1. Tailored vaccines – fantasy or reality? Irini Doytchinova Medical University of Sofia School of Pharmacy, Medical University of Sofia

2. Vaccines and Epitopes live attenuated or killed pathogens subunit vaccines epitope-based vaccines Epitope is a continuous or non-continuous sequence of a protein that is recognized by and interacts with other protein. linear epitope conformational epitope Т-lymphocyte В-limphocyte

3. Antigen processing pathways Intracellular pathway Extracellular pathway

4. T-cell epitope prediction in vitro and in vivo tests clinical tests Epitope-based vaccine development in silico prediction 100 aa 92 overlapping nonamer peptides 10 nonamer peptides

5. T-cell epitope prediction = MHC binding prediction The number of T-cell receptors (TCRs) within the human T-cell repertoire has been estimated between 10 7 and 10 15. IMGT/HLA Database (Sept. 2011) HLA class I 5,301 HLA class II1,509 All6,810 MHC binders T-cell epitopes All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. 90% of the T-cell epitopes have MHC affinity stronger than 500 nM. Aim: To identify the best MHC binders (the top 2% of all peptides generated from one protein).

6. Peptide binding site on MHC MHC class IMHC class II

7. Allele frequency in Bulgarian population The Allele Frequency Net Database (http://www.allelefrequency.net), September 2011 n = 55

8. Peptide vaccines are tailored drugs Cocktail of many epitopes each binding to one MHC protein A few promiscuous epitopes each binding to several MHC proteins

9. Immunoinformatics approaches Sequence-based methodsStructure-based methods peptide pIC50exp ILDPFPVTV 8.654 ALDPFPPTV 8.170 VLDPFPITV 8.139....................... LLDPFPPPV 7.442 ILDPIPPTV 7.296 LLDDFPVTV 7.155 ILDPLPPTV 7.145 YLFPGPVTA 6.305 Affinity = f (Chemical Structure) Motif-based, QMs, ANN, SVM Affinity = f (Interaction energy) Molecular docking, Molecular dynamics

10. Our immunoinformatics tools http://www.pharmfac.net/ddg

11. Server for in silico prediction of peptides binding to MHC proteins Additive sequence-based method Guan et al. Nucleic Acid Res., 31, 3621-3624, 2003; Guan et al. Appl. Bioinformatics, 2, 63-66, 2003; Guan et al. Appl. Bioinformatics, 5, 55-61, 2006 HLA class I: 11 alleles A*0101 А*0201, А*0202, А*0203, А*0206 А*0301, А*1101, А*3101 А*6801, А*6802 B*3501 HLA class II: 3 alleles DRB1*0101, DRB1*0401, DRB1*0701 mouse MHC class I: 3 alleles H2-Db, H2-Kb, H2-Kk mouse MHC class II: 6 alleles I-Ab, I-Ad, I-Ak, I-As I-Ed, I-Ek MHCPred

12. HIV epitope project Walshe et al. PLoS ONE, 4, e8095, 2009 training set of 43 peptides 25 binders + 18 non-binders model for binding to HLA-Cw*0102 22 predicted binders 11 true binders 1 new epitope recognized by T cells experimentally tested virtual screening on HIV proteome additive PLS method experimentally tested Collaborators: Leiden University Medical School UCL Medical School Funding: The Jenner Institute, Oxford University Human Immunodeficiency Virus (HIV)

13. EpiJen Server for in silico prediction of T-cell epitopes binding to MHC class I proteins Multi-step algorithm based on the additive method QM for proteasome cleavage QM for ТАР affinity QMs for MHC affinity T-cell recognition non-cleaved non-transported non-bound cleaved transported bound non-recognized top 5% Doytchinova et al. J. Immunol., 173, 6813-6819, 2004; Doytchinova & Flower. Mol. Immun., 43, 2037-2044, 2006; Doytchinova et al. BMC Bioinformatics, 7, 131, 2006

14. VaxiJen Doytchinova and Flower, Vaccine, 25, 856, 2007; Doytchinova and Flower, BMC Bioinformatics, 8, 4, 2007; Doytchinova and Flower, The Open Vaccine J., 1, 22, 2008 training set of proteins immunogens + non-immunogens uniform set of proteins model for immunogenicity prediction assessment of sensitivity, specificity and accuracy CV and external validation discriminant analysis by PLS z-descriptors + ACC transformation Training setAccuracy % Bacterial70 Viral70 Tumor86 Parasite79 Fungal97 Server for in silico prediction of immunogens and subunit vaccines

