Immunological Bioinformatics

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Department of Systems Biology

Technical University of Denmark

Immunological Bioinformatics

Processing, combined predictions, and rational

epitope selection

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Cellular Immunity

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Proteasome specificity

Low polymorphism– Constitutive & Immuno-

proteasome

Evolutionary conserved Stochastic and low specificity

– Only 70-80% of the cleavage sites are reproduced in repeated experiments

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Proteasome evolution (1 unit)

Constitutive

Immuno

Human (Hs) - HumanDrosophila (Dm) - Fly

Bos Taurus (Bota) - CowOncorhynchus mykiss (Om) - Fish

…

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Immuno- and Constitutive proteasome specificity

...LVGPTPVNIIGRNMLTQL..

P1 P1’

Immuno Constitutive

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NetChop– Neural network based method

PaProc– Weight matrix based method

FragPredict– Based on a statistical analysis of cleavage-

determining amino acid motifs present around the scissile bond• i.e. also weight matrix like

Predicting proteasomal cleavage

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NetChop 3.0 Cterm (MHC ligands)

LDFVRFMGVMSSCNNPA LVQEKYLEYRQVPDSDP RTQDENPVVHFFKNIVT TPLIPLTIFVGENTGVP LVPVEPDKVEEATEGEN YMLDLQPETTDLYCYEQ PVESMETTMRSPVFTDN ISEYRHYCYSLYGTTLE AAVDAGMAMAGQSPVLR QPKKVKRRLFETRELTD LGEFYNQMMVKAGLNDD GYGGRASDYKSAHKGLK KTKDIVNGLRSVQTFAD LVGFLLLKYRAREPVTK SVDPKNYPKKKMEKRFV SSSSTPLLYPSLALPAP FLYGALLLAEGFYTTGA

NetChop-3.0 C-term– Trained on class I

epitopes– Most epitopes are

generated by the immuno proteasome

– Predicts the immuno proteasome specificity

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NetChop20S-3.0In vitro digest data from the constitutive proteasome

Toes et al., J.exp.med. 2001

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Prediction performance

€

Sens =TP

AP

Spec =TN

AN

CC =TP ⋅TN − FN ⋅FPPP ⋅AN ⋅AP ⋅PN

TPFP

APAN

Aroc=0.5

Aroc=0.8

1 - spec

Sen

s

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Predicting proteasomal cleavage

-0.4-0.2

00.20.40.60.8

1

Performance

FragPredictPAProCI Netchop2.0NetChop3.0

Sens Spec CC

0

0.5

1

Performance

CC PCC Aroc

CC 0.12 0.1 0.41 0.48

PCC 0.13 0.48 0.55

Aroc 0.56 0.82 0.85

FragPredict PAProCI Netchop20S NetChop20S-3.0

NetChop20S-3.0

NetChop-3.0

• Relative poor predictive performance–For MHC prediction CC~0.92 and AUC~0.95

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Proteasome specificity

NetChop is one of the best available cleavage method– www.cbs.dtu.dk/services/NetChop-3.0

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Cellular Immunity

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What does TAP do?

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TAP affinity prediction

Transporter Associated with antigen Processing Binds peptides 9-18 long Binding determined mostly by N1-3 and C terminal amino acids

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A low matrix entry corresponds to an amino acid well suited for TAP binding

TAP binding motif matrix

Peters et el., 2003. JI, 171: 1741.

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TAP affinity prediction

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Predicting TAP affinity

9 meric peptides >9 meric

Peters et el., 2003. JI, 171: 1741.

ILRGTSFVYV-0.11 + 0.09 - 0.42 - 0.3 = -0.74

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Integrating all three steps (protesaomal cleavage, TAP transport and MHC binding) should lead to improved identification of peptides capable of eliciting CTL responses

Integration?

