Abstract
BackgroundIdentifying immunogens that induce HIV-1-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types.MethodsWe 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-Îł ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. To provide a benchmark we compared the predictions for NetMHCpan4.0 to MHCflurry1.2.0 and NetCTL1.2.ResultsNetMHCpan4.0 performed best correctly predicting 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 (37â79 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). MHCflurry1.2.0 similarly predicted all but 2 of the peptides that NetMHCpan4.0 predicted and NetCTL1.2 predicted only 14 of the 93 experimental peptides.ConclusionNetMHCpan4.0 class I epitope predictions covered 95% of the epitope responses identified in six 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 through minimizing the experimental effort.
Highlights
Identifying immunogens that induce Human immunodeficiency virus type 1 (HIV-1)-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various Human leucocyte antigen (HLA) types
Using experimental epitope mapping data generated from 757 peptides tested on cells of 6 early HIV-1 infected individuals at paired time points, we show that NetMHCpan4.0 can be useful for markedly reducing pooled peptide experiments as demonstrated by the 95% experimental and computational concordance
These were from a Ugandan early HIV-1 serodiscordant couple cohort approved by the Uganda Virus Research Institute (UVRI), Research and Ethics review board and the Uganda National Council of Science and Technology (UNCST)
Summary
Identifying immunogens that induce HIV-1-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types. Computational algorithms were initially demonstrated as useful tools for predicting potential epitopes that might elicit quality T-cell responses [1, 2]. For HIV-1 vaccine design purposes an important consideration for the suitability of a computational algorithm is the breadth of discrete number of T-cell epitopes it generates that could reach particular levels of coverage [11] of circulating viruses. In order to translate the computational epitope prediction into vaccine design, the number of discrete epitopes computationally generated from particular HIV-1 proteins is an important metric for further investigation [11]
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