Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort.
Adolescent
Adult
Child
Cohort Studies
Computational Biology
/ methods
Enzyme-Linked Immunospot Assay
Epitope Mapping
/ methods
Epitopes, T-Lymphocyte
/ immunology
Female
HIV Infections
/ immunology
HIV-1
/ immunology
Histocompatibility Antigens Class I
/ immunology
Humans
Male
Middle Aged
Neural Networks, Computer
Peptides
/ immunology
Uganda
Young Adult
Artificial neural network
Epitope mapping
HIV-1
In-silico
MHCflurry1.2.0 and NetCTL1.2
NetMHCpan4.0.
T-cell
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
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
172Subventions
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
Références
Immunogenetics. 2009 Jan;61(1):1-13
pubmed: 19002680
J Infect Dis. 2018 Jul 13;218(4):633-644
pubmed: 29669026
AIDS Res Hum Retroviruses. 2016 Mar;32(3):237-46
pubmed: 26548707
Methods Mol Biol. 2007;409:185-200
pubmed: 18450001
BMC Bioinformatics. 2007 Oct 31;8:424
pubmed: 17973982
Nucleic Acids Res. 2009 Jul;37(Web Server issue):W634-42
pubmed: 19483099
Radiology. 1982 Apr;143(1):29-36
pubmed: 7063747
Korean J Radiol. 2004 Jan-Mar;5(1):11-8
pubmed: 15064554
Nat Immunol. 2006 Feb;7(2):173-8
pubmed: 16369537
BMC Genomics. 2018 Jan 19;19(Suppl 1):42
pubmed: 29363421
Immunology. 2014 Oct;143(2):193-201
pubmed: 24724694
J Transl Med. 2011 Dec 08;9:212
pubmed: 22152192
Bioinformatics. 2010 Mar 15;26(6):822-30
pubmed: 20130029
Tissue Antigens. 1998 Jul;52(1):57-66
pubmed: 9714475
Proc Natl Acad Sci U S A. 1985 Oct;82(20):7048-52
pubmed: 2413457
Protein Sci. 2003 May;12(5):1007-17
pubmed: 12717023
AIDS. 2003 Sep 5;17(13):1871-9
pubmed: 12960819
Vaccine. 2015 Mar 30;33(14):1664-72
pubmed: 25728323
Bioinformatics. 2015 Jul 1;31(13):2174-81
pubmed: 25717196
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W172-9
pubmed: 15980449
J Virol. 2004 Mar;78(5):2187-200
pubmed: 14963115
J Biomed Biotechnol. 2010;2010:218590
pubmed: 21772787
J Immunol. 2008 Feb 15;180(4):2174-86
pubmed: 18250424
Bioinformatics. 2009 Jan 1;25(1):83-9
pubmed: 18996943
Trends Parasitol. 2014 Aug;30(8):401-11
pubmed: 25028089
PLoS Comput Biol. 2006 Jun 9;2(6):e65
pubmed: 16789818
Bioinformatics. 2005 Oct 1;21(19):3797-800
pubmed: 16076886
J Immunol Methods. 2003 Mar 1;274(1-2):221-8
pubmed: 12609547
Bioinform Biol Insights. 2016 Apr 28;10:27-35
pubmed: 27147821
J Virol. 2006 May;80(10):4717-28
pubmed: 16641265
Immunogenetics. 2016 Feb;68(2):157-65
pubmed: 26572135
J Immunol. 2017 Nov 1;199(9):3360-3368
pubmed: 28978689
PLoS One. 2008 Jan 09;3(1):e1424
pubmed: 18183304
Bioinformatics. 2014 Aug 15;30(16):2381-3
pubmed: 24790156
J Virol. 2007 Mar;81(5):2440-8
pubmed: 17182686
Bioinformatics. 2008 Feb 1;24(3):358-66
pubmed: 18083718
Cell Syst. 2018 Jul 25;7(1):129-132.e4
pubmed: 29960884
PLoS One. 2014 Dec 29;9(12):e115745
pubmed: 25545691
Mol Med. 2002 Mar;8(3):137-48
pubmed: 12142545
Eur J Immunol. 2005 Aug;35(8):2295-303
pubmed: 15997466
PLoS One. 2011 Jan 20;6(1):e15639
pubmed: 21283794
AIDS Res Hum Retroviruses. 2012 Apr;28(4):384-92
pubmed: 21867408
J Gen Virol. 2015 Jul;96(Pt 7):1890-8
pubmed: 25724670