Formacion de complejos entre proteinas: prediccion estructural

Transcription

Magíster Bioinformática y Biología Computacional (UCM)
2012/13
PREDICCION DE ESTRUCTURAS
Docking entre Proteínas
Juan Fernández-Recio
Barcelona Supercomputing Center (BSC)
[email protected]
• Introduction to protein-protein interactions
• Geometry-based docking
• Energy-based docking and scoring
• Flexible docking
• Evaluation of docking methods and applications
• Future challenges
1
Gtggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgccgatgtccatgacattg
caaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatgacttcttcccgactct
cgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcaccatccgcgacatcatt
gaaccgcgcggtgcaaccatcgttggtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagag
ttgcatctcgacgaaattctcaatgcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttg
gtaaagacgttgccgatgtccatgacattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgca
gtgtgactgggatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgc
gacgcattgggcaccatccgcgacatcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatg
acgaccactttgtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacga
aattctcaatgcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgcc
gatgtccatgacattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatg
acttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcac
catccgcgacatcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatgacgaccactttgtc
ggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacgaaattctcaatgcct
gaatggctatcactggcatctatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatgg
ctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgtctgctgctgggcatcccaacctggtattacggcgaagcgcagt
gtgactgggatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgacgatggctatca
ctggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgccgatgtccatgacattgcaaaaagcag
caaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatgacttcttcccgactctcgaagagatt
gatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcaccatccgcgacatcattgaaccgcgcg
gtgcaaccatcgttggtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcg
acgaaattctcaatgcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgt
tgccgatgtccatgacattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgg
gatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgg
gcaccatccgcgacatcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatgacgaccactt
tgtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacgaaattctcaat
gcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgccgatgtccatg
acattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatgacttcttccc
gactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcaccatccgcgac
atcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatgacgaccactttgtcggtctggcta
tcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacgaaattctcaatgcctgaatggctat
cactggcatctatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgg
catctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgtctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactggga
tgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgcgaaattctcaatgcctgaatgg
ctatcactggcatctatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatca
ctggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgtctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgact
gggatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgcccgaaattctcaatgcctgaa
2
Gtggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgccgatgtccatgacattg
caaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatgacttcttcccgactct
cgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcaccatccgcgacatcatt
gaaccgcgcggtgcaaccatcgttggtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagag
ttgcatctcgacgaaattctcaatgcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttg
gtaaagacgttgccgatgtccatgacattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgca
gtgtgactgggatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgc
gacgcattgggcaccatccgcgacatcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatg
acgaccactttgtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacga
aattctcaatgcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgcc
gatgtccatgacattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatg
acttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcac
catccgcgacatcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatgacgaccactttgtc
ggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacgaaattctcaatgcct
gaatggctatcactggcatctatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatgg
ctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgtctgctgctgggcatcccaacctggtattacggcgaagcgcagt
gtgactgggatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgacgatggctatca
ctggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgccgatgtccatgacattgcaaaaagcag
caaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatgacttcttcccgactctcgaagagatt
gatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcaccatccgcgacatcattgaaccgcgcg
gtgcaaccatcgttggtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcg
acgaaattctcaatgcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgt
tgccgatgtccatgacattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgg
gatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgg
gcaccatccgcgacatcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatgacgaccactt
tgtcggtctggctatcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacgaaattctcaat
