{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:40:58Z","timestamp":1773945658542,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2133650"],"award-info":[{"award-number":["IIS-2133650"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10\u00a0000 known human MHC Class I proteins with and without experimental data. These computed motifs demonstrated high similarities with those derived from experimental data. In addition, we found that the motifs could be used for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The software can be found in https:\/\/github.com\/minrq\/pMHC.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad055","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T21:02:48Z","timestamp":1674507768000},"source":"Crossref","is-referenced-by-count":16,"title":["Binding peptide generation for MHC Class I proteins with deep reinforcement learning"],"prefix":"10.1093","volume":"39","author":[{"given":"Ziqi","family":"Chen","sequence":"first","affiliation":[{"name":"Machine Learning Department, NEC Labs America , Princeton, NJ 08540, USA"},{"name":"Computer Science and Engineering Department, The Ohio State University , Columbus, OH 43210, USA"}]},{"given":"Baoyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chemical and Biomolecular Engineering Department, Rice University , Houston, TX 77005, USA"}]},{"given":"Hongyu","family":"Guo","sequence":"additional","affiliation":[{"name":"Digital Technologies Research Centre, National Research Council Canada , Ottawa, ON K1A 0R6, Canada"},{"name":"School of Electrical Engineering and Computer Science, University of Ottawa , Ottawa, ON K1N 6N5, Canada"}]},{"given":"Prashant","family":"Emani","sequence":"additional","affiliation":[{"name":"School of Medicine, Yale University , New Haven, CT 06520, USA"}]},{"given":"Trevor","family":"Clancy","sequence":"additional","affiliation":[{"name":"NEC OncoImmunity AS, Oslo Cancer Cluster , Oslo 0379, Norway"}]},{"given":"Chongming","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Medicine, Baylor College of Medicine , Houston, TX 06520, USA"}]},{"given":"Mark","family":"Gerstein","sequence":"additional","affiliation":[{"name":"School of Medicine, Yale University , New Haven, CT 06520, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6842-1165","authenticated-orcid":false,"given":"Xia","family":"Ning","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, The Ohio State University , Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5002-3417","authenticated-orcid":false,"given":"Chao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Medicine, Baylor College of Medicine , Houston, TX 06520, USA"}]},{"given":"Martin Renqiang","family":"Min","sequence":"additional","affiliation":[{"name":"Machine Learning Department, NEC Labs America , Princeton, NJ 08540, USA"}]}],"member":"286","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"2023020815013333200_btad055-B1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.celrep.2016.12.019","article-title":"Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade","volume":"18","author":"Charoentong","year":"2017","journal-title":"Cell Rep"},{"key":"2023020815013333200_btad055-B2","doi-asserted-by":"crossref","first-page":"634836","DOI":"10.3389\/fmolb.2021.634836","article-title":"Ranking-based convolutional neural network models for peptide-MHC Class I binding prediction","volume":"8","author":"Chen","year":"2021","journal-title":"Front. 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