QSAR and the Prediction of T cell Epitopes

1. Darren R Flower QSAR and the Prediction of T cell Epitopes http://www.jenner.ac.uk/res-bio darren.flower@jenner.ac.uk

2. Growth in sales Vaccines: 12% yr-1 Drugs: 5% yr-1 $ 15 B $ 5 B $ 1.75 B 1990 2000 2010 VACCINE MARKET VACCINES 1% $5 Billion DRUGS 99% $350 Billion

3. # of Biotech companies HUMAN VACCINES ARE MOVING FROM A MARGINAL TO A MAJOR R & D DRIVEN SECTOR AIDS ANTIBIOTIC RESISTANCE BIOTERRORISM A limited number of vaccines targeting major diseases A few innovative vaccines with blockbuster potential R &D pipeline: 100s of new vaccines 1980 2000 10 150

4. IMMUNOVACCINOLOGY WHOLE ORGANISM SUBUNIT VACCINE EPITOPE VACCINE attenuated Vaccines induce protective immunity. Protective immunity is an enhanced adaptive immune response to re-infection. Delivered as recombinant protein / vector or as naked DNA + adjuvants &/or “danger signals”

5. Types of Peptide Epitope Antibody or “B cell” Epitope Conformational Linear B cell Epitope T cell Epitope Non- Conformational Class I MHCs all cells Foreign and self proteins 8-10 amino acids Class II MHCs Professional Antigen Presenting cells Foreign proteins 8-20 amino acids

6. T cell response TCR CD8 PEPTIDE Class I

7. AFFINITY MEASURE “PEPTIDE” FUNNEL NARROWEST POINT IC50 IC50 KD Half maximal lysis or qualitative measure (thymidine incoporation, cell killing, etc KD BL50 C50 SC50 CLEAVAGE PATTERNS t1/2 RESPONSE MEASURE etc. Class I PROTEIN T cell response PROTEA SOME MHC TAP TCR

8. PREDICTING EPITOPES Traditional Motifs: X{Y/F}XXPXXWS Frequency Matrices, Profiles “AI” Solutions: Neural Networks, HMMs, etc 3D-QSAR: ComFA / CoMSIA 2D-QSAR: Free Wilson Analysis Epitope prediction is a chemical problem. We have taken a quantitative approach. Molecular Dynamics Quantitative Structure Activity Relationships

9. Compile data RELATIONAL DATABASE JenPep Extract data for particular Allele CoMSIA / CoMFA ADDITIVE METHOD QSAR TESTABLE PREDICTIONS QUANTITITATIVE MEASURES OF PEPTIDE-MHC AFFINITY FROM THE LITERATURE

10. EDWARD JENNER INSTITUTE FOR VACCINE RESEARCH JENPEP Helen McSparron Martin Blythe Christianna Zygouri

11. JENPEP VERSION 1.0 2061 T-Cell Epitopes 5848 MHC Binding data (IC50, BL50, t1/2, etc) 432 TAP Binding data ACCESS Relational Database GUI using HTML and ASP on our website: www.jenner.ac.uk/JenPep MJ Blythe, IA Doytchinova, and DR Flower. JenPep: a database of quantitative functional peptide data for immunology. Bionformatics 2002 18 434-439

12. JENPEP “VERSION 2.0” 3018 T cell epitopes 12210 MHC Binding data (IC50, BL50, t1/2, Kd, etc) 441 TAP Binding data 1656 B cell epitopes 300 pMHC-TCR Binding data bespoke postgreSQL relational database GUI using perl and HTML on our website: www.jenner.ac.uk/JenPep H McSparron, C Zygouri, D Taylor, MJ Blythe, IA Doytchinova, and DR Flower. JenPep+: Novel developments in quantitative immunological databases Nucleic Acids Research, commissioned

13. Develop database system further: extend existing databases (T cell, MHC, TAP, B cell, pMHC-TCR) with new data and further retrospective analysis add new database sections: non-natural peptides and non-natural MHC mutants antibody binding whole protein antigens Host - Superantigen / Virulence Factor Binding Data Co-receptor Binding Data etc.

14. Binding Affinity of peptides vs. Host Immunogenicity MHCs: hundreds of alleles. Each with a different peptide binding selectivity. T cell epitopes bind well to MHCs. 95% of all known T cell epitopes bind to MHC with an IC50< 500nM. Exact T cell response is dependent on the T cell repertoire. Therefore, prediction of MHC binding is “best” option for predicting T cell epitopes.

