MHC polymorphism - CBS

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CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS
MHC polymorphism
Funcional clustering of MHC molecules: The concept of supertypes
Technical University of Denmark - DTU
Department of systems biology
Polymorphism of MHC
• 
Within a host limited number of loci (genes)
• 
only 6 different class I molecules (two A, B and C)
• 
only 12 different class II molecules
Within a population > 100 alleles per locus
Immunological Bioinformatics, 2010
• 
•  The
IMGT/HLA Sequence Database currently encompass
more than 1500 HLA class I proteins
Source: http://www.anthonynolan.com/HIG/index.html
HLA polymorphism
~1% probability that an MHC molecule binds a peptide
Different hosts sample different peptides from same
pathogen.
More MHC molecules: more diversity
in the presented peptides
Seq2Logo: http://www.cbs.dtu.dk/biotools/Seq2Logo Immunological Bioinformatics, 2010
HLA-A*02:01 sequence logo
HLA specificity overlap
A0101
A6802
B0702
A0201
MHC Supertypes
•  Tertiary (structure) •  Shared peptide binding motifs
•  Identification of cross-reacting peptides
•  Many of the different HLA molecules have similar
specificities
•  HLA molecules with similar specificities can be
grouped together
•  Methods to define supertypes
•  Structural similarities
•  Primary (sequence)
Each HLA molecule within a supertype binds essentially the
Sette et al, Immunogenetics
same peptides
(1999) 50:201-212
A6802
A0201
HLA polymorphism – the supertype hypothesis
LOGOS OF
HLA-A
ALLELES
A2
e
p
y
t
r
e
p
su
O Lund et al., Immunogenetics. 2004 55:797-810
Clustering
O Lund et al., Immunogenetics. 2004 55:797-810
Phenotypic frequencies
Supertypes
Caucasian
African
Japanese
Chinese
Hispanic
Average
A2, A3, B7
83%
86%
88%
88%
86%
86%
+ A1, A24,
B44
100%
98%
100%
100%
99%
99%
+B27, B58,
B62
100%
100%
100%
100%
100%
100%
Sette et al, Immunogenetics (1999) 50:201-212
HLA polymorphism - frequencies
=> A CTL based vaccine must consist of 6-9 HLA class I epitopes Sette et al, Immunogenetics (1999) 50:201-212
HLA polymorphism - supertypes
The truth about supertypes!
A3
A26
A1
A24
A2
HLA polymorphism!
Data
1000
800
600
400
200
0
HLA-A
HLA-B
HLA-C
681
1165
569
SYFPEITHI
27
59
4
IEDB
34
28
0
Proteins
•  Alleles characterized with 5 or more data points
•  3% covered
1200
How to fill this gap?
Using alignment
A68:01
A68:02
A68:01
A68:02
A68:01
A68:02
A68:01
A68:02
A68:01
A68:02
Align A68:01 (365) versus A68:02 (365). Aln score 2454.000 Aln len 365 Id 0.9863
A68:01
0 MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFYTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAA
::::::::::::::::::::::::::::::::::: :::::::::::::::::::::::::::::
A68:02
0 MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFYTSMSRPGRGEPRFIAVGYVDDTQFVRFDSDAA
65 SQRMEPRAPWIEQEGPEYWDRNTRNVKAQSQTDRVDLGTLRGYYNQSEAGSHTIQMMYGCDVGSD
::::::::::::::::::::::::::::::::::::::::::::::::::::::: ::::::: :
65 SQRMEPRAPWIEQEGPEYWDRNTRNVKAQSQTDRVDLGTLRGYYNQSEAGSHTIQRMYGCDVGPD
