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


Journal

BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551

Informations de publication

Date de publication:
22 Feb 2020
Historique:
received: 28 08 2019
accepted: 12 02 2020
entrez: 24 2 2020
pubmed: 24 2 2020
medline: 6 5 2020
Statut: epublish

Résumé

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. 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. 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. 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.

Sections du résumé

BACKGROUND 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 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 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 CONCLUSIONS
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.

Identifiants

pubmed: 32087680
doi: 10.1186/s12879-020-4876-4
pii: 10.1186/s12879-020-4876-4
pmc: PMC7036183
doi:

Substances chimiques

Epitopes, T-Lymphocyte 0
Histocompatibility Antigens Class I 0
Peptides 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

172

Subventions

Organisme : Medical Research Council
ID : MC_UU_00027/1
Pays : United Kingdom
Organisme : European and Developing Countries Clinical Trials Partnership
ID : TA_05_40200_40203
Organisme : UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement, the Wellcome Trust
ID : WT078927MA

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Auteurs

Daniel Lule Bugembe (DL)

MRC/UVRI and LSHTM Uganda Research Unit, P. O. Box 49, Plot 51-59 Nakiwogo Road, Entebbe, Uganda. dan.lule@mrcuganda.org.

Andrew Obuku Ekii (AO)

MRC/UVRI and LSHTM Uganda Research Unit, P. O. Box 49, Plot 51-59 Nakiwogo Road, Entebbe, Uganda.

Nicaise Ndembi (N)

Institute of Human Virology, Abuja, Nigeria.

Jennifer Serwanga (J)

MRC/UVRI and LSHTM Uganda Research Unit, P. O. Box 49, Plot 51-59 Nakiwogo Road, Entebbe, Uganda.
Uganda Virus Research Institute, Entebbe, Uganda.

Pontiano Kaleebu (P)

MRC/UVRI and LSHTM Uganda Research Unit, P. O. Box 49, Plot 51-59 Nakiwogo Road, Entebbe, Uganda.
Uganda Virus Research Institute, Entebbe, Uganda.

Pietro Pala (P)

MRC/UVRI and LSHTM Uganda Research Unit, P. O. Box 49, Plot 51-59 Nakiwogo Road, Entebbe, Uganda.

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Classifications MeSH