gcctgaatggctatcactggcatctttttcggcagcgacaccggtaataccgaaaatatcgcaaaaatgattcaaaaacagcttggtaaagacgttgccgatgtccatg
acattgcaaaaagcagcaaagaagatctggaagcttatgacattctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactgggatgacttcttccc
gactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgaatatttctgcgacgcattgggcaccatccgcgac
atcattgaaccgcgcggtgcaaccatcgttggtcactggccaactgcgggctatcatttcgaagcatcaaaaggtctggcagatgacgaccactttgtcggtctggcta
tcgacgaagaccgtcagccggaactgaccgctgaacgtgtagaaaaatgggttaaacagatttctgaagagttgcatctcgacgaaattctcaatgcctgaatggctat
cactggcatctatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgg
catctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgtctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgactggga
tgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgccgcgaaattctcaatgcctgaatgg
ctatcactggcatctatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatca
ctggcatctttttcggcaatggctatcactggcatctttttcggcaatggctatcactgtctgctgctgggcatcccaacctggtattacggcgaagcgcagtgtgact
gggatgacttcttcccgactctcgaagagattgatttcaacggcaaactggttgcgctgtttggttgtggtgaccaggaagattacgcccgaaattctcaatgcctgaa
Biosphere
DNA
3
Cell biology
Protein function
Interaction
with other
molecules
Cells
Cell Biology
Physiology
Protein 3D structure
Organisms
Folding and
stability
Ecology
Protein sequence
Biosphere
Genetic
code
DNA
RNA
transcription
human 650M PPI
fly 75M PPI
yeast 38K PPI
Cell biology
Protein function
Interaction
with other
molecules
Cells
Cell Biology
Physiology
Protein 3D structure
human 25K prot
fly 13K prot
yeast 7K prot
Folding and
stability
Protein sequence
Organisms
Ecology
Biosphere
Genetic
code
RNA
transcription
DNA
4
Study of Protein-Protein Interactions
Biophysical Analysis
Protein Interaction Detection
Applications
P4A
P4
P2A
- NMR (chemical shifts)
P3
P2
P1A
P
6
P7
P1
P5
- sequence conservation
- binding essays
- mutants & alanine-scanning
P8
P9
P10
P1B
P9A
P9B
- two-hybrid test
- affinity column, gel assays...
- BIAcore
- mass-spectrometry
- electron microscopy
- cross-linking
- co-immunoprecipitation
- immunofluorescence
- knock-out
- phylogenetic profiles, gene fusion events...
- ...
Structural Characterization
- protein design
- inhibitor discovery:
peptide mimicking
ligand docking
VLS
- association mechanism
- X-ray
- NMR
- druggable pockets
5
Structural Analysis at Atomic Resolution:
NMR and X-ray
Types of protein-protein interactions
PERMANENT / NON-OBLIGATORY
HOMO- / HETEROOBLIGOMERS / COMPLEXES
Jones & Thornton (1996) PNAS, 93, 13
Ofran & Rost (2003) JMB, 325, 377
HOMO- / HETERO-OLIGOMERIC
NON-OBLIGATE / OBLIGATE
TRANSIENT / PERMANENT
Nooren & Thornton (2003) EMBO J, 22, 3486
DOMAIN-DOMAIN INTERFACE CLASSIFICATION
PERMANENT / TRANSIENT, SYMMETRIC...
Kim et al. (2006) PLOS Comp Biol, 2, e124 (http://www.scoppi.org)
6
Types of protein-protein interactions
MULTI-MOLECULAR ASSEMBLIES
3Dcomplex, http://supfam.mrc-lmb.cam.ac.uk/elevy/3dcomplex/Home.cgi
Human interactome
130-650K estimated interactions*
25K confirmed interactions#
(2K with known structure)
homology
?
3K can be modelled
In silico
structural
prediction
* Venkatesan et al 2009 Nature Methods 6, 83-90
Stumpf et al 2008 PNAS 105, 6959-6964
20K
# Stein, Mosca & Aloy 2011 COSB 21, 200
7
Protein-Protein Docking
Generation of the structure of a protein-protein complex
from the individual protein structures
Motivation …
- X-ray, NMR: Determination of complex structures remains difficult
- Low-resolution data on PPI available (cryo-EM, MS…)
- Understand energetics and mechanism of protein-protein association
- Protein design (diagnostic, environment) and drug discovery
8
docking
9
10
11
Protein-protein docking – major methods
Exhaustive search
(FFT)
Stochastic sampling
(Monte-Carlo, minimization)
Geometric docking
Global energy optimization
12
Rigid-Body Docking:
Geometry Approach
Rigid-Body Docking:
Geometry Approach
13
Protein Docking Using FFT
R
Discretize
Fast Fourier
Transform
R
R
Complex
Conjugate
Correlation function
L
Rotate
Discretize
L
L
Fast Fourier
Transform
Surface
Interior
Protein Docking Using FFT
Correlation
Comp. cost can decrease by >104 (from N6 to N3lnN3)
Surface
Y Translation
IFFT
IFFT
R
L
Interior
X Translation
14
FTDOCK
ZDOCK
Molecular Shape Recognition: Hex
2D Spherical Harmonic Surfaces
15
Geometrical hashing - PatchDock
Schneidman-Duhovny et al. Proteins 2003
Duhovny (Schneidman), D., Nussinov, R. Wolfson,HJ. WABI 2002
Multi-protein docking: CombDock
?
16
Symmetric docking: SymmDock
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ Proteins 05
17
Protein-Protein Docking Energy
E
0
RMSD
18
E = Evw + Eel + Ehb + Ehp
(ECEPP/3)
E
‘soft’ vdW =
el
 332.0
qis q j
2
4d ij
Max E el= 20 kcal/mole
Min E el = -20 kcal/mole
= 0.03 kcal/mole * ASA(apolar)
RosettaDock
Random Start
Position
Low-Resolution
Monte Carlo Search
High-Resolution
Refinement
105
Clustering
Predictions
19
HADDOCK
HADDOCK
20
RECEPTOR
LIGAND
Calculate
maps
ICM-DISCO protein docking
Positioning
(x 120)
Pseudo-Brownian
Monte-Carlo Minimization
RIGID BODY
DOCKING
Energy funct =
vdw + el + hb + desolv
Monte Carlo sampling
(positional)
Local minimization
NO
Solution
rejected
Metropolis
criteria
20000 energy
evaluations
YES
Solution
accepted
Low energy solutions
RMSD > 4 Å
Conformational stack
Fernández-Recio et al. 2002 Protein Sci. 11, 280-291
pyDock: FFT sampling + energy scoring
80 protein-protein cases
R
R
R
L
L
C ( x, y, z )    f h,k ,l  g h  x ,k  y ,l  z 
n
n
FTDOCK’s docking sets
n
FFT
h 1 k 1 l 1
ZDOCK’s docking sets
REC LIG
E  0.1
i
j
 r  r
i
j
ei e j  
 d
ij



