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Selection of T Cell Epitopes Using an Integrative Approach

Selection of T Cell Epitopes Using an Integrative Approach. Mette Voldby Larsen cand. scient. in Biology PhD in Immunological Bioinformatics. Outline. Summary of biological processes preceding a CTL response Summary of the methods available for predicting the processes Case study:

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Selection of T Cell Epitopes Using an Integrative Approach

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  1. Selection of T Cell Epitopes Using an Integrative Approach Mette Voldby Larsen cand. scient. in Biology PhD in Immunological Bioinformatics

  2. Outline • Summary of biological processes preceding a CTL response • Summary of the methods available for predicting the processes • Case study: -Obtaining data, generating method, evaluating the method (small exercise – how to make Roc curves) - What can you use the method for?

  3. MHC-I molecules present peptides on the surface of most cells

  4. CTL response Virus- infected cell Healthy cell MHC-I

  5. Epitope-based vaccines: Only the peptides, which are presented by MHC class I molecules are used in the vaccine >Haemagglutinin - Influenza A virus (A/chicken/Jilin/9/2004(H5N1)) MEKIVLLLAIVSLVKSDQICIGYHANNSTEQVDTIMEKNVTVTHAQDILEKTHNGKLCDL DGVKPLILRDCSVAGWLLGNPMCDEFINVPEWSYIVEKASPANDLCYPGDFNDYEELKHL LSRINHFEKIQIIPKSSWSNHEASSGVSSACPYQGKSSFFRNVVWLIKKNSTYPTIKRSY NNTNQEDLLVLWGIHHPNDAAEQTKLYQNPTTYISVGTSTLNQRLVPKIATRSKVNGQSG RMEFFWTILKPNDAINFESNGNFIAPEYAYKIVKKGDSAIMKSELEYGNCNTKCQTPMGA INSSMPFHNIHPLTIGECPKYVKSNRLVLATGLRNSPQRERRRKKRGLFGAIAGFIEGGW QGMVDGWYGYHHSNEQGSGYAADKESTQKAIDGVTNKVNSIIDKMNTQFEAVGREFNNLE RRIENLNKKMEDGFLDVWTYNAELLVLMENERTLDFHDSNVKNLYDKVRLQLRDNAKELG NGCFEFYHKCDNECMESVRNGTYDYPQYSEEARLNREEISGVKLESIGTYQILSIYSTVA SSLALAIMVAGLSLWMCSNGSLQCRICI

  6. Epitope-based vaccines: Only the peptides, which are presented by MHC class I molecules are used in the vaccine >Haemagglutinin - Influenza A virus (A/chicken/Jilin/9/2004(H5N1)) MEKIVLLLAIVSLVKSDQICIGYHANNSTEQVDTIMEKNVTVTHAQDILEKTHNGKLCDL DGVKPLILRDCSVAGWLLGNPMCDEFINVPEWSYIVEKASPANDLCYPGDFNDYEELKHL LSRINHFEKIQIIPKSSWSNHEASSGVSSACPYQGKSSFFRNVVWLIKKNSTYPTIKRSY NNTNQEDLLVLWGIHHPNDAAEQTKLYQNPTTYISVGTSTLNQRLVPKIATRSKVNGQSG RMEFFWTILKPNDAINFESNGNFIAPEYAYKIVKKGDSAIMKSELEYGNCNTKCQTPMGA INSSMPFHNIHPLTIGECPKYVKSNRLVLATGLRNSPQRERRRKKRGLFGAIAGFIEGGW QGMVDGWYGYHHSNEQGSGYAADKESTQKAIDGVTNKVNSIIDKMNTQFEAVGREFNNLE RRIENLNKKMEDGFLDVWTYNAELLVLMENERTLDFHDSNVKNLYDKVRLQLRDNAKELG NGCFEFYHKCDNECMESVRNGTYDYPQYSEEARLNREEISGVKLESIGTYQILSIYSTVA SSLALAIMVAGLSLWMCSNGSLQCRICI

  7. Epitope-based vaccine SLYNTVATL VSRLWWERI

  8. NetCTL Combining preexisting prediction methods Proteasomal cleavage: NetChop, Artificial Neural Network Kesmir et al (2002). Immunity. 23. 249-62 TAP transport efficiency: Consensus TAP matrix Peters et al (2003). J Immunol. 171. 1741-9 MHC class I affinity: NetMHC, Artificial Neural Network Buus et al (2003). Tissue Antigens. 62. 378-84 Nielsen et at (2003). Protein Sci. 12. 1007-17

  9. Dataset • 863 known epitopes from the SYFPEITHI database • 216 known epitopes from the Los Alamos HIV database • The source proteins are found in SwissProt and split into all possible 9mers. • 9mers not known to be epitopes are considered non-epitopes

  10. Hypothetical protein: MPADNSELVISISAL Calculation of the combined NetCTL score: 0.15 * Prot + 0.05 * TAP + 1* MHC-I

  11. Performance measure – Roc curve

  12. AUC = 0.5 AUC = 1.0

  13. Results

  14. Results

  15. AUC-values What does the numbers mean? For an experimentalist aiming at identifying new epitopes he/she has to test 30% fewer peptides to find the same amount of epitopes

  16. Practical use of NetCTL -ongoing projects Prediction of epitopes in: • HIV (collaboration with Karolinska Institute in Sweden) • Influenza A (collaboration with Panum institute) • Tuberculosis (collaboration with Leiden University in the Netherlands) • West nile virus (collaboration with Panum institute) • Yellow fever virus (collaboration with Panum institute) • Rickettsia (collaboration with Argentina) • Lassa/Junin virus (collaboration with Panum and Argentina)

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