between different conformational
states in which access to the central binding site is switched from the extracellular medium (outward open conformation) to the intracellular compartment (inward open conformation) in order
to transport a substrate across the plasma membrane. Recently, a mutant form of GLUT1 was
purified and structurally characterized by X-ray crystallography [1]. The structure was trapped
in an inward-open state with a molecule of -nonyl glucoside (BNG), a glucose-derivative, located
within a cavity. Even if this atomic 3D structure is valuable, it does not allow to fully elucidate
the transport mechanism of glucose by GLUT1.
To address this question, we first modeled the 3D structure of the wild-type (WT) form using the
crystal structure of the GLUT1 mutant. Then, we performed molecular dynamics simulations of
the WT protein embedded in a POPC bilayer where the crystallized ligand was either replaced
by a -D-glucose or suppressed.
In the holo state (i.e. with a bound glucose in the central cavity), the glucose translocates
out of the protein. In the cytoplasmic half of the channel, glucose interacts predominantly with
polar or charged residues and underwent several rotations before leaving the binding site and
exiting the protein. In the apo state (i.e. without glucose in the central cavity), the results of
1 µs equilibrium MD simulations of GLUT1 showed the partial closure of the cytoplasmic end
of the protein. This state was similar to the intermediate state, namely the occluded state seen
in the bacterial homologue XylE, in which the solvated central cavity is inaccessible from either
side of the membrane. For the first time, these results enlighten the role of the different amino
acids in the conformational changes and in the translocation mechanism.
1. Deng D, Xu C, Sun P, Wu J, Yan C, et al. (2014) Crystal structure of the human glucose
transporter GLUT1. Nature 510: 121-125.
∗
Intervenant
‡
†
138
Poster P
As-Rigid-As-Possible interpolation for
structural biology
Minh Khoa Nguyen
1
∗† 1
, Léonard Jaillet 1 , Stephane Redon
1
INRIA Rhône-Alpes (INRIA Grenoble Rhône-Alpes) – INRIA – ZIRST 655 Avenue de l’Europe
Montbonnot 38334 Saint Ismier cedex, France
Finding the transition pathway or minimum energy path (MEP) between two conformations
is a complicated problem in structural biology, because the search space typically has high dimension. Methods such as Nudged Elastic Band (NEB) [1] and zero temperature string [2] search for
a MEP from an initial guess of the pathway. A common way to choose the initial guess is to use
the linear interpolation between the start and goal conformations. However, such a guess does
not necessarily give a realistic path, and it has been shown that it can lead to low convergence
or failure of the chosen method [3].
In the field of computer graphics, methods for interpolating between a complicated shape and a
deformed version of it have been applied to find a smooth and realistic motion between two poses
of a figure [4] [5] [6]. Inspired by this research, we apply the As-Rigid-As-Possible (ARAP) interpolation between two given conformations. This method attempts to preserve the rigidity of the
structure, i.e. changes in bond lengths and angles are minimized as much as possible during the
interpolation process. Consequently, the interpolation creates a series of low-change-of-energy
transitions from one conformation to another. This method can find a good initial guess for
other algorithms such as NEB.
In our method, a large molecule is divided into smaller connected regions. The amount of
rotation of each region is calculated from two given conformations and then interpolated. The
interpolated image of the entire molecule is a result of combining all individual interpolations. As
a result, this method produces little local deformations. For evaluation purposes, this method is
compared with linear interpolation. NEB is applied to minimize the energy of the initial guesses
obtained by ARAP interpolation and linear interpolation. The tests were performed on the folding of a graphene sheet into a nanotube, as well as a few other benchmarks. Results show that
the initial guess produced by the ARAP interpolation method gives an appropriate transition
path in a shorter time than that from linear interpolation.
Our method has several advantages: force field independence, low energy changes along the
pathway due to local rigidity preservation, and high parallelization capability. It will be applied
to more complicated molecules such as proteins and RNA in the future.
References:
H. Jónsson, G. Mills, K. W. Jacobsen, “Nudged Elastic Band Method for Finding Minimum En∗
†
Intervenant
139
Poster P
ergy Paths of Transitions,” Classical and Quantum Dynamics in Condensed Phase Simulations,
ed. B. J. Berne, G. Ciccotti and D. F. Coker (World Scientific, 1998), 385.
E. Weinan, W. Ren, E. Vanden-Eijnden, “Simplified and improved string method for computing
the minimum energy paths in barrier-crossing events,” The Journal of Chemical Physics (2007),
126 (16), 164103.
B. Peters, A. Heyden, AT. Bell, A. Chakraborty, “A growing string method for determining
transition states: comparison to the nudged elastic band and string methods,” The Journal of
Chemical Physics (2004), 120(17), 7877-7886.
O. Sorkine, M. Alexa, “As-rigid-as-possible surface modeling,” In Proceedings of the fifth Eurographics symposium on Geometry processing (SGP ’07). Eurographics Association, Aire-la-Ville,
Switzerland, Switzerland, 109-116.
Z. Zhang, G. Li, H. Lu, Y. Ouyang, M. Yin, C. Xian, “Fast as-isometric-as-possible shape interpolation,” Computers & Graphics (2015), 46, 244-256.
W. Baxter, P. Barla, K. Anjyo. “Rigid shape interpolation using normal equations,” In Proceedings of the 6th international symposium on Non-photorealistic animation and rendering (NPAR
’08). ACM, New York, NY, USA, 59-64.
140
Poster P
What wins in determining protein regular
secondary structures? The polar/non polar
binary pattern or the intrinsic amino acid
composition? A statistical analysis using the
HCA approach.
Joseph Rebehmed
∗ 1
, Jean-Paul Mornon 1 , Isabelle Callebaut†
1
1
Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC) – Institut de
recherche pour le développement [IRD] : UR206, Université Pierre et Marie Curie (UPMC) - Paris VI,
CNRS : UMR7590, Muséum National d’Histoire Naturelle (MNHN) – Tour 23 - Barre 22-23 - 4e étage BC 115 4 place Jussieu 75252 PARIS, France
The intrinsic preferences of a polypeptidic chain to form -helices or -strands can be overwhelmed by the polar/nonpolar periodicity and the drive to form amphiphilic structures capable
of burying hydrophobic surface area. As a consequence, the precise identity of a amino acid at a
particular location in a sequence may be less important than the simple choice of whether it is
polar or nonpolar.
In this study, we considered two sets of 3D structures extracted from the SCOP database (classes
a, b, c, d, e) at two levels of redundancy (ASTRAL 95 and ASTRAL 40) in order to clarify the
roles played respectively by binary patterns and amino acid compositions in the formation of regular secondary structures (RSS). We considered HCA-derived hydrophobic clusters (HC), which
are constrained binary patterns particularly well fitting RSS limits, to investigate the preferences
of binary patterns towards RSS and estimate the influence of amino acid composition.
We first used a “Quark” code, to decompose each HC into its basic units and thereby evaluate how its main periodicity correlates with its main state in terms of RSS. Results showed that
-helices are more permissive to -strand patterns than -strands are to -helix patterns.
