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G.L. Zhang, A.M. Khan , K.N. Srinivasan, A.T. Heiny, K.X. Lee,

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes. G.L. Zhang, A.M. Khan , K.N. Srinivasan, A.T. Heiny, K.X. Lee, C.K. Kwoh, J.T. August and V. Brusic. Outline. Background & Motivations

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G.L. Zhang, A.M. Khan , K.N. Srinivasan, A.T. Heiny, K.X. Lee,

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  1. Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes G.L. Zhang, A.M. Khan, K.N. Srinivasan, A.T. Heiny, K.X. Lee, C.K. Kwoh, J.T. August and V. Brusic

  2. Outline Background & Motivations System – Hotspot Hunter Discussion

  3. Identification of T-cell epitopes for the study of vaccines and immunotherapies Peptide TCR HLA http://immuneweb.xxmc.edu.cn/Lymphoid%20System.files/UntiPCT8.jpeg

  4. P1 Promiscuous epitopes P2 P3 P4 One supertype H1 H2 H3 H4 T-cell epitope clusters (hotspots) for the development of epitope-based vaccines • Promiscuous T-cell epitopes relevant to large proportion of the human population • Presence of clusters of promiscuous T-cell epitopes (hotspots) in antigens

  5. Mapping hotspots experimentally is a challenging task • Large size of pathogen proteomes (sequence length versus sequence number) • Low natural prevalence of T-cell epitopes (~1-5%) for a given HLA molecule • High cost of peptide synthesis • Limited access to human PBMC • Time-consumingexperimental assays Peptide TCR HLA

  6. Existing prediction systems are not suitable for large-scale study of hotspots in pathogen proteomes • Limitations of existing promiscuous epitope prediction systems • Single protein sequence per submission • Do not predict for hotspots • Impractical for large-scale systematic study of hotspots in large proteomes

  7. Outline • Background & Motivations • System – Hotspot Hunter • Discussion

  8. http://antigen.i2r.a-star.edu.sg/hh/

  9. Hotspot Hunter • Screen and select of hotspots specific to four common HLA supertypes • HLA class I A2, A3, B7  cover ~ 88% of human population • HLA class II DR  cover ~100% of human population

  10. Hotspot Hunter Implementation • Predictive Engines  ANN and SVM methods • Predictions results integrated using soft computing principles • 10-fold cross-validation results showed that the system is of high accuracy

  11. Hotspot Hunter can reliably identify real hotspots • HLA-DR supertype specific hotspots for HCV Core protein sequence Experimental verified 30-51 118-173 95-108 FP 30-47 FN 130-147 Hotspot Hunter predictions

  12. Hotspot Hunter Functions • Single sequence query • Multiple sequence query • Target selection • Selection of common hotspot across more than one HLA supertype

  13. Khan et al. (2006) BMC Bioinformatics

  14. Outline Background & Motivations System – Hotspot Hunter Discussion

  15. Hotspot Hunter is a new generation computational tool aiding in epitope-based vaccine design • Allows prediction of immunological hotspots • Combines the strengths of the ANN and SVM  robust prediction performance • Multiple sequence query  suitable for large-scale study • Provides a utility for selecting candidate hotspots and experimental targets

  16. Application of Hotspot Hunter • Our system can be customized and integrated into specialized databases • Tumor Antigen Database: http://research.i2r.a-star.edu.sg/Templar/DB/cancer_antigen/ • CandiVF - Candida albicans Virulence Factor Database Tongchusak et al., (2005) Int J Pep Res Ther.

  17. Acknowledgment • Funding Agency • NIH, USA

  18. Validation using experimental binders • Human pappilomavirus type 16 proteins E6 (Kast et al.,1994) • E6 hot-spot regions • HLA-A2 E6 7-34 (7-15, 18-26, and 26-34) • HLA-A2 E6 52-60 (single peptide) • HLA-A3 E6 33-67 (33-41, 42-50, and 59-67) • E6 75-101 (75-83, 89-97, and 93-101) • E6 125-151 (125-133 and 143-151) E6 HLA-A2 HLA-A3

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