Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort.

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Abstract Background: Identifying immunogens that induce HIV-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types. Methods: We tested the performance of the NetMHCpan4.0 computational neural network in re-identifying 93 T-cell epitopes that had been previously independently mapped using the whole proteome IFN-g ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. Results: NetMHCpan4.0 correctly predicted 88 of the 93 experimentally mapped epitopes for a set length of 9-mer and matched HLA class I alleles. Receiver Operator Characteristic (ROC) analysis gave an area under the curve (AUC) of 0.928. Setting NetMHCpan4.0 to predict 11-14mer length did not improve the prediction (37-79 of 93 peptides) with an inverse correlation between the number of predictions and length set. Late time point peptides were significantly stronger binders than early peptides (Wilcoxon signed rank test: p =0.0000005). Conclusion: NetMHCpan4.0 class I epitope predictions covered 95% of the epitope landscape recognised by HIV-1 infected individuals, and would have reduced the number of experimental confirmatory tests by >80%. Algorithmic epitope prediction in conjunction with HLA allele frequency information can cost-effectively assist immunogen design. Keywords: HIV-1, epitope mapping, T-cell, artificial neural network, in-silico and NetMHCpan4.0.

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