Then, we calculated the propensities of amino acids for the different states of secondary structures, considering all the RSS associated with each HC or distinguishing between -helices and strands. Concordant and discordant behaviors are then assigned following the agreement/disagreement
of the observed RSS with that expected from the HC dictionary. In agreement with previous reports, three classes of amino acids are highlighted, with preferences for -helices (A,L,M,E,Q,K,R),
-strands (V,I,F,W,Y,C,T) and coils (P,G,D,N,S). Histidine (H) has quasi-equal preferences for
the three states. On this basis, distinct behaviors can be highlighted between the concordant
and discordant HC. In “-strand” HC, the weight of aliphatic hydrophobic amino acids appears to
be prominent for shifting from a concordant to a discordant behavior, whereas in “-helix” HC, all
the amino acids with preferences for -helices globally decrease at the benefit of non-hydrophobic
∗
†
Intervenant
141
Poster P
amino acids with preference for -strands and coils.
Altogether, our results enlighten the respective role played by binary pattern and amino acid
composition for RSS formation. The original approach described here could complement the
currently available methods to predict secondary structures in proteins and could assist protein
engineering and design, from the only information of a single amino acid sequence.
142
Poster P
Molecular features switching off the
melatonin-induced activity from MT1 to
GPR50 receptors
Nicolas Renault ∗† 1 , Xavier Laurent 1 , Nathalie Clement 2 , Jean-Luc
Guillaume 2 , Amaury Farce 1 , Ralf Jockers 2 , Philippe Chavatte 1,3
1
Faculté des Sciences Pharmaceutiques de Lille – Université Lille 2 - LIRIC U995 Inserm – 3, rue du
Professeur Laguesse - 59000 Lille, France
2
Institut Cochin – CNRS : UMR8104, Inserm : U1016, Université Paris V - Paris Descartes – 22 rue
Méchain, 75014 Paris, France
3
Institut de Chimie Pharmaceutique Albert Lespagnol – Université Lille II - Droit et santé – 3, rue du
Professeur Laguesse. 59000 LILLE, France
Recent crystal structures of G protein-coupled receptors (GPCRs) highlight the previously
unappreciated role of the second extracellular loop (E2) in ligand binding, ligand gating or
receptor activation. In agreement with functional in vitro assays, molecular dynamics simulations
reveal the critical role of E2 in activation of the melatonin MT1 receptor and in inactivation of
the closely related orphan GPR50 receptor.
∗
†
Intervenant
143
Poster P
Coarse-Graining Parameterization of Specific
Cardiac Membrane Lipids
Edithe Selwa
1
∗ 1
, Bogdan Iorga
1
Institut de Chimie des Substances Naturelles (ICSN) – CNRS : UPR2301 – Avenue de la terrasse
91198 Gif sur yvette cedex, France
Recently, a Cardiac Targeting Peptide (CTP) [1] was shown to be able to transduce specifically the myocardium and can be used as a new strategy to deliver therapeutic drugs in the
framework of treating heart failure [2,3].
Our general aim is to understand how CTP interacts and penetrates the cardiomyocyte membrane by molecular dynamics simulations. However, simulation of such large biological systems
is challenging, because of the multitude of spatial and temporal scales involved. Biological processes, such as self-assembly of lipid bilayers, remain inaccessible by all-atom simulations, since
these time-consuming approaches are computationally expensive. Coarse grained (CG) models,
which have gained a lot of popularity lately, overcome these limits, by replacing full-atomistic
details by lower resolution particles, while retaining chemical specificity.
In this work, given the complexity and the size of the system, we have used a CG representation of the system components, based on MARTINI force field [4,5]. We have parameterized
a number of missing molecules using this force field, and then generated a realistic model of the
cardiomyocyte membrane that will be used in the future for the study of the interaction with
CTP.
∗
Intervenant
144
Poster P
Prediction of peptide conformational stability
Marie Amandine Laurent 1 , Raphael Licha 1 , Jananan Pathmanathan 1 ,
Jacques Chomilier 1 , Dirk Stratmann ∗ 1
1
Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC) – Institut de
recherche pour le développement [IRD] : UR206, Université Pierre et Marie Curie (UPMC) - Paris VI,
CNRS : UMR7590, Muséum National d’Histoire Naturelle (MNHN) – Tour 23 - Barre 22-23 - 4e étage BC 115 4 place Jussieu 75252 PARIS, France
Protein-protein interactions play a key role in biological functions. Their inhibition become
an important issue for drug design. Peptides are suitable candidates to target specific interactions
especially if they are extracted from the interaction interface. Among these protein fragments,
Tightened End Fragments (TEF) (**) have the advantage to be potentially more stable in terms
of their conformation. This should confer them a better affinity. A TEF is not unique at the
interaction interface and the choice can be optimized in term of stability and affinity. Our study
is about the aspect of conformational stability. Using molecular dynamics simulations, we developed a protocol for peptide conformational stability prediction. To validate our method, we
assembled a benchmark of peptide structures associated with protein fragments. Our method succeeds in distinguishing stable from unstable peptides. In addition, it allowed us to identify some
fragments that are stable outside the protein. (**) http://www.impmc.upmc.fr/software/tef
∗
Intervenant
145
Poster P
Dynamique d’assemblage de la capside des
norovirus
Thibault Tubiana
∗† 1,2
, Stéphane Bressanelli‡
1,2
, Yves Boulard§
1,2
1
Institut de Biologie Intégrative de la Cellule (I2BC) – CNRS : UMR9198 – Bâtiment 21 Avenue de la
Terrasse 91190 Gif-sur-Yvette Cedex, France
2
Institut de Biologie et de Technologies de Saclay (IBITECS / SB2SM) – CEA – CEA Saclay bat 532
91191 Gif sur Yvette cedex, France
Les norovirus sont des petits virus non enveloppés à ARN simple brin de polarité positive.
Ils sont la cause principale des gastroentérites virales aigues chez les humains et les animaux
et présentent un intérêt mondial pour la santé. Leur capside est composée de 180 copies d’une
seule protéine structurale VP1 composée de deux domaines principaux : le domaine shell (S)
et le domaine protruding (P) qui contient 2 sous domaines (P1 et P2)[1]. Le domaine P est la
partie exposée au milieu biologique, il permet notamment de stabiliser et d’ajuster la taille de la
capside[2]. Le domaine S quant à lui est le module d’assemblage de la capside virale[2].
Des études ont montré que les capsides de certains norovirus (NB2-VLP, NV-VLP) peuvent
être désassemblées et réassemblées in vitro selon le pH du milieu[3] et que l’assemblage de NB2VLP (NewBury2 – Virus-Like particles) à partir de dimères de VP1 est un processus en deux
étapes : une apparition rapide d’intermédiaires moléculaires puis une formation lente de la capside, ce qui est en désaccord avec les modèles actuels d’assemblage de capside[4]. Ces études
nous conduisent à l’hypothèse que le bras N-terminal serait un bon candidat dans la stabilisation
allostérique des intermédiaires et que la (dé)protonation de certains résidus serait impliquée dans
l’assemblage ou le désassemblage (dépendant au pH) de la capside in vitro.
A l’aide d’approches computationnelles, nous cherchons donc à étendre ces études cinétiques
aux norovirus humains et ainsi à déterminer les bases moléculaires du mécanisme physique original d’assemblage des norovirus. Des techniques de bioinformatique telles que des alignements de
séquences et modélisations par homologie ont été appliquées afin d’obtenir une structure tridimensionnelle du dimère de NB2-VP1 (Génogroupe III), basée sur la structure cristallographique
de son homologue du génogroupe I : le Norwalk Virus (1IHM[1]). Ce modèle a ensuite été utilisé
dans des simulations de dynamique moléculaire et de recuits simulés afin d’analyser le comportement du bras N-terminal dans un environnement aqueux.