r r
  2 i j

 d

 ij
qi q j
REC LIG
 332.0 
i
12
4d ij2
j
REC
LIG
i
j




6





  ADPi BSAi   ADPj BSA j
pyDock – Cheng, Blundell, Fernandez-Recio (2007) Proteins 68, 503-515
21
22
?
23
Docking success depends on flexibility
Pons et al. (2010) Proteins 78, 95-108
Docking unbound trypsin/BPTI
Prot. Sci., 2002
Docking
unbound
Docking
complexed
subunits
Distribution of
solutions
Real complex
Best docking
solution
Correct
solution
24
Protein-protein binding mechanism
1894: LOCK AND KEY: E. FISCHER
1958: INDUCED FIT: D. E. KOSHLAND
1999: CONFORMATIONAL SELECTION: R. NUSSINOV
- RIGID-BODY DOCKING
- small: RIGID-BODY + REFINEMENT
- large: FLEXIBLE DOCKING SEARCH
- PRECOMPUTED ENSEMBLES
25
RECEPTOR
Calculate
maps
LIGAND
ICM-DISCO protein docking
SIDE-CHAIN
REFINEMENT
Positioning
(x 120)
Energy =
vdw + el + hb +
desolv
RIGID BODY
DOCKING
(ligand interface side-chains)
Local minimization
NO
Metropolis
criteria
(positional)
Local minimization
Solution
rejected
YES
NO
20000 energy
evaluations
Metropolis
criteria
Solution
accepted
Solution
rejected
1000 energy evaluations
per flexible torsion angle
YES
Solution
accepted
Low energy solutions
RMSD > 4 Å
Conformational stack
Fernández-Recio et al. 2002 Protein Sci. 11, 280-291
26
ICM Interface Side-Chain Optimization
Prot. Sci., 2002
Grid
refinement
Algorithm:
ICM-BP-SGO of side-chains
and positional variables
Docking
unbound
Docking
complexed
subunits
Distribution of
solutions
Real complex
Best docking
solution
After refinement
Correct
solution
RosettaDock
Random Start
Position
Low-Resolution
Monte Carlo Search
High-Resolution
Refinement
105
Clustering
Predictions
27
HADDOCK
FireDock
Fast Interaction Refinement in Molecular Docking
Rigid-Body Docking
Side-Chain Optimization
1
Rigid-Body Optimization
2
Rigid-body
candidates
Refinement
Complex Hypotheses
Ranking
.
.
.
28
FlexDock
Detection of Hinges and Rigid
Parts in the Flexible Molecule
part1
part2
part3
part1
Rigid Parts Docking via
Geometric Hashing
A
A
…
Assembly of partial dockings
into a flexible result
29
ATTRACT
30
Precomputed ensembles + docking
31
Precomputed ensembles + docking
32
Protein-protein benchmarking
2002-2010 Weng’s benchmark series
A Novel Shape Complementarity Function
100%
Grid-based shape complementarity (GSC)
GSC+Desolvation+Electrostatics
Pairwise shape complementarity (PSC)
PSC +Desolvation+Electrostatics
90%
80%
70%
Success Rate
0.0: 54 cases
1.0: 59 cases
2.0: 84 cases
3.0: 124 cases
4.0: 176 cases
60%
50%
40%
30%
20%
10%
0%
1
10
100
1000
Number of Predictions
http://zlab.umassmed.edu/benchmark/
DOCKING VALIDATION
CAPRI: A Critical Assessment of PRedicted Interactions