15. EpiTOP Server for proteochemometrics-based prediction of peptides binding to MHC class II proteins Affinity = L + P + LP Dimitrov et al., Bioinformatics 26, 2066, 2010. training set of 2666 peptides binding to 12 HLA-DRB1 proteins models for binding prediction EpiTOP Proteochemometric QSAR CV and external validation Proteochemometrics is a QSAR method specially designed to deal with ligands binding to a set of similar proteins. Prof. Jarl Wikberg – Uppsala University, Sweden

16. MHC class II binding prediction by structure-based methods Combinatorial library binding score PKYVKQNTLKLAT + 0.456 PKXVKQNTLKLAT - 0.123 PKYXKQNTLKLAT … PKYVXQNTLKLAT … PKYVKXNTLKLAT … PKYVKQXTLKLAT … 1 2 3 4 5 6 7 8 9 A … … … … … … … … … C … … … … … … … … … D … … … … … … … … … E … … … … … … … … … … … … … … External validation Quantitative Matrix Peptide – HLA-DP2 protein complex (DPA1*0103 red, DPB1*0101 blue) pdb code: 3lqz, April 2010

17. External validation Test set of 457 known binders to HLA-DP2 protein originating from 24 foreign proteins Immune Epitope Database: http://www.immuneepitope.org Peptidescore Score = X p1 + X p2 + X p3 + X p4 + X p5 + X p6 + X p7 + X p8 + X p9 MGHRTYYKL0.567 GHRTYYKLP1.245 HRTYYKLPR2.935 RTYYKLPRT-0.769 TYYKLPRTT3.719 YYKLPRTTN1.543 YKLPRTTNV0.451 KLPRTTNVD2.039 TYYKLPRTT3.719 HRTYYKLPR2.935 KLPRTTNVD2.039 YYKLPRTTN1.543 GHRTYYKLP1.245 MGHRTYYKL0.567 YKLPRTTNV0.451 RTYYKLPRT-0.769 Peptidescore ranking top 5%

18. Structural Immunoinformatics Method Sensitivity at the top 5% Time Molecular docking by GOLD on PC by AutoDock on BlueGene-P 42% 38% a batch of 20 complexes 1 week 10-15 min Molecular dynamics by GROMACS on BlueGene-P33% one complex for 1 ns 11 hours Patronov et al. BMC Str. Biol., 11, 32, 2011; Doytchinova et al. Protein Science, in press.

19. EpiDOCK Server for structure-based prediction of peptides binding to MHC proteins HLA-DR: 12 alleles DRB1*0101, DRB1*0301, DRB1*0401, DRB1*0404, DRB1*0405, DRB1*0701, DRB1*0802, DRB1*0901, DRB1*1101, DRB1*1201, DRB1*1302, DRB1*1501 HLA-DQ: 6 alleles DQ2: DQA1*0501/DQB1*0201 DQ3:DQA1*0501/DQB1*0301 DQ3: DQA1*0301/DQB1*0302 DQ4: DQA1*0401/DQB1*0402 DQ5: DQA1*0101/DQB1*0501 DQ6: DQA1*0102/DQB1*0602 HLA-DP: 5 alleles DP1: HLA-DPA1*0201/HLA-DPB1*0101 DP2: HLA-DPA1*0103/HLA-DPB1*0201 DP4: HLA-DPA1*0103/HLA-DPB1*0401 DP4: HLA-DPA1*0103/HLA-DPB1*0402 DP5: HLA-DPA1*0201/HLA-DPB1*0501 SLA-1: 4 alleles SLA-1*0101, SLA-1*0401, SLA-1*0501, SLA-1*1101 Atanasova et al. Mol. Informatics, 30, 368, 2011

20. Activity on our servers Top 5 countries visiting: 1.India 2.USA 3.EU countries 4.Japan 5.Iran Top 5 servers used: 1.VaxiJen 2.MHCPred 3.AntiJen 4.EpiJen 5.EpiTOP

21. Current projects Anti-SIV vaccine project Collaborators: CReSA (Spanish private foundation for research in animal health) INIA (Spanish National Institute of Agriculture and Food Research) Funding: Spanish Ministry of Science Anti-tick vaccine project Collaborator: University of Pretoria, SA Funding: University of Pretoria, SA Boophilus microplus Swine Influenza Virus (SIV)

22. Acknowledgements All models are wrong but some are useful. George E. P. Box, 1987 Professor of Statistics, University of Wisconsin Darren R. Flower Aston University, Birmingham, UK Ivan Dimitrov Mariyana Atanasova Panaiot Garnev School of Pharmacy Medical University of Sofia Funding: National Research Fund, Ministry of Education and Science, Bulgaria, SuperCA (Grant 2-115/2008) and SuperCA++ (Grant 02-1/2009) Peicho Petkov School of Physics, University of Sofia Atanas Patronov Hannover Biomedical Research School, Germany