Nos résultats préliminaires étendent les études réalisées auparavant dans l’équipe [3,4]. Ces résultats montrent que sous sa forme déprotonée le bras N-terminal établit de nombreuses interactions
polaires de type pont-salins permettent sa stabilisation sous le domaine S de la protéine. Ces
pont-salins ont ensuite été neutralisés par protonation des acides aminés acides impliqués dans
∗
Intervenant
‡
§
†
146
Poster P4
l’interaction afin d’étudier l’importance de ces liaisons pour la stabilité du bras N-terminal.
1. Prasad BV V. X-ray Crystallographic Structure of the Norwalk Virus Capsid. Science (80- ).
1999;286: 287–290. doi:10.1126/science.286.5438.287
2. Bertolotti-Ciarlet A, White LJ, Chen R, Prasad BV V., Estes MK. Structural Requirements for
the Assembly of Norwalk Virus-Like Particles. J Virol. 2002;76: 4044–4055. doi:10.1128/JVI.76.8.40444055.2002
3. Tresset G, Decouche V, Bryche J-F, Charpilienne A, Le Cœur C, Barbier C, et al. Unusual
self-assembly properties of Norovirus Newbury2 virus-like particles. Arch Biochem Biophys. Elsevier Inc.; 2013;537: 144–52. doi:10.1016/j.abb.2013.07.003
4. Tresset G, Le Coeur C, Bryche J-F, Tatou M, Zeghal M, Charpilienne A, et al. Norovirus
capsid proteins self-assemble through biphasic kinetics via long-lived stave-like intermediates. J
Am Chem Soc. 2013;135: 15373–81. doi:10.1021/ja403550f
147
Poster P
Identification and automated weighting of
ensemble-averaged NOE restraints
Silke Wieninger
∗ 1
, Beat Vögeli 2 , Nelly Morellet 3 , Benjamin Bardiaux 1 ,
Guillaume Bouvier 1 , Michael Nilges 1
1
3
Bioinformatique Structurale – Institut Pasteur de Paris – France
2
Laboratory of Physical Chemistry, ETH Zürich – Suisse
Institut de Chimie des Substances Naturelles – Centre de Recherches de Gif – France
NMR data represent a time and ensemble average. Accordingly, not all restraints derived
from NMR experiments can be fulfilled by a single structure. To avoid introducing errors and
to estimate the dynamical behavior of the protein, ensemble calculations are performed, which
model a few conformations simultaneously. By treating only a subset of the NOEs as ensemble
restraints instead of the full restraint set, we reduce the risk of data overfitting. We identify
mutually incompatible restraints by generating a large set of conformations in a conventional
ARIA run[1]. Clustering of the conformations using self-organizing maps[2,3] enables us to assign
the restraints to different classes, which are weighted automatically during the ensemble ARIA
run, based on the deviation of the atom distances from the NOE target distances[4]. Exemplary,
we determine the structure of the 56-residue protein GB3, the third immunoglobulin binding
domain of protein G, taking advantage of an extensive set of exact NOEs[5]. On another system,
the piggyBac transposase, we show that the ensemble strategy allows to identify conformations
which cannot be obtained using standard structure determination protocols.
W Rieping, M Habeck, B Bardiaux, A Bernard, TE Malliavin, M Nilges (2007), Bioinformatics,
23:381-382.
T Kohonen (2013), Neural Networks, 37:52-65.
G Bouvier, N Duclert-Savatier, N Desdouits, D Meziane-Cherif, A Blondel, P Courvalin, M
Nilges, TE Malliavin (2014), Chemical information and modeling, 54:289-301.
M Habeck, W Rieping, M Nilges (2006), PNAS, 103:1756-1761.
B Vögeli, S Kazemi, P Gntert, R Riek (2012), Nature structural and molecular biology, 19:10531057.
∗
Intervenant
148
Listes des auteurs
Bouakouk-Chitti, Zohra, 106
Boukhenouna, Samia, 95
Boulard, Yves, 146
Bourg, Stéphane, 26, 100, 109
Bouvier, Guillaume, 76, 85, 148
Braka, Abdennour, 100
Branlant, Guy, 38, 91, 95
Bressanelli, Stéphane, 146
Broutin, Isabelle, 69
Brut, Marie, 102
Bui, Linh Chi, 90, 93
Bureau, Ronan, 53
Busca, Patricia, 55
Abdellatif, Nadhir, 83
Abi Hussein, Hiba, 59
Aci-Sèche, Samia, 34, 100
Agbulut, Onnik, 93
Aghajari, Nushin, 37
Allain, Fabrice, 74
Allouche, David, 45, 98
Alvarez, Pablo, 105
Amillastré, Emilie, 105
André, Isabelle, 45, 96, 98, 105
Andre-Leroux, Gwenaëlle, 130
Antonczak, Serge, 65, 113, 119
Archontis, Georgios, 103
Artemova, Svetlana, 83
Asencio Hernández, Julia, 39
Aubert, Marc, 83
Audit, Edouard, 103
Callebaut, Isabelle, 141
Cambon, Emmanuelle, 96
Camproux, Anne-Claude, 59, 121
Carrique, Loic, 37
Castaing, Bertrand, 26, 109
Cerdan, Rachel, 27
Charpentier, Emmanuelle, 119
Chatron, Nolan, 24
Chauvot De Beauchene, Isaure, 79
Chavatte, Philippe, 143
Chavent, Matthieu, 67
Cherfils, Jacqueline, 22
Cheron, Jean-Baptiste, 113, 119, 121
Chomienne, Christine, 90
Chomilier, Jacques, 145
Chrétien, Denis, 135
Clement, Nathalie, 143
Coadou, Gaël, 28
Colin, Sabrina, 30
Colloc’h, Nathalie, 89
Cools, Jan, 90
Cortes, Isidro, 85
Cortes, Juan, 114
Costa, Mauricio, 104, 123
Coste, Franck, 26, 109
Cragnolini, Tristan, 118
Crouzy, Serge, 115
Culard, Françoise, 26, 109
Czjzek, Mirjam, 135
Baaden, Marc, 117, 118, 135
Ballut, Lionel, 37
Baltus, Christine B., 132
Banarès, Jean-Louis, 123
Barakat, Fatima, 111
Barbault, Florent, 55
Barbe, Sophie, 45, 96, 98, 105
Bardiaux, Benjamin, 74, 148
Batista, Paulo R., 104
Baud, Stéphanie, 112
Bazgier, Václav, 132
Belloy, Nicolas, 119
Becker, Hubert F., 51
Ben Ouirane, Kaouther, 69
Bender, Andreas, 85
Benoit, Etienne, 24
Berka, Karel, 132
Bersweiler, Antoine, 38, 91
Best, Robert, 61
Bisch, Paulo M., 104
Blondel, Arnaud, 137
Blumberger, Jochen, 61
Boitard, Solène, 93
Bonnet, Pascal, 26, 34, 100
Borrel, Alexandre, 59
