Prediction of class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens

K. N. Srinivasan, G. L. Zhang, A. M. Khan, J. T. August, V. Brusic

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

Motivation: Processing and presentation of major histocompatibility complex class I antigens to cytotoxic T-lymphocytes is crucial for immune surveillance against intracellular bacteria, parasites, viruses and tumors. Identification of antigenic regions on pathogen proteins will play a pivotal role in designer vaccine immunotherapy. We have developed a system that not only identifies high binding T-cell antigenic epitopes, but also class I T-cell antigenic clusters termed immunological hot spots. Methods: MULTIPRED, a computational system for promiscuous prediction of HLA class I binders, uses artificial neural networks (ANN) and hidden Markov models (HMM) as predictive engines. The models were rigorously trained, tested and validated using experimentally identified HLA class I T-cell epitopes from human melanoma related proteins and human papillomavirus proteins E6 and E7. We have developed a scoring scheme for identification of immunological hot spots for HLA class I molecules, which is the sum of the highest four predictions within a window of 30 amino acids. Results: Our predictions against experimental data from four melanoma-related proteins showed that MULTIPRED ANN and HMM models could predict T-cell epitopes with high accuracy. The analysis of proteins E6 and E7 showed that ANN models appear to be more accurate for prediction of HLA-A3 hot spots and HMM models for HLA-A2 predictions. For illustration of its utility we applied MULTIPRED for prediction of promiscuous T-cell epitopes in all four SARS coronavirus structural proteins. MULTIPRED predicted HLA-A2 and HLA-A3 hot spots in each of these proteins.

Original languageEnglish
Pages (from-to)i297-i302
JournalBioinformatics
Volume20
Issue numberSUPPL. 1
DOIs
Publication statusPublished - Dec 1 2004

    Fingerprint

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Srinivasan, K. N., Zhang, G. L., Khan, A. M., August, J. T., & Brusic, V. (2004). Prediction of class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens. Bioinformatics, 20(SUPPL. 1), i297-i302. https://doi.org/10.1093/bioinformatics/bth943