{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T18:24:40Z","timestamp":1781893480726,"version":"3.54.5"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: MHC:peptide binding plays a central role in activating the immune surveillance. Computational approaches to determine T-cell epitopes restricted to any given major histocompatibility complex (MHC) molecule are of special practical value in the development of for instance vaccines with broad population coverage against emerging pathogens. Methods have recently been published that are able to predict peptide binding to any human MHC class I molecule. In contrast to conventional allele-specific methods, these methods do allow for extrapolation to uncharacterized MHC molecules. These pan-specific human lymphocyte antigen (HLA) predictors have not previously been compared using independent evaluation sets.<\/jats:p>\n               <jats:p>Result: A diverse set of quantitative peptide binding affinity measurements was collected from Immune Epitope database (IEDB), together with a large set of HLA class I ligands from the SYFPEITHI database. Based on these datasets, three different pan-specific HLA web-accessible predictors NetMHCpan, adaptive double threading (ADT) and kernel-based inter-allele peptide binding prediction system (KISS) were evaluated. The performance of the pan-specific predictors was also compared with a well performing allele-specific MHC class I predictor, NetMHC, as well as a consensus approach integrating the predictions from the NetMHC and NetMHCpan methods.<\/jats:p>\n               <jats:p>Conclusions: The benchmark demonstrated that pan-specific methods do provide accurate predictions also for previously uncharacterized MHC molecules. The NetMHCpan method trained to predict actual binding affinities was consistently top ranking both on quantitative (affinity) and binary (ligand) data. However, the KISS method trained to predict binary data was one of the best performing methods when benchmarked on binary data. Finally, a consensus method integrating predictions from the two best performing methods was shown to improve the prediction accuracy.<\/jats:p>\n               <jats:p>Contact: \u00a0mniel@cbs.dtu.dk<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btn579","type":"journal-article","created":{"date-parts":[[2008,11,8]],"date-time":"2008-11-08T01:34:41Z","timestamp":1226108081000},"page":"83-89","source":"Crossref","is-referenced-by-count":89,"title":["Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods"],"prefix":"10.1093","volume":"25","author":[{"given":"Hao","family":"Zhang","sequence":"first","affiliation":[{"name":"Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby 2800, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claus","family":"Lundegaard","sequence":"additional","affiliation":[{"name":"Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby 2800, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morten","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby 2800, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2008,11,7]]},"reference":[{"key":"2023013110024205100_B1","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1093\/bioinformatics\/btg055","article-title":"MHCBN: a comprehensive database of MHC binding and non-binding peptides","volume":"19","author":"Bhasin","year":"2003","journal-title":"Bioinformatics"},{"key":"2023013110024205100_B2","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1007\/s00251-005-0798-y","article-title":"Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications","volume":"57","author":"Bui","year":"2005","journal-title":"Immunogenetics"},{"key":"2023013110024205100_B3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1002\/prot.10039","article-title":"LiveBench-2: large-scale automated evaluation of protein structure prediction servers","volume":"5","author":"Bujnicki","year":"2001","journal-title":"PROTEINS: Structure, Function, and Genetics Suppl."},{"key":"2023013110024205100_B4","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1034\/j.1399-0039.2003.00112.x","article-title":"Sensitive quantitative predictions of peptide-MHC binding by a \u2018Query by Committee\u2019 artificial neural network approach","volume":"62","author":"Buus","year":"2003","journal-title":"Tissue antigens"},{"key":"2023013110024205100_B5","doi-asserted-by":"crossref","first-page":"5322","DOI":"10.1109\/IEMBS.2006.259832","article-title":"A meta-predictor for MHC class II binding peptides based on naive Bayesian approach","volume":"1","author":"Huang","year":"2006","journal-title":"Conf. 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