Bosc, Nicolas, 34
Boschi-Muller, Sandrine, 30, 88
Damian, Marjorie, 123
149 Gageat, Cédric, 118, 125
Gaillard, Thomas, 43
Garkavenko, Maria, 126
Garnier, Norbert, 26, 100, 109
Gate, Jocelyn, 83
Gelly, Jean-Christophe, 81
Geneix, Colette, 59
Ghouzam, Yassine, 81
Gloaguen, Céline, 53
Goffinont, Stéphane, 26, 109
Golebiowski, Jérôme, 65, 113, 119
Grudinin, Sergei, 77, 83, 126
Guca, Ewelina, 27
Gueroult, Marc, 105
Guichou, Jean-François, 27
Guidez, Fabien, 90
Guillaume, Jean-Luc, 143
Guyon, Frédéric, 57
Daniellou, Richard, 28
Dauchez, Manuel, 112, 119
David, Benoît, 63
De Brevern, Alexandre, 81
De Givry, Simon, 98
De March, Claire, 119
De Sancho, David, 61
De Vecchis, Dario, 117
De Vries, Sjoerd, 79
Debiec, Hanna, 107
Dejaegere, Annick, 52, 62
Delalande, Olivier, 134, 135
Delamar, Michel, 55
Delarue, Patrice, 111
Delsuc, Marc-André, 39
Dementin, Sébastien, 35, 94
Derreumaux, Philippe, 118, 125
Desdouits, Nathan, 137
Diharce, Julien, 65
Djaout, Kamel, 51
Dobbek, Holger, 94
Domnik, Lilith, 94
Donald, Bruce, 45
Douguet, Dominique, 50
Doutreligne, Sébastien, 118
Druart, Karen, 103
Duncan, Anna, 67
Duneau, Jean-Pierre, 70
Dupret, Jean-Marie, 90, 93
Duquesne, Sophie, 105
Durant, Laeticia, 90
Duval, Romain, 90
Hélie, Jean, 67
Hénin, Jérôme, 117
Hadj-Said, Jessica, 35, 94
Hilvert, Donald, 42
Hoh, François, 27
Hu, Rongjing, 55
Hubert, Jean-François, 134, 135
Iorga, Bogdan, 107, 144
Isenberg, Tobias, 73
Jacob, Christophe, 95
Jaillet, Léonard, 83, 139
Jockers, Ralf, 143
Jorda, Radek, 132
Eberhardt, Jérôme, 62
Emonard, Hervé, 119
Etchebest, Catherine, 69, 138
Etique Nicolas, 119
Katava, Marina, 128
Kauffmann, Brice, 29
Kazennov, Andrei, 126
Kellou-Taïri, Saf a, 106
Krebs, Fanny, 52
Kriznik, Alexandre, 38, 91
Kryštof, Vladimír, 132
Kubas, Adam, 61
Farce, Amaury, 143
Fehrentz, Jean-Alain, 123
Ferber, Mathias, 76
Ferey, Nicolas, 135
Fiorucci, Sébastien, 65, 113, 135
Flatters , Delphine, 121
Floquet, Nicolas, 104, 123
Fogha, Jade, 53
Fourmond, Vincent, 35, 94
Léger, Christophe, 35, 94
Lafite, Pierre, 28
Lambert, Eléonore, 112
Langenfeld, Florent, 24
150 Narth, Christophe, 71
Neveu, Emilie, 77
Ng Fuk Chong, Matthieu, 138
Nguyen, Khoa, 83
Nilges, Michael, 74, 76, 85, 137, 148
Lattard, Virginie, 24
Laurent, Marie Amandine, 145
Laurent, Xavier, 143
Laurin, Yoann, 129
Le Guen, Yann, 96
Le Rumeur, Elisabeth, 134, 135
Lec, Jean-Christophe, 30, 88
Legeai-Mallet, Laurence, 55
Li, Yan, 55
Licha, Raphael, 145
Limoges, Benoit, 29
Louet, Maxime, 66
Oulyadi, Hassan, 28
Overington, John P., 85
Pandélia, Maria-Eirini, 35
Panel, Nicolas, 43
Pasco-Brassart, Sylvie, 112
Pasquali, Samuela, 118, 125
Pathmanathan, Jananan, 145
Perahia, David, 104, 123
Petit, Emile, 90
Petitjean, Michel, 59
Piquemal, Jean-Philip, 71
Popov, Petr, 77
Postic, Guillaume, 81
Poulain, Laurent, 53
Prangé, Thierry, 89
Prestat, Guillaume, 55
Prevost, Chantal, 129, 135
Prié, Gildas, 132
Malliavin, Therese, 85
Manga, Khushdeep, 135
Mano, Nicolas, 29
Marie, Jacky, 123
Marot, Christophe, 132
Marques, Stéphanie, 28
Marroun, Sami, 28
Martinez, Jean, 104, 123
Martiny, Laurent, 119
Martiny, Virginie, 107
Marty, Alain, 105
Mary, Sophie, 123
Mathieu, Cécile, 90, 93
Maurel, François, 55
Mavre, François, 29
Mazon, Hortense, 30, 88, 91, 95
Menhart, Nick, 135
Mereu, Ilaria, 133
Merrouch, Meriem, 94
Meyer, Christophe, 34
Mias-Lucquin, Dominique, 134
Mignon, David, 43, 103
Molza, Anne-Elisabeth, 134, 135
Monet, Damien, 137
Montaut, Sabine, 28
Morellet, Nelly, 148
Mornon, Jean-Paul, 141
Moroy, Gautier, 57
Morris, May C., 104
Moulis, Claire, 96
Mulard, Laurence, 96
Munier-Lehmann, Hélène, 33
Myllykallio, Hannu, 51
Rahuel-Clermont, Sophie, 38, 91, 95
Rasolohery, Inès, 57
Raussin, Florent, 104
Rebehmed, Joseph, 141
Reddy, Tyler, 67
Redon, Stephane, 83, 139
Regad, Leslie, 59, 121
Remaud-Siméon, Magali, 96
Renault, Nicolas, 143
Renault, Pedro, 104
Rieux, Charlotte, 26, 109
Rinaldo, David, 48
Ritchie, Dave, 77
Robert, Charles, 129
Roberts, Kyle, 45
Rodrigues-Lima, Fernando, 90, 93
Rollin, Patrick, 28
Ronco, Pierre, 107
Sacquin-Mora, Sophie, 129
Salamone, Stéphane, 96
151 Sanejouand, Yves-Henri, 63
Sansom, Mark, 67
Santolini, Jérôme, 23
Savarin, Philippe, 129
Schiex, Thomas, 45, 98
Schuler, Marie, 28
Selwa, Edithe, 107, 144
Senac, Caroline, 121
Senet, Patrick, 111
Sidore, Marlon, 70
Simoncini, David, 98
Simonson, Thomas, 43
Sopkova-De-Oliveira Santos Jana, 53
Sterpone, Fabio, 128
Stines-Chaumeil, Claire, 29
Stote, Roland, 52, 62
Stratmann, Dirk, 145
Strick, Terence, 32
Sturgis, James, 70
Tellier, Charles, 63
Toledano, Michel, 95
Traore, Seydou, 45, 98
Tubiana, Thibault, 146
Talfournier, François, 30, 88
Taly, Antoine, 117, 118
Tatibouet, Arnaud, 28
Tchertanov, Luba, 24
Yengui, Mohamed, 83
Zacharias, Martin, 79
Zhang, Rhuizheng, 55
Vögeli, Beat, 148
Van Westen, Gerard J.P., 85
Verges, Alizée, 96
Verzeaux, Laurie, 119
Vial, Henri, 27
Viaud-Massuard, Marie-Claude, 132
Violot, Sébastien, 37
Viricel, Clément, 98
Voisin-Chiret, Anne Sophie, 53
Vos, Marten, 51
Wang, Po-Hung, 61
Wieninger, Silke, 148
Wroblowski, Berthold, 34
152 Liste des participants ABI HUSSEIN Hiba
BARAKAT Fatima
MTi – Inserm – UMR‐S 973 Université Paris Diderot‐Paris 7 Bât Lamarck A, 4e‐5e étage , Courrier 7113 35, rue Hélène Brion 75205 PARIS Cedex 13 France hiba.abihussein@univ‐paris‐diderot.fr Laboratoire Interdisciplinaire Carnot de Bourgogne UMR 6303 CNRS – Université de Bourgogne 9, avenue A. Savary, BP 47870 21078 DIJON Cedex France fatima.barakat@u‐bourgogne.fr ACI‐SÈCHE Samia BARBAULT Florent
