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Tailored vaccines – fantasy or reality?

Tailored vaccines – fantasy or reality?. School of Pharmacy, Medical University of Sofia. Irini Doytchinova Medical University of Sofia. Vaccines and Epitopes. live attenuated or killed pathogens. subunit vaccines. epitope-based vaccines. Т- lymphocyte. conformational epitope.

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Tailored vaccines – fantasy or reality?

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

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

  3. Antigen processing pathways Intracellular pathwayExtracellular pathway

  4. T-cell epitope prediction Epitope-based vaccine development 100 aa 92 overlapping nonamer peptides 10 nonamer peptides in silicoprediction in vitro andin vivo testsclinical tests

  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 107 and 1015. IMGT/HLA Database (Sept. 2011) HLA class I 5,301 HLA class II 1,509 All 6,810 All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. T-cell epitopes MHC binders 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 I MHC class II

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

  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 methods Structure-based methods Affinity = f(Chemical Structure) Motif-based, QMs, ANN, SVM Affinity = f(Interaction energy) Moleculardocking, Molecular dynamics

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

  11. MHCPred Server forin silico prediction of peptides binding to MHC proteins Additive sequence-based method HLAclassI: 11 alleles A*0101 А*0201, А*0202, А*0203, А*0206 А*0301, А*1101, А*3101 А*6801, А*6802 B*3501 HLAclassII: 3alleles DRB1*0101, DRB1*0401, DRB1*0701 mouseMHCclassI: 3alleles H2-Db, H2-Kb, H2-Kk mouseMHCclassII: 6 alleles I-Ab, I-Ad, I-Ak, I-As I-Ed, I-Ek Guanet 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

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

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

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

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

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

  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 Score = Xp1 + Xp2 + Xp3 + Xp4 + Xp5 + Xp6 + Xp7 + Xp8 + Xp9 Peptide score Peptide score top 5% MGHRTYYKL 0.567 GHRTYYKLP 1.245 HRTYYKLPR 2.935 RTYYKLPRT -0.769 TYYKLPRTT 3.719 YYKLPRTTN 1.543 YKLPRTTNV 0.451 KLPRTTNVD 2.039 TYYKLPRTT 3.719 HRTYYKLPR 2.935 KLPRTTNVD 2.039 YYKLPRTTN 1.543 GHRTYYKLP 1.245 MGHRTYYKL 0.567 YKLPRTTNV 0.451 RTYYKLPRT -0.769 ranking

  18. Structural Immunoinformatics 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: 12alleles 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: 6alleles 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: 5alleles 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: 4alleles 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 servers used: • VaxiJen • MHCPred • AntiJen • EpiJen • EpiTOP • Top 5 countries visiting: • India • USA • EU countries • Japan • Iran

  21. Current projects • Anti-tick vaccine project • Collaborator: University of Pretoria, SA • Funding: University of Pretoria, SA Boophilus microplus • 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 Swine Influenza Virus (SIV)

  22. Acknowledgements • 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) • Darren R. Flower • Aston University, Birmingham, UK • Peicho Petkov • School of Physics, • University of Sofia All models are wrong but some are useful. George E. P. Box, 1987 Professor of Statistics, University of Wisconsin • Atanas Patronov • Hannover Biomedical • Research School, Germany

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