Large-scale computational identification of HIV T-cell epitopes

Bioinformatics‐driven T‐cell epitope‐identification methods can enhance vaccine target selection significantly. We evaluated three unrelated computational methods to screen Pol, Gag and Env sequences extracted from the Los Alamos HIV database for HLA‐A∗0201 and HLA‐B∗3501 T‐cell epitope candidates. The hidden Markov model predicted 389 HLA‐B∗3501‐restricted candidates from 374 HIV‐1 and 97 HIV‐2 sequences. The artificial neural network (ANN) model, and Bioinformatics and Molecular Analysis Section (BIMAS) quantitative matrix predictions for A∗0201 yielded 1122 HIV‐1 and 548 HIV‐2 candidates. The overall sequence coverage of the predicted A∗0201 T‐cell epitopes was 2.7% (HIV‐1) and 3.0% (HIV‐2). HLA‐B∗3501‐predicted epitopes covered 0.9% (HIV‐1) and 1.4% (HIV‐2) of the total sequence. Comparison of 890 ANN‐ and 397 BIMAS‐derived HIV‐1 A∗0201‐restricted epitope candidates showed that only 13‐19% of the predicted and 26% of the experimentally confirmed T‐cell epitopes were captured by both methods. Extrapolating these results, we estimated that at least 247 predicted HIV‐1 epitopes are yet to be discovered as active A∗0201‐restricted T‐cell epitopes. Adequate comparison and combined usage of various predictive bioinformatics methods, rather than uncritical use of any single prediction method, will enable cost‐effective and efficient T‐cell epitope screening.