Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/109381
Title: Hotspot Hunter: A computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
Authors: Zhang, G.L.
Khan, A.M.
Srinivasan, K.N.
Heiny, A.T. 
Lee, K.X.
Kwoh, C.K.
August, J.T.
Brusic, V.
Issue Date: 13-Feb-2008
Citation: Zhang, G.L., Khan, A.M., Srinivasan, K.N., Heiny, A.T., Lee, K.X., Kwoh, C.K., August, J.T., Brusic, V. (2008-02-13). Hotspot Hunter: A computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes. BMC Bioinformatics 9 (SUPPL. 1) : -. ScholarBank@NUS Repository.
Abstract: Background: T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots. Results: Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population. Conclusion: Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design. © 2008 Zhang et al; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/109381
ISSN: 14712105
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