{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:45:37Z","timestamp":1773787537808,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"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":["61872094"],"award-info":[{"award-number":["61872094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"DOI":"10.13039\/100020441","name":"Shanghai Center for Brain Science and Brain-Inspired Technology","doi-asserted-by":"crossref","award":["B18015"],"award-info":[{"award-number":["B18015"]}],"id":[{"id":"10.13039\/100020441","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Municipal Science and Technology Major","award":["2017SHZDZX01"],"award-info":[{"award-number":["2017SHZDZX01"]}]},{"name":"Information Technology Facility, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences"},{"name":"MEXT KAKENHI","award":["19H04169"],"award-info":[{"award-number":["19H04169"]}]},{"name":"MEXT KAKENHI","award":["20F20809"],"award-info":[{"award-number":["20F20809"]}]},{"name":"MEXT KAKENHI","award":["21H05027"],"award-info":[{"award-number":["21H05027"]}]},{"name":"MEXT KAKENHI","award":["22H03645"],"award-info":[{"award-number":["22H03645"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,24]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Computationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with a binding interaction convolution layer, which allows to integrate all potential binding cores (in a given peptide) with the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as 5-fold cross-validation, leave one molecule out, validation with independent testing sets and binding core prediction. All these results and visualization of the predicted binding cores indicate the effectiveness of our model, DeepMHCII, and the importance of properly modeling biological facts in deep learning for high predictive performance and efficient knowledge discovery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>DeepMHCII is publicly available at https:\/\/github.com\/yourh\/DeepMHCII.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac225","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:10:15Z","timestamp":1649934615000},"page":"i220-i228","source":"Crossref","is-referenced-by-count":34,"title":["DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction"],"prefix":"10.1093","volume":"38","author":[{"given":"Ronghui","family":"You","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Sciences, Fudan University , Shanghai 200433, China"}]},{"given":"Wei","family":"Qu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Sciences, Fudan University , Shanghai 200433, China"},{"name":"Shanghai Qi Zhi Institute , Shanghai 200030, China"}]},{"given":"Hiroshi","family":"Mamitsuka","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Kyoto Prefecture, Japan"},{"name":"Department of Computer Science, Aalto University , Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6067-5312","authenticated-orcid":false,"given":"Shanfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Sciences, Fudan University , Shanghai 200433, China"},{"name":"Shanghai Qi Zhi Institute , Shanghai 200030, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education , Shanghai 200433, China"},{"name":"Shanghai Key Lab of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm, Fudan University , Shanghai 200433, China"},{"name":"Zhangjiang Fudan International Innovation Center , Shanghai 200433, China"},{"name":"National Genomics Data Center, CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2023041407553464400_","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s00251-015-0873-y","article-title":"Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification","volume":"67","author":"Andreatta","year":"2015","journal-title":"Immunogenetics"},{"key":"2023041407553464400_","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1093\/bioinformatics\/btx820","article-title":"An automated benchmarking platform for MHC class II binding prediction methods","volume":"34","author":"Andreatta","year":"2018","journal-title":"Bioinformatics"},{"key":"2023041407553464400_","doi-asserted-by":"crossref","first-page":"739","DOI":"10.4049\/jimmunol.169.2.739","article-title":"The majority of immunogenic epitopes generate CD4+ T cells that are dependent on MHC class II-bound peptide-flanking residues","volume":"169","author":"Arnold","year":"2002","journal-title":"J. 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