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Van Hai Van , Cao Thi Ngoc Phuong, Tran Linh Thuoc

Sixth International Conference on Bioinformatics InCoB2007. Training and applying hidden Markov models and support vector machines for prediction of T-cell epitopes. Van Hai Van , Cao Thi Ngoc Phuong, Tran Linh Thuoc Faculty of Biology, University of Natural Sciences, VNU-HCMC, Vietnam.

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Van Hai Van , Cao Thi Ngoc Phuong, Tran Linh Thuoc

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  1. Sixth InternationalConference on BioinformaticsInCoB2007 Training and applying hidden Markov models and support vector machines for prediction of T-cell epitopes Van Hai Van, Cao Thi Ngoc Phuong, Tran Linh Thuoc Faculty of Biology, University of Natural Sciences, VNU-HCMC, Vietnam

  2. Epitope prediction “Epitope is the portion of an antigen that is recognized by the antigen receptor on lymphocytes” Molecular Biology Epitope prediction: Computers aid to develop epitope-based vaccines against various human pathogens for which no vaccines currently exist http://www.scripps.edu/newsandviews/e_20050228/hiv.html

  3. T-cell epitope prediction • T-cell epitopes are a subset of MHC binding peptides  prediction of the peptides binding to MHC is essential for design of peptide-based vaccines • HLA-A0201 Sequence Binding motifs Artificial neural networks Quantitative matrices Hidden Markov models Support vector machines Decision tree Molecular Biology

  4. HMMs & SVMs SVMs (Support Vector Machines): Learning machine that can find the optimal separating hyperplane. HMMs (Hidden Markov Models) Statistical model that can capture complex relationships in data sets.

  5. Epitope prediction for dengue virus • Tropical disease • Dengue fever • Dengue hemorraghic fever • Dengue shock syndrome • Hypothesis of pathogenesis • Antibody – dependent enhancement • Virus virulence • No dengue vaccine is available In our research: . Develop procedure for building automatically T-cell epitope predicting models . Find candidates in silico for making multivalent vaccines on 4 types of Dengue virus

  6. Building models for predicting T-cell epitopes & applying these models on dengue virus

  7. Building effective prediction models? The predicting ability of HMM and SVM models depends on: Experimentally peptides binding to MHC molecules Partition of the peptides into training set and testing set Encoding method  A system finds easily and quickly the best prediction model when type of MHC molecules and quantity of binding peptides are changed

  8. Processing MHC-binding experimental peptides

  9. Create training and testing sets

  10. Training & testing procedure HMMs (HMMer) SVMs (SVM_light)

  11. Experiment 1

  12. Result of the training by HMMs HMM.7.136: AROC=0.914 Choose parameter from HMM.7.136: At point: E=3.4, S=-8.5, SE=0.91, SP= 0.86, AROC=0.885

  13. Result of the training by SVMs At blosum-62 encoding, data set SVM.7.blo62.46: SE=0.83, SP=0.90, AROC=0.87 Binary encoding: AROC=0.42÷0.77 Blosum-62 encoding: AROC= 0.47÷0.87 Chemical-physical encoding: AROC= 0.41÷0.71

  14. Experiment 2

  15. Result of the training by HMMs

  16. Training in 6-amino acid homologous groups HMM.6.78: AROC=0.883 Parameters of HMM.6.78: At point: E=42, S=-9.2, SE=0.91, SP= 0.84, AROC=0.875

  17. Result of the training by SVMs methods

  18. : Binary encoding : Blosum-62 encoding : Binary-Blosum-62 encoding Training in 7-amino acid homologous groups At SVM.2.7.85: SE=0.93, SP=0.86, AROC=0.894

  19. Epitope predicting procedure for dengue virus • Do multiple sequence alignment • Extract consensus sequences more than or equal 9 amino acids • Create 9-mer overlap sequences • Predict peptides binding to MHC by HMMs profile or SVMs model

  20. Experiment 1 Experiment 2 Result of epitope prediction (peptide binding to HLA-A0201 prediction): Join overlap 9-amino acid peptides predicted binding to HLA-A0201 molecules

  21. Result of prediction • HMMs profile is stable and increase ability of prediction when there are additional data sets. • SVMs model is good but ability of prediction decreases when amount of training data increases.

  22. http://www.biology.hcmuns.edu.vn/epitope

  23. Conclusion • Successfully building system for training Hidden Markov models and Support Vector Machines • Generating training and testing data based on separating data set into homologous groups give us good result. • Could predict consensus epitope for 4 types of Dengue virus based on data of peptides binding to HLA-A0201

  24. Future plans • Set other kernels on SVMs method • Survey other encoding method for sequences having flexible length • Survey other methods for classifying MHC data to homologous groups • Automate procedure collecting and updating data of peptide binding MHC from databases

  25. Thank you very much!

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