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Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

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-ce...

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Autores principales: Zhang, Guang Lan, Khan, Asif M, Srinivasan, Kellathur N, Heiny, AT, Lee, KX, Kwoh, Chee Keong, August, J Thomas, Brusic, Vladimir
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2259420/
https://www.ncbi.nlm.nih.gov/pubmed/18315850
http://dx.doi.org/10.1186/1471-2105-9-S1-S19
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author Zhang, Guang Lan
Khan, Asif M
Srinivasan, Kellathur N
Heiny, AT
Lee, KX
Kwoh, Chee Keong
August, J Thomas
Brusic, Vladimir
author_facet Zhang, Guang Lan
Khan, Asif M
Srinivasan, Kellathur N
Heiny, AT
Lee, KX
Kwoh, Chee Keong
August, J Thomas
Brusic, Vladimir
author_sort Zhang, Guang Lan
collection PubMed
description 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 . It is a new generation computational tool aiding in epitope-based vaccine design.
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spelling pubmed-22594202008-03-04 Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes Zhang, Guang Lan Khan, Asif M Srinivasan, Kellathur N Heiny, AT Lee, KX Kwoh, Chee Keong August, J Thomas Brusic, Vladimir BMC Bioinformatics Proceedings 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 . It is a new generation computational tool aiding in epitope-based vaccine design. BioMed Central 2008-02-13 /pmc/articles/PMC2259420/ /pubmed/18315850 http://dx.doi.org/10.1186/1471-2105-9-S1-S19 Text en Copyright © 2008 Zhang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Zhang, Guang Lan
Khan, Asif M
Srinivasan, Kellathur N
Heiny, AT
Lee, KX
Kwoh, Chee Keong
August, J Thomas
Brusic, Vladimir
Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
title Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
title_full Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
title_fullStr Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
title_full_unstemmed Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
title_short Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
title_sort hotspot hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2259420/
https://www.ncbi.nlm.nih.gov/pubmed/18315850
http://dx.doi.org/10.1186/1471-2105-9-S1-S19
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