http://www.ebi.ac.uk/msd-srv/capri/
1st CAPRI – Sep02 La Londe (France)
Special issue, in:
PROTEINS: Structure, Function, and Genetics 52
(July 2003)
T01
Hpr (unbound)
HPr kinase (unbound)
2nd CAPRI – Dec04 Gaeta (Italy)
Special issue, in:
PROTEINS: Structure, Function, and Bioinformatics 60
(July 2005)
T02
VP6 (unbound)
Fab (bound)
3rd CAPRI – Apr07 Toronto (Canada)
T03
Hemagglutinin (unbound)
Fab (bound)
T04, T05, T06
a-amylase (unbound)
VHH (bound)
T07
TCRb (unbound)
speA (unbound)
Special issue, in:
PROTEINS: Structure, Function, and Bioinformatics 69
(December 2007)
4th CAPRI – Dec10 Barcelona (Spain)
Special issue, in:
PROTEINS: Structure, Function, and Bioinformatics
(November 2010)
33
1st CAPRI - Predictions
…
6 groups:
2 acceptable models
3 groups:
1 acceptable models
5 groups:
no acceptable models
Fernandez-Recio et al. (2003) Proteins 52, 113-117
2nd CAPRI - Predictions
T08
7.6Å
T10
8.5Å
T11
T12
6.0Å
0.7Å
4.1Å
3.0Å
T13
11.1Å
T14
T18
T19
0.6Å
Fernandez-Recio et al. (2005) Proteins 60, 308-313
34
CAPRI 1-3: summary of results
Weng 61%
F-Recio/Totrov/Abagyan 57%
Bonvin 57%
4th CAPRI – Barcelona 2009
X-ray structure
5.9 Å
4.1 Å
scorers
predictors
almost hit
3.8 Å
12.3 Å
5.2 Å
2.5 Å
T29
10.3 Å
Best in
scorers!
T32
Fnat 45%
T34
2nd best in
scorers!
T35
8.1 Å
Best (only one)
in predictors!
5.5 Å
2.3 Å
T39
T40
T41
T42
35
Target 29
(bound / unbound)
Receptor
TRM82
(bound)
Ligand
TRM8
(unbound)
PDB complex 2VDU
Predictors (total 41 groups):
0.1*vdw
PyDock Model 1 ( ** medium)
40% native contacts
Ligand RMSD: 5.9Å; Interface RMSD 1.9Å
[4th best of any group !!]
From zdock
Scorers (total 18 groups):
0.1*vdw
PyDock Model 4 (*** high) [best of any group !!]
71% native contacts
Ligand RMSD: 2.5Å; Interface RMSD 1.0Å
Target 40 (unbound / bound)
Receptor
PDB complex 3E8L
Bovine trypsin
PDB 1BTY
(unbound)
Ligand
API-A
(bound)
Predictors (total 38 groups):
Incorrect restraints !!!
0.1*vdw
Energy+Restraints (inhibitor L87 AND K145)
PyDock Model 2 ( incorrect)
Ligand RMSD: 12.5Å
Energy+Restraints: inhibitor L87 OR K145
PyDock Model 8 (* acceptable?)
Scorers (total 14 groups):
0.1*vdw
Energy+Restraints (inhibitor L87 AND K145)
PyDock Model 6 (incorrect)
Ligand RMSD: 12.5Å
Incorrect restraints !!!
Energy+Restraints: inhibitor L87 OR K145
PyDock Model 1 (* acceptable)
36
4th CAPRI
10th out
of 64
groups
21 other groups
0
5th CAPRI (ongoing)
60%
success
rate
3th out of
58
groups
37
pyDock protocol for CAPRI
PREDICTORS
10K
FTDock
Rigid-body
poses
Ele+Des+(0.1*vdw)
2K
ZDock
pyDockRST
Int residues?
~10-50K
RotBUS
uploaders
SCORERS
NIP
Clustering
4Å (10Å)
10 models
ODA
vdw?
4Å? 10Å?
Submit to CAPRI
tinker
