{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T07:12:21Z","timestamp":1793085141991,"version":"4.5.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T00:00:00Z","timestamp":1589414400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["75N93019C00001"],"award-info":[{"award-number":["75N93019C00001"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014419","name":"EIT Health","doi-asserted-by":"publisher","award":["19638"],"award-info":[{"award-number":["19638"]}],"id":[{"id":"10.13039\/100014419","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http:\/\/www.cbs.dtu.dk\/services\/NetMHCpan-4.1\/ and http:\/\/www.cbs.dtu.dk\/services\/NetMHCIIpan-4.0\/.<\/jats:p>","DOI":"10.1093\/nar\/gkaa379","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T15:11:08Z","timestamp":1588173068000},"page":"W449-W454","source":"Crossref","is-referenced-by-count":2473,"title":["NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data"],"prefix":"10.1093","volume":"48","author":[{"given":"Birkir","family":"Reynisson","sequence":"first","affiliation":[{"name":"Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, DK\u00a028002, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8735-2719","authenticated-orcid":false,"given":"Bruno","family":"Alvarez","sequence":"first","affiliation":[{"name":"Instituto de Investigaciones Biotecnol\u00f3gicas, Universidad Nacional de San Mart\u00edn, Buenos Aires,\u00a0BA 16503, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sinu","family":"Paul","sequence":"first","affiliation":[{"name":"La Jolla Institute for Immunology, La Jolla, CA\u00a092037, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjoern","family":"Peters","sequence":"first","affiliation":[{"name":"La Jolla Institute for Immunology, La Jolla, CA\u00a092037, USA"},{"name":"Department of Medicine, University of California, San Diego, CA\u00a092093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7885-4311","authenticated-orcid":false,"given":"Morten","family":"Nielsen","sequence":"first","affiliation":[{"name":"Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, DK\u00a028002, Denmark"},{"name":"Instituto de Investigaciones Biotecnol\u00f3gicas, Universidad Nacional de San Mart\u00edn, Buenos Aires,\u00a0BA 16503, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,5,14]]},"reference":[{"key":"2020062614041779400_B1","first-page":"424","article-title":"Janeway\u2019s Immunobiology, Ninth Edition","volume":"89","author":"Duan","year":"2016","journal-title":"Yale Journal of Biology and Medicine"},{"key":"2020062614041779400_B2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1146\/annurev-immunol-082119-124838","article-title":"T cell epitope predictions","volume":"38","author":"Peters","year":"2020","journal-title":"Annu. Rev. Immunol."},{"key":"2020062614041779400_B3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13073-016-0288-x","article-title":"NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets","volume":"8","author":"Nielsen","year":"2016","journal-title":"Genome Med."},{"key":"2020062614041779400_B4","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s00251-013-0720-y","article-title":"NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ","volume":"65","author":"Karosiene","year":"2013","journal-title":"Immunogenetics"},{"key":"2020062614041779400_B5","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.cels.2018.05.014","article-title":"MHCflurry: open-source Class I MHC binding affinity prediction","volume":"7","author":"O\u2019Donnell","year":"2018","journal-title":"Cell Syst."},{"key":"2020062614041779400_B6","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1186\/1471-2105-10-394","article-title":"Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior","volume":"10","author":"Kim","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"2020062614041779400_B7","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.immuni.2017.02.007","article-title":"Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction","volume":"46","author":"Abelin","year":"2017","journal-title":"Immunity"},{"key":"2020062614041779400_B8","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.immuni.2019.08.012","article-title":"Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction","volume":"51","author":"Abelin","year":"2019","journal-title":"Immunity"},{"key":"2020062614041779400_B9","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.4049\/jimmunol.1600808","article-title":"Unsupervised HLA peptidome deconvolution improves ligand prediction accuracy and predicts cooperative effects in peptide-HLA interactions","volume":"197","author":"Bassani-Sternberg","year":"2016","journal-title":"J. Immunol."},{"key":"2020062614041779400_B10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/nbt.4313","article-title":"Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification","volume":"37","author":"Bulik-Sullivan","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"2020062614041779400_B11","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1038\/s41587-019-0289-6","article-title":"Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes","volume":"37","author":"Racle","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"2020062614041779400_B12","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.4049\/jimmunol.1700893","article-title":"NetMHCpan-4.0: improved peptide-MHC class i interaction predictions integrating eluted ligand and peptide binding affinity data","volume":"199","author":"Jurtz","year":"2017","journal-title":"J. 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