130 GRFLRGYRQDAYDGKDYIALKEDLRSWTAADMAAQTTKHKWEAAHVAEQWRAYLEGTCVEWLRRY
::::::: : :::::::::::::::::::::::::::::::::::::::::::::::::::::::
130 GRFLRGYHQYAYDGKDYIALKEDLRSWTAADMAAQTTKHKWEAAHVAEQWRAYLEGTCVEWLRRY
195 LENGKETLQRTDAPKTHMTHHAVSDHEATLRCWALSFYPAEITLTWQRDGEDQTQDTELVETRPA
:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
195 LENGKETLQRTDAPKTHMTHHAVSDHEATLRCWALSFYPAEITLTWQRDGEDQTQDTELVETRPA
260 GDGTFQKWVAVVVPSGQEQRYTCHVQHEGLPKPLTLRWEPSSQPTIPIVGIIAGLVLFGAVITGA
:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
260 GDGTFQKWVAVVVPSGQEQRYTCHVQHEGLPKPLTLRWEPSSQPTIPIVGIIAGLVLFGAVITGA
325 VVAAVMWRRKSSDRKGGSYSQAASSDSAQGSDVSLTACKV
::::::::::::::::::::::::::::::::::::::::
325 VVAAVMWRRKSSDRKGGSYSQAASSDSAQGSDVSLTACKV
Sequence based clustering
0.01
B15_01
B58_01
A24_02
A26_01
A02_01
A68_01
A68_02
B40_01
B39_01
B07_02
B08_01
A03_01
A01_01
B27_05
Sequence logos
HLA-A*6801
HLA-A*6802
Pan-specific method
•  The contact residues are defined as being within 4.0
Å of the peptide in any of a representative set of
HLA-A and -B structures with nonamer peptides. • 
• 
Only polymorphic residues from A, B, and C alleles
are included Pseudo-sequence consisting of 34 amino
acid residues. Technical University of Denmark - DTU
•  Include polymorphic residues in potential
contact with the bound peptide
Example
Amino acids of HLA pockets
YFAVLTWYGEKVHTHVDTLVRYHY
YFAVWTWYGEKVHTHVDTLLRYHY
YFAEWTWYGEKVHTHVDTLVRYHY
YYAVLTWYGEKVHTHVDTLVRYHY
YYAVWTWYRNNVQTDVDTLIRYHY
HLA
A0201
A0201
A0201
A0201
A0201
A0201
A0202
A0203
A0206
A6802
Aff
0.131751
0.487500
0.364186
0.582749
0.206700
0.727865
0.706274
1.000000
0.682619
0.407855
Peptide
VVLQQHSIA
SQVSFQQPL
SQCQAIHNV
LQQSTYQLV
LQPFLQPQL
VLAGLLGNV
VLAGLLGNV
VLAGLLGNV
VLAGLLGNV
VLAGLLGNV
Predictions for novel HLA alleles
How good are the predictions?
Leave-one-out validation
Close neighbors can help you!
Leave One out performance
HLA-A02:01 versus HLA-A68:02
PCC: 0.61
0.8
0.6
HLA-A6801
A6802
0.032
0.019
0.118
0.025
0.038
0.028
0.021
0.038
0.117
0.039
0.140
0.026
0.048
0.017
0.020
0.025
0.154
0.035
0.011
0.023
0.4
0.2
0
0
0.2
0.4
HLA-A0201
0.6
0.8
ISCDEGRFK
TDRAAQTRE
IAPLRMSAT
KPAFKTGEE
GVERHIHIF
TYGWAWLLK
AEDIAKTVA
MSGNEIYDH
EDVERGQVV
ILVEHARVE
QKPTLTVML
AQKTIEWAQ
VEHPNVYKM
EERASSSKN
EDRKGHDRR
LQGTTDVTP
NIGVILLLT
MRLAHDPDA
GEYLKEKIR
IPRCSPPPP
A0201
0.022
0.013
0.065
0.019
0.060
0.036
0.034
0.025
0.028
0.066
0.055
0.037
0.060
0.013
0.014
0.032
0.171
0.055
0.019
0.015
HLA-A02:01 versus HLA-A68:01
PCC: 0.09
Heatmaps and binding motifs
HLA-A68:02
HLA-A02:01
HLA-A68:01
HLA-A03:01
HLA−A02:01
HLA−A68:02
HLA−A68:01
HLA.A02.01
HLA.A68.02
HLA.A68.01
HLA.A03.01
HLA−A03:01
d = 1− PCC(A, B)
The MHCcluster server
The MHCcluster server
Specificity-based clustering
HLA-B58_01
HLA-A24_02
Clustering of the 12 HLA supertypes
HLA-A01_01
HLA-B15_01
HLA−B58:01
HLA−B15:01
HLA-A26_01
HLA−A01:01
HLA−A26:01
HLA-A68_02
HLA−A24:02
HLA−A68:02
HLA-A02_01
HLA−A02:01
HLA−B08:01
HLA-A03_01
HLA−B07:02
HLA−B40:01
HLA-A68_01
HLA-B27_05
HLA−B39:01
