This article is part of the supplement: Asia Pacific Bioinformatics Network (APBioNet) Sixth International Conference on Bioinformatics (InCoB2007)
Proceedings
Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes
-
* Corresponding authors: Guang L Zhang guanglanzhang@gmail.com - Vladimir Brusic vladimir_brusic@dfci.harvard.edu
- † Equal contributors
1 Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
2 School of Computer Engineering, Nanyang Technological University, Singapore 639798
3 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597
4 Department of Microbiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597
5 Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
6 Product Evaluation and Registration Division, Centre for Drug Administration, Health Sciences Authority, 11 Biopolis Way, #011-03 Helios, Singapore 138667
7 Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA 02115, USA
8 School of Land, Crop, and Food Sciences, University of Queensland, Brisbame 4072, Australia
BMC Bioinformatics 2008, 9(Suppl 1):S19 doi:10.1186/1471-2105-9-S1-S19
Published: 13 February 2008Abstract
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/ webcite. It is a new generation computational tool aiding in epitope-based vaccine design.