15. EDWARD JENNER INSTITUTE FOR VACCINE RESEARCH PREDICTING Tcell EPITOPES Irini Doytchinova Christianna Zygouri PingPing Guan

16. T - Cell Epitope Search

17. CAVEAT our peptide sets are larger than is typical in the pharmaceutical literature the peptides themselves are physically large physical properties of peptides are extreme: multiple charges, zwitterions, huge range in hydrophobicity, etc. Sequence & thus properties are heavily biased in our peptide sets Affinity data is “poor”: multiple measurements of same peptide with orders of magnitude differences, some values are clearly wrong, mix of different standard peptides in radioligand competition assays, etc. performing a “meta-analysis”: probably many different binding modes forced into one QSAR model

18. Predicting T cell Epitopes Using QSAR CoMFA / CoMSIA Towards the quantitative prediction of T-cell epitopes: CoMFA and CoMSIA studies of peptides with affinity to class I MHC molecule HLA-A*0201. Doytchinova, I.A and Flower, D. R. J. Med. Chem. 2001, 44, 3572-3581. Physicochemical Explanation of Peptide Binding to HLA-A*0201 Major Histocompatibility Complex. A Three – Dimensional Quantitative Structure – Activity Relationship Study. Doytchinova, I.A and Flower, D. R. Proteins, in press. HLA-A*0201 most common allele in Caucasian population: 40% ~5x more binding data than for any other allele FREE WILSON ANALYSIS An Additive Method for the Prediction of Protein-Peptide Binding Affinity. Application to the MHC Class I Molecule HLA-A*0201 Irini A. Doytchinova*, Martin J. Blythe and Darren R. Flower J. Proteome Research 2002, 1, 263-272.

19. 152 peptides 152 peptides with affinity to with affinity to the HLA-A2.1 the HLA-A2.1 Training set Training set Test set Test set 102 102 50 50 peptides peptides peptides peptides Comparison of CoMFA & CoMSIA for HLA-A*0201 r2pred < 0.5 NC = 6 q2 =0.480 r2= 0.911 r2pred = 0.679 NC = 5 q2 = 0.542 r2 = 0.870

20. Full CoMSIA Analysis of HLA-A*0201 Hydrogen Bond Map Steric Map Electrostatic Map Hydrophobic Map NC = 7 q2 = 0.683 r2 = 0.891 n = 236

21. P 2 P4 P 6 P 8 H O H O H O H O H O H N N N N N H N N N N O H O H O H O H O P 1 P 3 P 5 P 7 P 9 ADDITIVE METHODFORBINDINGAFFINITY PREDICTION HLA-A*0201: NC = 5 q2 = 0.337 r2 = 0.898 n = 340

22. Amino acids contributions 1-2 Interactions 1-3 interactions

23. How does the additive method work? YLSPGPVTV with pIC exp = 7.642 50 pIC = + 1Y + 2L + 3S + 4P + 5G + 6P +7V + 8T + 9V const 50 + 1Y2L + 2L3S + 3S4P + 4P5G + 5G6P + 6P7V + 7V8T + 8T9V + 1Y3S + 2L4P + 3S5G + 4P6P + 5G7V + 6P8T + 7V9V pIC = 6.213 +0.304 + 0.219 – 0.164 + 0.135 + 0.013 - 0.008 + 0.096 + 0.035 + 0.263 50 + 0.240 – 0.015 + 0 + 0 + 0.101 + 0.075 + 0.059 + 0.102 + 0.031 + 0.044 – 0.107 + 0.046 + 0.011 + 0.008 - 0.001 = 7.700

24. CoMSIA & ADDITIVE METHOD ARE COMPLEMENTARY CoMSIA is “slow” but is “better” at extrapolating. ADDITIVE is very fast (analyze whole microbial genome in a few minutes) but is worse at extrapolating to peptide sequences very different to training data (missing values) Our models are not perfect but our results are at least as good as anyone else working in predicting MHC binding Trying to develop a range of “universal” models each covering a different allele

25. A2-Supertype models

26. MHCPred: an on-line server for peptide MHC binding prediction Models: A*0101, A*0201, A*0202, A*0203, A*0206, A*0301, A*1101, A*3301, A*6801, A*6802, B*3501 www.jenner.ac.uk/MHCPred P Guan, IA Doytchinova, C Zygouri, and DR Flower. MHCPred: bringing a quantitative dimension to online prediction of MHC Binding. To be submitted

27. In Progress Develop Additive Method to be descriptor based Develop “better” QSAR models using “clean” thermodynamic data from ITC and designed peptides Planned FUTURE DEVELOPMENTS OF THIS WORK Make “true” predictions - design new peptides and test them experimentally Develop models for uncharacterized MHC alleles using peptides generated with Experimental Design