HLA−B27:05
HLA.B58.01
HLA.B15.01
HLA.A01.01
HLA.A26.01
HLA.A24.02
HLA.A68.02
HLA.A02.01
HLA.B08.01
HLA.B07.02
HLA.B40.01
HLA.B39.01
HLA.B27.05
HLA-B07_02
HLA.A68.01
HLA-B39_01
HLA−A03:01
HLA.A03.01
HLA-B40_01
HLA−A68:01
HLA-B08_01
Specificity-based clustering (w logos)
HLA-A01_01
HLA-B15_01
HLA-A26_01
HLA-A68_02
HLA-A02_01
HLA-A03_01
HLA-A68_01
HLA-B27_05
HLA-B40_01
HLA-B39_01
HLA-B08_01
HLA-B07_02
HLA-B58_01
HLA-A24_02
Mice and men
H−2−Kb
HLA−B27 05
HLA−A24 02
H−2−Kd
H−2−Dd
HLA−B27:05
HLA−B39:01
H−2−Db
H−2−Kk
HLA−B40:01
HLA−B40 01
HLA−B07:02
1.00
H−2−Kk
H−2−Ld
0.99
1.00
1.00
1.00
1.00
0.84
0.80
1.00
1.00
0.82
0.86
0.99
HLA−B39 01
HLA−B08:01
HLA−A01 01
H−2−Kb
H−2−Db
H−2−Dd
HLA−B58 01
H−2−Kd
HLA−A24:02
HLA−B08 01
1.00
HLA−A01:01
HLA−A26 01
HLA−B58:01
HLA−A26:01
HLA−B15 01
HLA−B15:01
H−2−Ld
HLA−B07 02
HLA−A02:01
HLA.B27.05
H.2.Kk
HLA.B39.01
HLA.B40.01
HLA.B07.02
H.2.Ld
H.2.Kb
HLA.B08.01
H.2.Db
H.2.Kd
H.2.Dd
HLA.A24.02
HLA.A01.01
HLA.B58.01
HLA.A26.01
HLA.B15.01
HLA.A02.01
HLA−A03 01
HLA.A03.01
HLA−A03:01
HLA−A02 01
Text
Zhang et al. Bioinformatics 2008
Other pan-specific
methods
Work by Ilka Hoff
And now to animals
Are chimpanzees like Human?
•  Can
we predict binding specificities from
non-human primates using NetMHCpan
method trained on human specificity data
only?
Predicting Primate MHC
Sidney et al. (2006)
Sidney et al. (2006)
Non-human primates
Patr B*0101
Patr A*0101
Pig
Known BoLA class I epitopes
MHC N Predic1on HD6 2138 0.070 T2b 662 0.133 T2b 662 0.328 T2a 662 0.017 Average predicted
T2c of 12 662 0.036 rank
CTL BoLA
T2a 662 0.036 restricted
epitopes
is
3%
T2a 662 0.104 W10 2282 0.086 T5 586 0.017 T7 2850 0.058 AW10 1726 0.168 D18.4 1302 0.113 0.097 Phil Toye and Vish Nene, ILRI
Epitope Protein VGYPKVKEEML Tp1 SHEELKKLGML Tp2 DGFDRDALF Tp2 KSSHGMGKVGK Tp2 FAQSLVCVL Tp2 QSLVCVLMK Tp2 KTSIPNPCKW Tp2 TGASIQTTL Tp4 SKADVIAKY Tp5 EFISFPISL Tp7 CGAELNHFL Tp8 AKFPGMKKSK Tp9 Ave alterna1ve Predic1on VGYPKVKEEML 0.070 EELKKLGML 0.009 LEGDGFDRDAL 0.012 KSSHGMGKVGK 0.017 FAQSLVCVL 0.036 QSLVCVLMK 0.036 KTSIPNPCK 0.018 ATGASIQTTL 0.023 SKADVIAKY 0.017 FISFPISL 0.002 GAELNHFLTL 0.009 AKFPGMKKSK 0.113 0.030 Technical University of Denmark - DTU
MHCMotifViewer
MHCMotifViewer. Rapin et al. Immunogenetics. 2008
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
MhcMotifViewer
MhcMotifViewer
Unexpected similarities
Conclusions
give a simple but often wrong
approximation of MHC specificities
•  Specificity
clustering provides a more precise cluster of
MHC molecules
•  Pan-specific prediction methods can capture the subtle
differences in binding specificity between MHC
molecules
•  MHCcluster is a easy to use method for accurate MHC
clustering
Reproduces and reveals difference within the known HLA class
I and class II supertypes •  Demonstrates MHC specificity overlap between species (some
mice are like men)
• 
•  Supertypes
•  Supertypes
Conclusions
XXXXX
•  Fdgfdg
•  dfgdfg

MHC polymorphism - CBS

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