Neural models for predicting viral vaccine targets

Guang Lan Zhang, Asif M. Khan, Kellathur N. Srinivasan, J. Thomas August, Vladimir Brusic

Research output: Contribution to journalArticle

Abstract

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve - AROC > 0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC >; 0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.

Original languageEnglish (US)
Pages (from-to)1207-1225
Number of pages19
JournalJournal of Bioinformatics and Computational Biology
Volume3
Issue number5
DOIs
StatePublished - Oct 2005

Keywords

  • Artificial neural networks
  • Immunoinformatics
  • Viral vaccines

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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  • Cite this

    Zhang, G. L., Khan, A. M., Srinivasan, K. N., August, J. T., & Brusic, V. (2005). Neural models for predicting viral vaccine targets. Journal of Bioinformatics and Computational Biology, 3(5), 1207-1225. https://doi.org/10.1142/S0219720005001466