g.l. zhang, a.m. khan , k.n. srinivasan, a.t. heiny, k.x. lee,
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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
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HLA
http://immuneweb.xxmc.edu.cn/Lymphoid%20System.files/UntiPCT8.jpeg
Peptide TCR
Identification of T-cell epitopes for the study of vaccines and immunotherapies
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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
H1 H4H3H2
P1P2P3P4
Promiscuous epitopes
One supertype
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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-consuming experimental assays
HLA
Peptide TCR
C prM E NS1 NS2a NS2b NS3 NS4a NS4b NS5
DENV
100-900aa
>9000
C prM E NS1 NS2a NS2b NS3 NS4a NS4b NS5
DENV
100-900aa
>9000
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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
Existing prediction systems are not suitable for large-scale study of hotspots
in pathogen proteomes
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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
1010
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
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FPFN
Hotspot Hunter can reliably identify real hotspots
30-51 95-108 118-173
30-47 130-147
Hotspot Hunter predictions
Experimental verified
HLA-DR supertype specific hotspots for HCV Core protein sequence
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Hotspot Hunter Functions
Single sequence query
Multiple sequence query
Target selection
Selection of common hotspot across more than one HLA supertype
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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
Hotspot Hunter is a new generation computational tool aiding in epitope-
based vaccine design
1919
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 DatabaseTongchusak et al., (2005) Int J Pep Res Ther.
Application of Hotspot Hunter
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Human pappilomavirus type 16 proteins E6 (Kast et al., 1994)
E6 hot-spot regionsHLA-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)
Validation using experimental binders
E6HLA-A2
HLA-A3