Institut de Chimie organique et Analytique Université d'Orléans – Pôle de chimie Rue de Chartres ‐BP 6759 45067 ORLEANS Cedex 2 France samia.aci@cnrs‐orleans.fr ITODYS, UMR 7086 Université Paris Diderot 15, rue J‐A de Baïf 75205 PARIS France florent.barbault@univ‐paris‐diderot.fr ALLAIN Fabrice BARBE Sophie
Unité de Bioinformatique Structurale CNRS UMR3528 Institut Pasteur 25‐28, rue du Docteur Roux 75015 PARIS France [email protected] Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP) INSA/CNRS 5504 – UMR INSA/INRA 792 135, avenue de Rangueil 31077 TOULOUSE Cedex 04 France sophie.barbe@insa‐toulouse.fr ANDRE Isabelle BAUD Stéphanie
Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP) INSA/CNRS 5504 – UMR INSA/INRA 792 135, avenue de Rangueil 31077 TOULOUSE Cedex 04 France isabelle.andre@insa‐toulouse.fr SiRMa – UMR CNRS/URCA N° 7369 Plateau de Modélisation Moléculaire Multi‐
échelle – UFR Sciences Exactes et Naturelles, Moulin de la Housse 51687 REIMS Cedex 2 France stephanie.baud@univ‐reims.fr BAADEN Marc BELLOY Nicolas
Laboratoire de Biochimie Théorique – CNRS UPR9080 Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected] Plateau de Modélisation Moléculaire Multi‐
échelle Université de Reims Champagne‐Ardenne UFR SEN, Campus Moulin de la Housse 51687 REIMS France nicolas.belloy@univ‐reims.fr 153 BEN OUIRANE Kaouther BRAKA Abdennour
INSERM UMR_S 1134,DSIMB Université Paris Diderot‐ Paris 7 INTS 6, rue Alexandre Cabanel 75739 PARIS Cedex 15 France [email protected] Institut de Chimie Organique et Analytique Université d'Orléans – Pôle de chimie Rue de Chartres – BP 6759 45067 ORLEANS Cedex 2 France abdennour.braka@univ‐orleans.fr BLUMBERGER Jochen BRUT Marie
Department of Physics and Astronomy University College London Gower Street LONDON WC1E 6BT, UK Grande‐Bretagne [email protected] Laboratoire analyse et architecture des systèmes (LAAS) – CNRS 7, avenue du Colonel Roche BP 54200 31031 TOULOUSE Cedex 4 France [email protected] BOSC Nicolas BUI Linh Chi
Institut de Chimie Organique et Analytique Université d'Orléans Rue de Chartres 45067 ORLEANS France nicolas.bosc@univ‐orleans.fr Unité Biologie Fonctionnelle et Adaptative CNRS UMR 8251 – Université Paris Diderot Bâtiment Buffon – 3ème étage – Pièce 348A 4, rue Marie‐Andrée Lagroua Weill Hallé 75205 PARIS Cedex 13 France linh‐chi.bui@univ‐paris‐diderot.fr BOSCHI‐MULLER Sandrine BUSI Florent
IMoPA UMR 7365 CNRS‐UL – Equipe Enzymologie Moléculaire – Université de Lorraine – Faculté de médecine 9, avenue de la Forêt de Haye – CS 50184 54506 VANDOEUVRE‐LES‐NANCY Cedex France sandrine.boschi@univ‐lorraine.fr Unité Biologie Fonctionnelle et Adaptative CNRS UMR 8251 – Équipe des RMCX Université Paris Diderot Paris 7 4, rue Marie Andrée Lagroua Weill Hallé 75205 PARIS Cedex 13 France florent.busi@univ‐paris‐diderot.fr BOUVIER Guillaume CARRIQUE Loïc
Bioinformatique structurale Institut Pasteur Unité de Bioinformatique Structurale CNRS UMR 3528 – Département de Biologie Structurale et Chimie 75015 PARIS France [email protected] Biocrystallography and Structural Biology of Therapeutic Targets Group Institute for the Biology and Chemistry of Proteins 7, passage du Vercors 69367 LYON Cedex 07 France [email protected] 154 CASTAING Bertrand CHAVENT Matthieu
Centre de Biophysique Moléculaire UPR4301 CNRS Rue Charles Sadron 45071 ORLEANS Cedex 2 France castaing@cnrs‐orleans.fr Biochemistry Department SBCB unit South Park Road OXFORD OX1 3QU Grande‐Bretagne [email protected] CERDAN Rachel CHERFILS Jacqueline Dynamique des Interactions Membranaires Normales et Pathologiques – UMR5235 CNRS‐Université de Montpellier Place Eugène Bataillon – Bât 24 – cc107 34095 MONTPELLIER Cedex 5 France rachel.cerdan@univ‐montp2.fr Laboratoire de Biologie et Pharmacologie Appliquée CNRS – Ecole Normale Supérieure Cachan 61, avenue du Président Wilson 94235 CACHAN cedex France jacqueline.cherfils@ens‐cachan.fr CHALOIN Laurent CHERON Jean‐Baptiste Centre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34293 MONTPELLIER Cedex 5 France [email protected] Institut de Chimie de Nice UMR 7272, Université de Nice‐Sophia Antipolis – CNRS Parc Valrose 06108 NICE Cedex 2 France [email protected] CHATRON Nolan COLLOC'H Nathalie
CMLA CNRS UMR 8536 ENS Cachan Bât. Laplace, 1er étage 61, av. du président Wilson 94235 CACHAN Cedex France nchatron@ens‐cachan.fr ISTCT UMR 6301 CNRS Université de Caen Centre Cyceron Bd Becquerel 14074 CAEN France [email protected] CHAUVOT DE BEAUCHENE Isaure
CORTES Isidro
Theoretical biophysics – Molecular dynamics TUM JamesFranceanck Strasse 1 85748 GARCHING Allemagne [email protected] Unité de Bioinformatique Structurale Institut Pasteur 25‐28, rue du Dr. Roux 75015 PARIS France [email protected] 155 CORTES Juan DE VECCHIS Dario
Laboratoire analyse et architecture des systèmes (LAAS) – CNRS 7, avenue du Colonel Roche BP 54200 31031 TOULOUSE Cedex 4 France [email protected] Laboratoire de Biochimie Théorique – UPR 9080 Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected] CROUZY Serge DEKIMECHE Katia
Chimie et Biologie des Métaux CEA 17, rue des martyrs 38054 GRENOBLE Cedex 9 France [email protected] Schrödinger GmbH Dynamostr. 13 D‐68165 MANNHEIM Allemagne [email protected] DAUCHEZ Manuel
DELALANDE Olivier
