Article ; Online: Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort.
2020 Volume 20, Issue 1, Page(s) 172
Abstract: Background: 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.: Methods: We tested the performance of the ... ...
| Abstract | Background: 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. 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-γ 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. Results: NetMHCpan4.0 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 (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). 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. Conclusion: NetMHCpan4.0 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. |
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| MeSH term(s) | Adolescent ; Adult ; Child ; Cohort Studies ; Computational Biology/methods ; Enzyme-Linked Immunospot Assay ; Epitope Mapping/methods ; Epitopes, T-Lymphocyte/immunology ; Female ; HIV Infections/immunology ; HIV Infections/virology ; HIV-1/immunology ; Histocompatibility Antigens Class I/immunology ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; Peptides/immunology ; Uganda ; Young Adult |
| Chemical Substances | Epitopes, T-Lymphocyte ; Histocompatibility Antigens Class I ; Peptides |
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-02-22 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2041550-3 |
| ISSN | 1471-2334 ; 1471-2334 |
| ISSN (online) | 1471-2334 |
| ISSN | 1471-2334 |
| DOI | 10.1186/s12879-020-4876-4 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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