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Identifying CTL epitopes

1 EBN3_EBV YQAYSSWMY 2.56 1.00 0.03 0.34 0.99 0.02 0.01 0.75 0.94 0.92 2.97 0 2.802 EBN3_EBV QSDETATSH 2.22 0.01 0.28 0.88 0.04 0.83 0.51 0.30 0.11 0.99 -0.80 0 2.283 EBN3_EBV PVSPAVNQY 1.55 0.01 0.97 0.01 0.22 0.21 1.00 0.02 0.04 1.00 2.63 0 1.784 EBN3_EBV AYSSWMYSY 1.31 0.34 0.99 0.02 0.01 0.75 0.94 0.92 0.09 1.00 3.28 1 1.585 EBN3_EBV LAAGWPMGY 1.02 1.00 0.97 0.22 0.01 0.18 0.01 0.06 0.01 1.00 3.01 0 1.276 EBN3_EBV IVQSCNPRY 0.99 0.10 0.97 0.50 0.05 0.01 0.01 0.01 0.02 0.93 3.19 0 1.247 EBN3_EBV FLQRTDLSY 0.94 0.46 0.99 0.02 0.82 0.07 0.01 0.63 0.01 0.96 2.79 0 1.188 EBN3_EBV YTDHQTTPT 1.15 1.00 0.01 0.42 0.02 0.04 0.01 0.02 0.54 0.14 -0.87 0 1.129 EBN3_EBV GTDVVQHQL 0.96 0.01 0.02 0.03 0.99 1.00 0.02 0.46 0.30 1.00 0.53 0 1.09...

HLA affinity

Proteasomal cleavage

TAP affinity

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Large scale method validation

HIV A3 epitope predictions

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Pathogen and population coverage

How to hit them all in a few strokes

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HCV Genotypes

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Genotype Variation

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Genotype variation

de Oliveira et al., Nature 444, 836-837(14 December 2006)

HIV-1 CRF02_AG (a), HCV genotype 4 (b) and HCV genotype 1 (c)

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Genotype 1

Top Scoring Peptides Top Scoring Peptides

Genotype 2

Genotype 3

Genotype 4

Genotype 5

Genotype 6

Select peptide with maximal coverage


preferring uncovered strains

Select peptide with maximal coverage preferring lowest covered strains

Repeat until the Repeat until the desired number of desired number of

peptides is selectedpeptides is selected

GenoCover

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Genotype 1

Genotype 2

Genotype 3

Genotype 4

Genotype 5

Genotype 6

QPRGRRQPIQPRGRRQPI

PeptidePeptide Predicted Predicted affinity (nM)affinity (nM)

55

SPRGSRPSWSPRGSRPSW 4343

GenomeGenomeCoverageCoverage

55

44

DPRRRSRNLDPRRRSRNL** 336666

RARAVRAKLRARAVRAKL

PeptidesPeptides

66 33

TPAETTVRLTPAETTVRL** 3838 33

33

33

22

33

44

33

* Verified B7 supertype restricted CD8+ epitope in the Los Alamos HCV epitope database

HCV Results - B7

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Population Diversity

http://static.howstuffworks.com/gif/population-six-billion-1.jpg

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MHC-Cover

HLA-A*0101


HLA-A*0201

HLA-A*0301

HLA-B*0702

HLA-B*2705

HLA-B*4402

Select peptide with maximal MHC

coverage


coverage preferring uncovered MHCs


coverage preferring lowest

covered HLAs



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Population diversity

http://www.piperreport.com/archives/Images/Marketing%20to%20Diverse%20Medicare%20Population.jpg

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MHC-Cover

HLA-A*0101


HLA-A*0201

HLA-A*0301

HLA-B*0702

HLA-B*2705

HLA-B*4402

Select peptide with maximal

population coverage


preferring uncovered MHCs

with highest population coverage

Select peptide with maximal coverage preferring lowest

covered HLAs with highest population

coverage



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Epi-select

HLA-A*0101

HLA-A*0201

HLA-A*0301

HLA-B*0702

HLA-B*2705

HLA-B*4402

Select peptide with maximal

population coverage and

maximal genotype coverage


preferring uncovered MHCs

with highest population

coverage and maximal genotype

coverage Select peptide with maximal coverage preferring lowest

covered HLAs with highest population

coverage and maximal genotype

coverage



Genotype 1

Genotype 2

Genotype 3

Genotype 4

Genotype 5

Genotype 6

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Reaching optimal coverage

HCV Genotypes

Date post:	27-Jan-2016
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Immunological Bioinformatics

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