CNRS UMR 7369 MEDyC, P3M‐MaSCA Université de Reims Champagne‐Ardenne UFR Sciences Exactes et Naturelles Moulin de la Housse – BP1039 51687 REIMS Cedex 2 France manuel.dauchez@univ‐reims.fr Institut de Génétique et Développement de Rennes (IGDR) Université de Rennes 1 2, avenue du Professeur Léon Bernard 35000 RENNES France olivier.delalande@univ‐rennes1.fr DAVID Benoît DELSUC Marc‐André UFIP, UMR CNRS 6286 Université de Nantes UFR SCIENCES ET TECHNIQUES 2, rue de la Houssinière 44322 NANTES Cedex 03 France [email protected]‐nantes.fr Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS 1, rue LaurentFranceies 67404 ILLKIRCH France [email protected] DE LAMOTTE Frédéric DEMENTIN Sébastien UMR Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales INRA Avenue Agropolis – TA A‐108/03 34398 MONTPELLIER Cedex 5 France [email protected] Bioénergétique et Ingénierie des Protéines BIP6 CNRS 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected] 156 DIHARCE Julien DUNEAU Jean‐Pierre Institut de Chimie de Nice, ICN UMR CNRS 7272 28, avenue Valrose 06108 NICE Cedex 2 France [email protected] Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM) CNRS – Aix‐Marseille Université 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected] DJAOUT Kamel DUPRET Jean‐Marie Laboratoire d'Optique et Biosciences (LOB) Ecole Polytechnique Route de Saclay 91128 PALAISEAU France [email protected] Unité de Biologie Fonctionnelle et Adaptative Université Paris Diderot – CNRS UMR 8251 Bâtiment Buffon, 3ème étage 4, rue Marie Andrée Lagroua Weill Hallé 75205 PARIS Cedex 13 France jean‐marie.dupret@univ‐paris‐diderot.fr DOUGUET Dominique DUVAL Romain
Institut de Pharmacologie Moléculaire et Cellulaire CNRS UMR7275 660, route des lucioles 06560 VALBONNE France [email protected] Unité Biologie Fonctionnelle et Adaptative Université Paris Diderot – CNRS UMR 8251 Bâtiment Buffon 4, rue Marie Andrée Lagroua Weill‐Hallé 75013 PARIS France [email protected] DOUTRELIGNE Sébastien EBERHARDT Jérôme Laboratoire de Biochimie Théorique CNRS 13, rue Pierre et Marie Curie 75005 PARIS France [email protected] Modélisation Moléculaire IGBMC 1, rue LaurentFranceies BP 10142 67404 ILLKIRCH Cedex France [email protected] DRUART Karen FARCE Amaury
Laboratoire de Biochimie (BIOC) Ecole Polytechnique Route de Saclay 91128 PALAISEAU France [email protected] Therapeutic innovation targetting inflammation – LIRIC – UMR 995 Inserm Faculté de Médecine – Pôle Recherche Place Verdun 59045 LILLE Cedex France amaury.farce‐2@univ‐lille2.fr 157 FERBER Mathias GAGEAT Cédric
Bio‐Informatique Structurale Institut Pasteur 25, rue du docteur Roux 75015 PARIS France [email protected] Institut de Biologie Physico‐Chimique (IBPC) CNRS 13, rue Pierre et Marie Curie 75005 PARIS France [email protected] FIORUCCI Sébastien GAILLARD Thomas
Institut de Chimie de Nice – UMR 7272 CNRS Université de Nice Sophia Antipolis ICN, UMR 7272 CNRS 28, avenue Valrose 06108 NICE Cedex 2 France [email protected] Laboratoire de Biochimie (BIOC) Ecole Polytechnique 91128 PALAISEAU Cedex France [email protected] FLATTERS Delphine
GARNIER Norbert
UMRS 973, MTi Université Paris Diderot 35, rue H. Brion Bât. Lamarck A (courrier 7113) 75205 PARIS Cedex 13 France delphine.flatters@univ‐paris‐diderot.fr Centre de Biophysique Moléculaire CNRS Rue Charles Sadron 45071 ORLEANS Cedex 2 France norbert.garnier@cnrs‐orleans.fr FLOQUET Nicolas GELLY Jean‐Christophe Institut des Biomolécules Max Mousseron (IBMM) – UMR5247 CNRS Faculté de Pharmacie 15, avenue Charles Flahault – BP 14491 34093 MONTPELLIER Cedex 05 France nicolas.floquet@univ‐montp1.fr INSERM UMR_S 1134,DSIMB, Université Paris Diderot‐ Paris 7,INTS 6, rue Alexandre Cabanel 75739 PARIS Cedex 15 France jean‐christophe.gelly@univ‐paris‐diderot.fr FORTUNE Antoine
GOFFINONT Stéphane Département de Pharmacochimie Moléculaire UMR5063 CNRS / Université Joseph Fourier 470, avenue de la chimie 38400 SAINT MARTIN D'HERES France antoine.fortune@ujf‐grenoble.fr Centre de Biophysique Moléculaire – CNRS UPR 4301 Rue Charles Sadron CS 80054 45071 ORLEANS Cedex 2 France [email protected] 158 GRUDININ Sergei IORGA Bogdan
LJK CNRS NANO – D Inria Minatec Campus 17, rue des Martyrs 38054 GRENOBLE France [email protected] Institut de Chimie des Substances Naturelles (ICSN) CNRS 1, avenue de la Terrasse Bât. 27 91198 GIF‐SUR‐YVETTE France [email protected] GUCA Ewelina ISENBERG Tobias
Dynamique des Interactions Membranaires Normales et Pathologiques – UMR5235 CNRS‐Université de Montpellier Place Eugène Bataillon – Bât 24 – cc107 34095 MONTPELLIER Cedex 5 France [email protected] AVIZ research team Inria Saclay Université Paris‐Sud Bât. 660 91405 ORSAY France [email protected] Marc GUEROULT JAILLET Léonard
LISBP INSA 135, avenue de Rangueil 31077 TOULOUSE France marc.gueroult@insa‐toulouse.fr NANO‐D Inria Rhône‐Alpes Bâtiment 51 C Minatec Campus 17, rue des Martyrs 38054 GRENOBLE Cedex France [email protected] HADJ‐SAID Jessica
KAJAVA Andrey
Bioénergétique et Ingénierie des Protéines ‐ UMR 7281 IMM 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected] CRBM CNRS 1919, route de Mende 34293 MONTPELLIER France [email protected] HILVERT Donald KAPLAN Elise
Laboratory of Organic Chemistry ETH Zurich HCI F339 Vladimir‐Prelog‐Weg 3 ZURICH Suisse [email protected] Centre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34090 MONTPELLIER Cedex 5 France [email protected] 159 KATAVA Marina LAFITE Pierre