Fernandez-Recio et al. (2004)
JMB 335, 843-865
38
The challenge of size
The challenge of flexibility
39
The challenge of low-affinity complexes
The challenge of using models for docking
84 protein-protein
complex structures
>30% seq ID
36 cases with
PDB template
30-50% seq ID
23 cases with
PDB template
50-70% seq ID
12 cases with
PDB template
70-90% seq ID
Modeller
80
70
70
X-ray subunits
60
50
Modeled subunits
40
50
30
20
100
150
200
250
RANK
300
350
400
450
500
70
60
Modeled subunits
X-ray subunits
50
40
30
20
random
10
10
0
50
Modeled subunits
40
20
random
80
60
30
10
90
X-ray subunits
SUCCESS RATE [%]
90
80
SUCCESS RATE [%]
SUCCESS RATE [%]
90
0
100
100
100
random
50
100
150
200
250
RANK
300
350
400
450
0
500
50
100
150
200
250
RANK
300
350
400
450
500
40
The challenge of multi-protein complexes
Docking 1:1
Stalk
Multi-protein
docking ??
The challenge of identifying interactions
Significant correlation of pyDock with
experimental binding energies
r = 0.762
?
Data from Kastritis & Bonvin 2010
Identifying PPIs from docking profiles
Wass et al. 2011 MolSysBiol 7, 469
41
The ultimate challenge: inhibiting protein-protein interactions
Approx. 80% of small organic molecules approved for human use by
the FDA target cell-surface receptors and enzymes
Only 10–15% of all human proteins are currently «druggable»
Network medicine
Biomedical applications of cell networks
(e.g. Pujol et al, TiPS, 2010; Sardón et al, EMBO Rep, 2010; Soler-López et al, Genome Res, 2011)
Protein-protein docking in drug discovery
42
Bibliography
Protein-Protein Interaction - General
- Protein-Protein Complexes, M. Zacharias ed., Imperial College Press
- Protein-Protein Recognition, C. Kleanthous ed., Oxford University Press
- Conte et al. (1999) J. Mol. Biol 285, 2177-2198
Docking Simulations
- Computational Protein-Protein Interactions, R. Nussinov, G. Schreiber ed., CRC Press
- Katchalski-Katzir et al. (1992) PNAS 89, 2195-2199
- Halperin et al. (2002) Proteins 47, 409-443
- Smith & Sternberg (2002) Curr. Opin. Struct. Biol. 12, 28-35
- Bonvin (2006) Curr. Opin. Struct. Biol. 16, 194
- Gray (2006) Curr. Opin. Struct. Biol. 16, 1
- Ritchie (2008) Curr. Protein Pept. Sci. 9, 1-15
- Andrusier et al. (2008) Proteins 73, 271
- May et al. (2008) Curr. Comput. Aided Drug Des. 4, 143-153
CAPRI
- Proteins Special Issues (July 2003, July 2005, December 2007, November 2010)
“Protein Interactions and Docking” group
Barcelona Supercomputing Center
[email protected]
- Proyecto Master
- Doctorado en biomedicina
43

Formacion de complejos entre proteinas: prediccion estructural

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