Computer Aided Vaccine Design

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The complexity of the immune system dwarfs the complexity of genomic data by several magnitudes. Yet, the rational design of vaccines must be able to wade through this complexity to make highly effective vaccines with little side effects. In this presentation made at the Functional Genomics and Bioinformatics meeting of the East African Workshop, I discuss how exactly computers may aid in the rational design of vaccines.

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Computer Aided Vaccine Design

  1. 1. Computer Aided Vaccine Design Geoffrey H. Siwo East Africa Regional Worshop 2007
  2. 2. Key Questions in Vaccine Design <ul><li>Which immune response should the vaccine elicit? </li></ul><ul><li>- T-helper cell mediated immunity </li></ul><ul><li>- Cytotoxic T cell mediated immunity </li></ul><ul><li>- Humoral immune responses </li></ul>
  3. 3. Why Comp. Aided Vaccine Design? <ul><li>Immune system is complex </li></ul><ul><li>-10 13 MHC class I haplotypes (IMGT-HLA) </li></ul><ul><li>-10 7 -10 15 different T-cell receptors </li></ul><ul><li>-10 12 B-cell clonotypes in an individual </li></ul><ul><li>-10 11 linear epitopes composed of nine amino acids </li></ul><ul><li>->>10 11 conformational epitopes </li></ul>
  4. 4. B-cell Epitopes <ul><li>Recognized by antibodies </li></ul><ul><li>Linear B- cell Epitopes </li></ul><ul><li>Conformational B-cell </li></ul><ul><li>Epitopes </li></ul>
  5. 5. Linear B-cell Epitopes <ul><li>Consist of contiguous sequence of amino eg </li></ul><ul><li>SSAGGQQQESS – a linear epitope of MSP of A. marginale </li></ul><ul><li>Also known as Sequential epitopes </li></ul><ul><li>http://www.imtech.res.in/raghava/bcipep/ - for information on B-cell epitopes </li></ul>
  6. 6. Conformational B-cell Epitopes <ul><li>Epitopes formed by molecular interractions of distantly located amino acids </li></ul><ul><li>http://web.kuicr.kyoto-u.ac.jp/~ced/index.php </li></ul>
  7. 7. Prediction of Linear B-cell Epitopes <ul><li>Linear B-cell Epitopes: easy to predict but not good immunogens </li></ul><ul><li>Prediction based on: </li></ul><ul><li>- Hydrophilicity </li></ul><ul><li>- Flexibility </li></ul><ul><li>- Secondary structures </li></ul>
  8. 9. Prediction of Conformational Epitopes <ul><li>Much more difficult than for linear B-cell Epitopes </li></ul><ul><li>Based on similarity of 3-D structures of different antigens </li></ul><ul><li>Hence, 3-D structure of antigen MUST be known </li></ul>
  9. 12. T- cell Epitopes <ul><li>Have major differences to B-cell epitopes </li></ul><ul><li>Are linear and produced by antigen processing </li></ul><ul><li>Presented in conjuction with MHC class I or II molecules to CD8 + Cytotoxic T-cells and CD4 + helper T-cells, respectively </li></ul>
  10. 13. Exogenous Antigen Processing
  11. 14. Endogenous Antigen Processing
  12. 15. Prediction of T-cell Epitopes <ul><li>Each stage in the antigen processing and presentation pathway occurs non-randomly </li></ul><ul><li>T-cell epitopes are linear and short (8-11 amino acids for class I, slightly more for class II) </li></ul><ul><li>Prediction of T-cell epitopes is easier than conformational B-cell epitopes </li></ul>
  13. 16. Sites for MHC class I Epitope Prediction
  14. 17. Complexity of T-cell Epitopes <ul><li>Over 10 13 MHC class I haplotypes </li></ul><ul><li>There are 10 11 possible linear epitopes composed of 9 amino acids </li></ul><ul><li>How does the immune system discriminate between epitopes and non-epitopes? </li></ul>
  15. 19. Prediction of MHC class I Epitopes <ul><li>Motif based methods </li></ul><ul><li>Quantitative matrices </li></ul><ul><li>Structure based methods </li></ul><ul><li>Machine learning methods </li></ul><ul><li>- Artificial Neural networks </li></ul><ul><li>- Support vector machines </li></ul>
  16. 20. Motif based Epitope Prediction <ul><li>Based on the presence of specific amino acids at certain positions of the peptide </li></ul><ul><li>These are known as anchor residues </li></ul><ul><li>Motifs are less accurate (60-65% accuracy) as not all peptides that bind to a given MHC molecule have exact motifs </li></ul>
  17. 21. Quantitative Matrices <ul><li>Are refined motifs, essentially covering all amino acids of the peptide </li></ul><ul><li>The contribution of each amino acid at specific position within the binding peptide is quantified </li></ul><ul><li>The quantitative matrices are generated from experimental binding data of a large ensemble of variant sequences. Epivax maintains an inhouse database on this. </li></ul>
  18. 22. The score of a peptide is calculated by summing up the individual scores Of the amino acids at each position.
  19. 23. Quantitative Matrices based Methods Tool URL Alleles Covered ProPred http://www.imtech.res.in/raghava/propred1 47 MHC alleles nHLAPred http://www.imtech.res.in/raghava/nhlapred 67 MHC alleles SYFPEITHI http://www.syfpeithi.de > 200 MHC alleles LpPEP http://reiner.bu.edu/zhiping/lppep.html 1 MHC allele RANKPEP http://mif.dfci.harvard.edu/Tools/rankpep. html >40 MHC alleles BIMAS http://bimas.dcrt.nih.gov/molbio/hla_bind/ >46 MHC alleles MAPPP http://reiner.bu.edu/zhiping/lppep.html >50 MHC alleles
  20. 25. Machine Learning Approaches <ul><ul><li>Machine Learning is a branch of artificial intelligence </li></ul></ul><ul><ul><li>Involves training machine learning software using sequences known to bind to a given HLA </li></ul></ul><ul><ul><li>allele and a separate set of non-binding sequences </li></ul></ul>
  21. 26. General schema of Machine learning Approaches Dataset of MHC binding and non-binding peptides Training set Training of ANN or SVM validation Test set Prediction tool
  22. 27. In silico Vaccine Discovery <ul><li>A wide range of software are available </li></ul><ul><li>How do you choose the best? </li></ul><ul><li>- Prediction accuracy </li></ul><ul><li>- HLA allele population coverage </li></ul><ul><li>- Promiscous epitope prediction </li></ul><ul><li>- T-cell epitope Hotspots </li></ul><ul><li>- Conserved epitope prediction </li></ul>
  23. 28. MULTIPRED: Artificial Neural Network based Prediction
  24. 29. MULTIPRED http://research.i2r.a-star.edu.sg/multipred/ <ul><li>Can predict promiscous epitopes </li></ul><ul><li>High sensitivity and specificity </li></ul><ul><li>T-cell Hotspots identification </li></ul>
  25. 30. PEPVAC http://bio.dfci.harvard.edu/PEPVAC/ <ul><li>Broad population coverage of HLA alleles </li></ul><ul><li>Can predict promiscous epitopes </li></ul><ul><li>Can identify conserved epitopes </li></ul><ul><li>Can predict epitopes with proteasome cleavage sites </li></ul><ul><li>Produces less number of epitopes hence making lab. experimentation easier </li></ul>
  26. 35. The Future <ul><li>From Genomics to Immunomics </li></ul><ul><li>Establishment of Immunome Databases- the complete set of epitopes in an organism </li></ul><ul><li>Modelling of cytokine networks </li></ul><ul><li>In silico vaccine trials </li></ul>

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