Laboratoire de Biochimie Théorique CNRS – UPR 9080 13, rue Pierre et Marie Curie 75005 PARIS France [email protected] Institut de Chimie Organique et Analytique Université Orléans – CNRS UMR 7311 BP6759 Rue de Chartres 45067 ORLEANS Cedex 2 France pierre.lafite@univ‐orleans.fr KELLOU‐TAIRI Safia
LAURIN Yoann
Physico‐Chimie Théorique et Chimie Informatique Université des Sciences et de la Technologie Houari Boumediene – BP 32 El Alia ALGER Algérie [email protected] Laboratoire de Biochimie Théorique Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected] KREBS Fanny LEC Jean‐Christophe Biocomputing group Structural Biology & Genomics Dept. – IGBMC
Université de Strasbourg 1, rue LaurentFranceies 67404 ILLKIRCH‐GRAFFENSTADEN Cedex France [email protected] UMR 7365 IMoPA – Equipe 3 / Enzymologie Moléculaire et Structurale 9, rue de la Forêt de Haye Bâtiment Biopole 54505 VANDOEUVRE‐LES‐NANCY France jean‐christophe.lec@univ‐lorraine.fr KRIZNIK Alexandre
LEGENDRE Audrey
UMR 7365 CNRS – Université de Lorraine Ingénierie Moléculaire et Physiopathologie Articulaire (IMoPA) –Campus Biologie Santé 9, avenue de la Forêt de Haye – CS 50184 54505 VANDOEUVRE LES NANCY Cedex France alexandre.kriznik@univ‐lorraine.fr Unité de Bioinformatique Structurale Institut Pasteur/CNRS 28, rue du Dr. Roux 75015 PARIS France [email protected] LABESSE Gilles LEGER Christophe
Centre de Biochimie Structurale INSERM U1054 – CNRS UMR5048 – UM 29, rue de Navacelles 34090 MONTPELLIER Cedex France [email protected] Bioénergétique et Ingénierie des Protéines CNRS / AMU 31, chemin Joseph Aiguier, 13402 MARSEILLE Cedex 20 France [email protected] 160 LEROUX Gwenaëlle
MARTINY Virginie
Unité Mathématiques et Informatique Appliquées, du Génome à l'Environnement (MaIAGE) – INRA Domaine de Vilvert 78352 JOUY‐EN‐JOSAS France [email protected] Molecular Modelling and Structural Crystallography ICSN – CNRS UPR 2301, LabEx LERMIT Avenue de la Terrasse, Bât. 27, Bureau 310 91198 GIF‐SUR‐YVETTE France [email protected] LIONNE Corinne MATHIEU Cécile
Centre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34293 MONTPELLIER Cedex 5 France [email protected] Unité Biologie Fonctionnelle et Adaptative Université Paris Diderot, CNRS UMR 8251 Bâtiment Buffon 4, rue M.A Lagroua Weill Hallé 75013 PARIS France [email protected] LOUET Maxime MEREU Ilaria
Molécules Thérapeutiques in silico (MTi) Université Paris Diderot – Inserm UMR‐S 973 Bât Lamarck A, 4e et 5e étage , Courrier 7113 35, rue Hélène Brion 75205 PARIS Cedex 13 France [email protected] Multiscale Biomolecular Simulation Group Steinbuch Centre for Computing (SCC) Karlsruhe Institute of Technology (KIT) Hermann‐von‐Helmholtz‐Platz 1 76344 EGGENSTEIN‐LEOPOLDSHAFEN Allemagne [email protected] MANA Zohra MERROUCH Meriem Bio‐Logic SAS 1, rue de l'Europe 38640 CLAIX France zohra.mana@bio‐logic.net Laboratoire de Bioénergétiques et Ingénieries des Protéines UMR 72‐81 CNRS 31, chemin Joseph Aiguier 13402 MARSEILLE cedex 20 France [email protected] MAROT Christophe
MIAS‐LUCQUIN Dominique UMR CNRS 7292 GICC – Equipe 4 Innovation Moléculaire et Thérapeutique UFR Sciences Pharmaceutiques Univ. Tours 31, avenue de Monge 37200 TOURS France Christophe.marot@univ‐orleans.fr Institut de Génétique et Développement de Rennes (IGDR) Université de Rennes 1 2, avenue du Professeur Léon BERNARD 35000 RENNES France [email protected]‐rennes1.fr 161 MOLZA Anne‐Elisabeth NEVEU Emilie
Institut de Génétique et Développement de Rennes‐ Equipe Structure et Interactions Moléculaires (SIM) – Université de Rennes 1 2, avenue du Pr. Léon Bernard CS 34317 35043 RENNES Cedex France [email protected] NANO‐D – INRIA DRT/LETI/DACLE/ Bâtiment 51 C Minatec Campus 17, rue des Martyrs 38054 GRENOBLE Cedex France [email protected] MONET Damien NG FUK CHONG Matthieu Unité de Bioinformatique Structurale Institut Pasteur 25, rue du Docteur Roux 75015 PARIS France [email protected] INSERM UMR_S 1134 DSIMB 6, rue Alexandre Cabanel 75739 PARIS Cedex 15 France matthieu.ng‐fuk‐[email protected] MOROY Gautier NGUYEN Minh Khoa Molécules Thérapeutiques in silico (MTi) Université Paris Diderot – Inserm UMR‐S 973 35, rue Hélène Brion 75205 PARIS Cedex 13 France gautier.moroy@univ‐paris‐diderot.fr Laboratoire Jean Kuntzmann (LJK) INRIA Grenoble Minatec Campus 17, rue des Martyrs 38054 GRENOBLE France minh‐[email protected] MUNIER‐LEHMANN Hélène PETIT Emile
Unité de Chimie et Biocatalyse Institut Pasteur, CNRS UMR3523 Unité de Chimie et Biocatalyse 28, rue du Dr Roux 75724 PARIS cedex 15 France [email protected] UMR 8251 Equipe RMCX Université Denis‐Diderot Bâtiment A 4, rue Lagroua Well Hallé 75013 PARIS France [email protected] NARTH Christophe
RAHIMOVA Rahila
Laboratoire de Chimie Théorique Université Pierre et Marie Currie Barre 12‐13 4ième étage – CC 137 4, place Jussieu 75252 PARIS France [email protected] Centre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34293 MONTPELLIER Cedex 5 France [email protected] 162 RAHUEL‐CLERMONT Sophie RENAULT Nicolas
Laboratoire IMoPA – Equipe Enzymologie Moléculaire et Structurale UMR 7365 CNRS – Université de Lorraine 9, avenue de la Forêt de Haye, CS 50184 54505 VANDOEUVRE‐LES‐NANCY Cedex France sophie.rahuel@univ‐lorraine.fr Therapeutic innovation targetting inflammation – LIRIC – UMR 995 Inserm – Université Lille 2 / CHRU Lille Pôle Recherche, Place Verdun 59045 LILLE Cedex France nicolas.renault‐3@univ‐lille2.fr RASOLOHERY Inès
RIEUX Charlotte
