tailored vaccines – fantasy or reality?

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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. - PowerPoint PPT Presentation

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  • Tailored vaccines fantasy or reality?Irini DoytchinovaMedical University of SofiaSchool of Pharmacy, Medical University of Sofia

  • Vaccines and Epitopeslive attenuated or killed pathogenssubunitvaccinesepitope-basedvaccinesEpitope is a continuous or non-continuous sequence ofa protein that is recognized by and interacts with other protein.linearepitopeconformationalepitope-lymphocyte-limphocyte

  • Antigen processing pathwaysIntracellular pathway Extracellular pathway

  • T-cell epitope prediction

    in vitro and in vivo tests clinical testsEpitope-based vaccine development in silico prediction100 aa 92 overlapping nonamer peptides10 nonamer peptides

  • T-cell epitope prediction = MHC binding predictionThe number of T-cell receptors (TCRs) within the human T-cell repertoirehas been estimated between107 and 1015. IMGT/HLA Database (Sept. 2011)HLA class I 5,301HLA class II1,509All6,810MHC bindersT-cellepitopesAll 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).

  • Peptide binding site on MHCMHC class IMHC class II

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

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  • Peptide vaccines are tailored drugsCocktail of many epitopes each binding to one MHC proteinA few promiscuous epitopeseach binding to several MHC proteins

  • Immunoinformatics approaches Sequence-based methodsStructure-based methodsAffinity = f (Chemical Structure)

    Motif-based, QMs, ANN, SVMAffinity = f (Interaction energy)

    Molecular docking, Molecular dynamics

  • Our immunoinformatics toolshttp://www.pharmfac.net/ddg

  • Server for in silico prediction of peptides binding to MHC proteinsAdditive sequence-based methodGuan 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, 2006HLA class I: 11 allelesA*0101*0201, *0202, *0203, *0206*0301, *1101, *3101*6801, *6802B*3501

    HLA class II: 3 allelesDRB1*0101, DRB1*0401, DRB1*0701

    mouse MHC class I: 3 allelesH2-Db, H2-Kb, H2-Kk

    mouse MHC class II: 6 allelesI-Ab, I-Ad, I-Ak, I-AsI-Ed, I-EkMHCPred

  • HIV epitope projectWalshe et al. PLoS ONE, 4, e8095, 2009training set of 43 peptides25 binders + 18 non-bindersmodel for binding to HLA-Cw*010222 predicted binders11 true binders1 new epitoperecognized by T cellsexperimentally testedvirtual screening on HIV proteomeadditive PLS methodexperimentally testedCollaborators: Leiden University Medical School UCL Medical School

    Funding: The Jenner Institute, Oxford UniversityHuman Immunodeficiency Virus (HIV)

  • EpiJenServer for in silico prediction of T-cell epitopes binding to MHC class I proteinsMulti-step algorithm based on the additive method 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

  • VaxiJenDoytchinova and Flower, Vaccine, 25, 856, 2007; Doytchinova and Flower, BMC Bioinformatics, 8, 4, 2007; Doytchinova and Flower, The Open Vaccine J., 1, 22, 2008training set of proteinsimmunogens + non-immunogensuniform set of proteinsmodel for immunogenicity predictionassessment of sensitivity, specificity and accuracyCV and external validationdiscriminant analysis by PLSz-descriptors + ACC transformationServer for in silico prediction of immunogens and subunit vaccines

  • EpiTOPServer for proteochemometrics-based prediction of peptides binding to MHC class II proteins Affinity = L + P + LPDimitrov et al., Bioinformatics 26, 2066, 2010.training set of 2666 peptidesbinding to 12 HLA-DRB1 proteinsmodels for binding predictionEpiTOPProteochemometric QSARCV and external validationProteochemometrics is a QSAR method specially designed to deal with ligands binding to a set of similar proteins. Prof. Jarl Wikberg Uppsala University, Sweden

  • MHC class II binding prediction by structure-based methodsCombinatorial librarybinding scorePKYVKQNTLKLAT + 0.456PKXVKQNTLKLAT - 0.123PKYXKQNTLKLAT PKYVXQNTLKLAT PKYVKXNTLKLAT PKYVKQXTLKLAT 1 2 3 4 5 6 7 8 9A C D E External validationQuantitative MatrixPeptide HLA-DP2 protein complex(DPA1*0103 red, DPB1*0101 blue) pdb code: 3lqz, April 2010

  • External validationTest set of 457 known binders to HLA-DP2 proteinoriginating from 24 foreign proteinsImmune Epitope Database: http://www.immuneepitope.orgPeptidescoreScore = Xp1 + Xp2 + Xp3 + Xp4 + Xp5 + Xp6 + Xp7 + Xp8 + Xp9MGHRTYYKL0.567GHRTYYKLP1.245HRTYYKLPR2.935RTYYKLPRT-0.769TYYKLPRTT3.719YYKLPRTTN1.543YKLPRTTNV0.451KLPRTTNVD2.039TYYKLPRTT3.719 HRTYYKLPR2.935 KLPRTTNVD2.039 YYKLPRTTN1.543GHRTYYKLP1.245MGHRTYYKL0.567YKLPRTTNV0.451 RTYYKLPRT-0.769Peptidescorerankingtop 5%

  • Structural ImmunoinformaticsPatronov et al. BMC Str. Biol., 11, 32, 2011; Doytchinova et al. Protein Science, in press.

  • EpiDOCKServer for structure-based prediction of peptides binding to MHC proteins HLA-DR: 12 allelesDRB1*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 allelesDQ2: DQA1*0501/DQB1*0201DQ3:DQA1*0501/DQB1*0301 DQ3: DQA1*0301/DQB1*0302 DQ4: DQA1*0401/DQB1*0402DQ5: DQA1*0101/DQB1*0501DQ6: DQA1*0102/DQB1*0602HLA-DP: 5 allelesDP1: HLA-DPA1*0201/HLA-DPB1*0101DP2: HLA-DPA1*0103/HLA-DPB1*0201DP4: HLA-DPA1*0103/HLA-DPB1*0401DP4: HLA-DPA1*0103/HLA-DPB1*0402DP5: HLA-DPA1*0201/HLA-DPB1*0501

    SLA-1: 4 allelesSLA-1*0101, SLA-1*0401, SLA-1*0501, SLA-1*1101Atanasova et al. Mol. Informatics, 30, 368, 2011

  • Activity on our serversTop 5 countries visiting:IndiaUSAEU countriesJapanIran Top 5 servers used:VaxiJenMHCPredAntiJenEpiJenEpiTOP

  • 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, SABoophilus microplusSwine Influenza Virus (SIV)

  • Acknowledgements

    All models are wrong but some are useful.

    George E. P. Box, 1987Professor of Statistics, University of Wisconsin Darren R. Flower Aston University, Birmingham, UK Ivan Dimitrov Mariyana Atanasova Panaiot GarnevSchool of PharmacyMedical University of SofiaFunding: 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