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

32 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
JournalBioinformatics
Volume20
Issue numberSUPPL. 1
DOIs
Publication statusPublished - 2004
Externally publishedYes

Fingerprint

Epitopes
T-Lymphocyte Epitopes
T-cells
Antigens
Hot Spot
Proteins
Protein
Prediction
HLA-A3 Antigen
Hidden Markov models
HLA-A2 Antigen
Markov Model
Artificial Neural Network
Melanoma
Neural networks
Papillomavirus E7 Proteins
SARS Virus
Oncogenic Viruses
Histocompatibility Antigens Class I
Neural Networks (Computer)

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Prediction of class I T-cell epitopes : Evidence of presence of immunological hot spots inside antigens. / Srinivasan, K. N.; Zhang, G. L.; Khan, A. M.; August, J. T.; Brusic, V.

In: Bioinformatics, Vol. 20, No. SUPPL. 1, 2004.

Research output: Contribution to journalArticle

Srinivasan, K. N. ; Zhang, G. L. ; Khan, A. M. ; August, J. T. ; Brusic, V. / Prediction of class I T-cell epitopes : Evidence of presence of immunological hot spots inside antigens. In: Bioinformatics. 2004 ; Vol. 20, No. SUPPL. 1.
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