Molécules Thérapeutiques in silico (MTi) Université Paris Diderot INSERM UMR‐S 973 Bât Lamarck A, 4e et 5e étage , Courrier 7113 35, rue Hélène Brion 75205 PARIS Cedex 13 France ines.rasolohery@univ‐paris‐diderot.fr Centre de Biophysique Moléculaire CNRS Rue Charles Sadron 45071 ORLEANS Cedex 2 France charlotte.rieux@cnrs‐orleans.fr REBEHMED Joseph
RINALDO David
IMPMC Université Pierre & Marie Curie Case 115 4, place Jussieu 75252 PARIS Cedex 05 France [email protected] Schrödinger GmbH Zeppelinstraße 73 81669 MÜNCHEN Allemagne [email protected] REDON Stéphane RODRIGUES‐LIMA Fernando NANO‐D – INRIA DRT/LETI/DACLE/ Bâtiment 51 C Minatec Campus 17, rue des Martyrs 38054 GRENOBLE Cedex France [email protected] Unité Biologie Fonctionnelle et Adaptative Université Paris Diderot – CNRS UMR 8251 4, rue MA Lagroua Weill Halle 75013 PARIS France fernando.rodrigues‐lima@univ‐paris‐
diderot.fr REMAUD‐SIMEON Magali SANTOLINI Jérôme
LISBP‐INSAT Insa Toulouse 135, avenue de Rangueil 31077 TOULOUSE Cedex 4 France remaud@insa‐toulouse.fr UMR 8221 CEA/iBiTec‐S/SB2SM Bât. 532 CEA Saclay 91191 GIF‐SUR‐YVETTE Cedex France [email protected] 163 SELWA Edithe STRATMANN Dirk
ICSN CNRS Avenue de la Terrasse Bâtiment 27 91198 GIF‐SUR‐YVETTE France [email protected] IMPMC – UPMC – Paris VI, UMR 7590 CNRS Campus Jussieu, Couloir 22/23, 4ème étage, Bureau 408 – Case courrier 115 4, place Jussieu 75252 PARIS Cedex 05 France [email protected] SENAC Caroline STRICK Terence
Laboratoire d'imagerie biomédicale (LIB) UPMC CNRS INSERM 4, place Jussieu 75005 PARIS France [email protected] Equipe Nanomanipulation de Biomolécules Institut Jacques Monod 15, rue Hélène Brion 75013 PARIS France [email protected]‐paris‐diderot.fr SIDORE Marlon TALFOURNIER François Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM) CNRS/Université Aix‐Marseille 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected] IMoPA UMR 7365 CNRS – Université de Lorraine 34, cours Léopold 54000 NANCY France francois.talfournier@univ‐lorraine.fr SOPKOVA‐DE OLIVEIRA SANTOS Jana
TALY Antoine
CERMN Université de Caen Basse‐Normandie Bd Becquerel 14032 CAEN France [email protected] Laboratoire de Biochimie Théorique UPR 9080 Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected] STINES‐CHAUMEIL Claire TELLIER Charles
Centre de Recherche Paul Pascal CNRS UPR 8641 115, avenue Albert Schweitzer 33600 PESSAC France stines@crpp‐bordeaux.cnrs.fr UFIP, UMR CNRS n°6286 Université de Nantes 2, rue de la Houssinière BP 92208 44322 NANTES Cedex 3 France charles.tellier@univ‐nantes.fr 164 THOMAS Aline VERZEAUX Laurie
Département de Pharmacochimie Moléculaire CNRS – UFR de Pharmacie 470, rue de la Chimie – BP 53 38041 GRENOBLE Cedex 9 France aline.thomas@ujf‐grenoble.fr Laboratoire SiRMa UMR CNRS/URCA MEDyC n°7369 UFR SEN – Bâtiment 18 Chemin des rouliers 51687 REIMS Cedex 2 France [email protected] TINTILLIER Thibault
WEIN‐GRATRAUD Sharon IBMM Faculté de Pharmacie 15, avenue Charles Flahault BP 14491 34093 MONTPELLIER Cedex 5 France thibault.tintillier@univ‐montp1.fr Dynamique des Interactions Membranaires Normales et Pathologiques – UMR5235 CNRS‐Université de Montpellier Place Eugène Bataillon – Bât 24 – cc107 34095 MONTPELLIER Cedex 5 France wein@univ‐montp2.fr TRAORE Seydou WIENINGER Silke
CEA Saclay – IBITEC‐S/SB2SM/LBSR Point courrier 22 Bâtiment 144 91191 GIF‐SUR‐YVETTE Cedex France [email protected] Unité de Bioinformatique Structurale Institut Pasteur Paris 25‐28, rue du Dr Roux 75015 PARIS France [email protected] TUBIANA Thibault
Institut de Biologie Intégrative de la Cellule I2BC CNRS CEA Saclay – Bât 532 91191 GIF‐SUR‐YVETTE Cedex France [email protected] 165 PROGRAMME
Lundi 25 Mai
Mardi 26 Mai
Accueil
PLENIERE 2
Terence STRICK
Hélène MUNIER‐LEHMANN
Nicolas BOSC
Jessica HADJ‐SAID
Pause café
9h
10h
11h
Loic CARRIQUE
Sophie RAHUEL‐CLERMONT
Marc‐André DELSUC
Déjeuner
12h
Déjeuner
Mercredi 27 mai
Enz. comme cibles thérap.
PLENIERE 4
Dominique DOUGUET
Flash n°4
Kamel DJAOUT
Fanny KREBS
Jana SOPKOVA‐DE OLIVEIRA
Pause café
Jeudi 28 Mai
Visualisation et préd. struct.
PLENIERE 6
Tobias ISENBERG
Fabrice ALLAIN
Mathias FERBER
Emilie NEVEU
I. CHAUVOT DE BEAUCHENE
Pause café
Flash n°5
Florent BARBAULT
Inès RASOLOHERY
Hiba ABI HUSSEIN
Prix poster GT Enzymes
Déjeuner
Jean‐Christophe GELLY
Stéphane REDON
Prix GGMM
Isidro CORTES
Prix poster GGMM
Déjeuner
13h
14h
15h
16h
17h
18h
Bienvenue GGMM
Bienvenue GT Enzymes
Structures et mécanismes
PLENIERE 1
Jacqueline CHERFILS
Jérôme SANTOLINI
Nolan CHATRON
Bertrand CASTAING
Pause café
Pause café
Flash n°1
Ewelina GUCA
Pierre LAFITE
Claire STINES‐CHAUMEIL
Jean‐Christophe LEC
Présentation Elsevier
Apéritif de bienvenue
Posters (1h30)
Ingénierie enz., évol. dirigée
PLENIERE 3
Donald HILVERT
Flash n°2
Thomas GAILLARD
Seydou TRAORE
David RINALDO
Flash n°3
Flash n°3
Pause café
Posters (1h45)
Rel. dynamique‐fonction
PLENIERE 5
Jochen BLUMBERGER
Jérôme EBERHARDT
Benoît DAVID
Julien DIHARCE
Maxime LOUET
Présentation Jeu "L'Expert"
Pause café
Pause café
Matthieu CHAVENT
Kaouther BEN OUIRANE
Jean‐Pierre DUNEAU
Christophe NARTH
AG GGMM
Apéritif Sétois
Dégustation
produits régionaux
Posters (1h30)
Diner
Diner
19h
Diner
20h
Soirée festive
21h
Jeu "L'Expert est dans la salle"
Durées présentations
Plénière : 45 min
Flash : 1 min
Com. orale : 15 min
LEGENDE
Présentations Enzymes
Présentations communes
Présentations communes
Présentations GGMM
Flash poster
Sessions posters

GT Enzymes 2015 GGMM 2015 - 4ème Congrès du GT Enzymes

Transcription

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