Immunological Bioinformatics

1. Immunological Bioinformatics Processing, combined predictions, and rational epitope selection

2. Cellular Immunity

3. 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

4. Proteasome evolution (b1 unit) Human (Hs) - Human Drosophila (Dm) - Fly Bos Taurus (Bota) - Cow Oncorhynchus mykiss (Om) - Fish … Constitutive Immuno

5. Immuno- and Constitutive proteasome specificity Immuno Constitutive P1 P1’ ...LVGPTPVNIIGRNMLTQL..

6. Predicting proteasomal cleavage • 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

7. NetChop 3.0 Cterm (MHC ligands) • NetChop-3.0 C-term • Trained on class I epitopes • Most epitopes are generated by the immuno proteasome • Predicts the immuno proteasome specificity LDFVRFMGVMSSCNNPA LVQEKYLEYRQVPDSDP RTQDENPVVHFFKNIVT TPLIPLTIFVGENTGVP LVPVEPDKVEEATEGEN YMLDLQPETTDLYCYEQ PVESMETTMRSPVFTDN ISEYRHYCYSLYGTTLE AAVDAGMAMAGQSPVLR QPKKVKRRLFETRELTD LGEFYNQMMVKAGLNDD GYGGRASDYKSAHKGLK KTKDIVNGLRSVQTFAD LVGFLLLKYRAREPVTK SVDPKNYPKKKMEKRFV SSSSTPLLYPSLALPAP FLYGALLLAEGFYTTGA

8. NetChop20S-3.0In vitro digest data from the constitutive proteasome Toes et al., J.exp.med. 2001

9. TP FP Aroc=0.8 AP AN Aroc=0.5 Sens 1 - spec Prediction performance

10. Predicting proteasomal cleavage NetChop20S-3.0 NetChop-3.0 • Relative poor predictive performance • For MHC prediction CC~0.92 and AUC~0.95

11. Proteasome specificity • NetChop is one of the best available cleavage method • www.cbs.dtu.dk/services/NetChop-3.0

12. Cellular Immunity

13. What does TAP do?

14. TAP affinity prediction • Transporter Associated with antigen Processing • Binds peptides 9-18 long • Binding determined mostly by N1-3 and C terminal amino acids

15. TAP binding motif matrix A low matrix entry corresponds to an amino acid well suited for TAP binding Peters et el., 2003. JI, 171: 1741.

16. TAP affinity prediction

17. Predicting TAP affinity 9 meric peptides >9 meric ILRGTSFVYV -0.11 + 0.09 - 0.42 - 0.3 = -0.74 Peters et el., 2003. JI, 171: 1741.

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

19. Identifying CTL epitopes HLA affinity Proteasomal cleavage TAP affinity 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 2.80 2 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 2.28 3 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 1.78 4 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 1.58 5 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 1.27 6 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 1.24 7 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 1.18 8 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 1.12 9 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 ...

21. Large scale method validation HIV A3 epitope predictions

22. Pathogen and population coverage How to hit them all in a few strokes

23. HCV Genotypes

24. Genotype Variation

25. Genotype variation HIV-1 CRF02_AG (a), HCV genotype 4 (b) and HCV genotype 1 (c) de Oliveira et al., Nature 444, 836-837(14 December 2006)

26. GenoCover Select peptide with maximal coverage Top Scoring Peptides Genotype 1 Genotype 2 Select peptide with maximal coverage preferring uncovered strains Genotype 3 Genotype 4 Genotype 5 Genotype 6 Select peptide with maximal coverage preferring lowest covered strains Repeat until the desired number of peptides is selected

27. HCV Results - B7 Genome Coverage Peptides Predicted affinity (nM) Peptide Genotype 1 4 QPRGRRQPI 5 5 Genotype 2 3 SPRGSRPSW 43 4 Genotype 3 2 66 3 DPRRRSRNL* Genotype 4 3 RARAVRAKL 6 3 Genotype 5 3 TPAETTVRL* 38 3 Genotype 6 3 * Verified B7 supertype restricted CD8+ epitope in the Los Alamos HCV epitope database

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

29. MHC-Cover Select peptide with maximal MHC coverage Top Scoring Peptides HLA-A*0101 HLA-A*0201 Select peptide with maximal MHC coverage preferring uncovered MHCs HLA-A*0301 HLA-B*0702 HLA-B*2705 HLA-B*4402 Select peptide with maximal MHC coverage preferring lowest covered HLAs Repeat until the desired number of peptides is selected

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

31. MHC-Cover Select peptide with maximal population coverage Top Scoring Peptides HLA-A*0101 HLA-A*0201 Select peptide with maximal coverage preferring uncovered MHCs with highest population coverage HLA-A*0301 HLA-B*0702 HLA-B*2705 HLA-B*4402 Select peptide with maximal coverage preferring lowest covered HLAs with highest population coverage Repeat until the desired number of peptides is selected

32. Epi-select Select peptide with maximal population coverage and maximal genotype coverage Genotype 1 HLA-A*0101 Genotype 2 HLA-A*0201 Genotype 3 Select peptide with maximal coverage preferring uncovered MHCs with highest population coverage and maximal genotype coverage HLA-A*0301 Genotype 4 HLA-B*0702 Genotype 5 HLA-B*2705 Genotype 6 HLA-B*4402 Select peptide with maximal coverage preferring lowest covered HLAs with highest population coverage and maximal genotype coverage Repeat until the desired number of peptides is selected

33. Reaching optimal coverage HCV Genotypes