{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:56:45Z","timestamp":1779339405138,"version":"3.51.4"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002251"],"award-info":[{"award-number":["62002251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272335"],"award-info":[{"award-number":["62272335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province Youth Fund","award":["BK20200856"],"award-info":[{"award-number":["BK20200856"]}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC\u2013peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC\u2013peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, we propose RPEMHC, a new deep learning approach based on residue\u2013residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue\u2013residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC\u2013peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC\u2013peptide interactions and can potentially facilitate the vaccine development.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>The source code of the method along with trained models is freely available at https:\/\/github.com\/lennylv\/RPEMHC.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad785","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T18:12:02Z","timestamp":1704391922000},"source":"Crossref","is-referenced-by-count":18,"title":["RPEMHC: improved prediction of MHC\u2013peptide binding affinity by a deep learning approach based on residue\u2013residue pair encoding"],"prefix":"10.1093","volume":"40","author":[{"given":"Xuejiao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and 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