{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:13:52Z","timestamp":1783052032526,"version":"3.54.6"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"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":["#62376254"],"award-info":[{"award-number":["#62376254"]}],"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":["#32341018"],"award-info":[{"award-number":["#32341018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007156","name":"Hong Kong Innovation and Technology Fund","doi-asserted-by":"crossref","award":["ITS\/241\/21"],"award-info":[{"award-number":["ITS\/241\/21"]}],"id":[{"id":"10.13039\/501100007156","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Structure-based protein design is crucial for designing proteins with novel structures and functions, which aims to generate sequences that fold into desired structures. Current deep learning-based methods primarily focus on training and evaluating models using sequence recovery-based metrics. However, this approach overlooks the inherent ambiguity in the relationship between protein sequences and structures. Relying solely on sequence recovery as a training objective limits the models\u2019 ability to produce diverse sequences that maintain similar structures. These limitations become more pronounced when dealing with remote homologous proteins, which share functional and structural similarities despite low-sequence identity.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present DivPro, a model that learns to design diverse sequences that can fold into similar structures. To improve sequence diversity, instead of learning a single fixed sequence representation for an input structure as in existing methods, DivPro learns a probabilistic sequence space from which diverse sequences could be sampled. We leverage the recent advancements in in silico protein structure prediction. By incorporating structure prediction results as training guidance, DivPro ensures that sequences sampled from this learned space reliably fold into the target structure. We conducted extensive experiments on three sequence design benchmarks and evaluated the structures of designed sequences using structure prediction models including AlphaFold2. Results show that DivPro can maintain high structure recovery while significantly improving the sequence diversity.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and datasets are available at https:\/\/github.com\/veghen\/DivPro.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf258","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:02:48Z","timestamp":1752584568000},"page":"i382-i390","source":"Crossref","is-referenced-by-count":1,"title":["DivPro: diverse protein sequence design with direct structure recovery guidance"],"prefix":"10.1093","volume":"41","author":[{"given":"Xinyi","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong , Hong Kong 999077,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guibao","family":"Shen","sequence":"additional","affiliation":[{"name":"Information Hub, The Hong Kong University of Science and Technology , Guangzhou 511466,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingcong","family":"Chen","sequence":"additional","affiliation":[{"name":"Information Hub, The Hong Kong University of Science and Technology , Guangzhou 511466,","place":["China"]},{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology , Clear Water Bay, Kowloon , Hong Kong 999077,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyong","family":"Chen","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine Chinese Academy of Science , Qiantang District , Hangzhou Zhejiang Province 310000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pheng Ann","family":"Heng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong , Hong Kong 999077,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"2025071509023993800_btaf258-B1","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1038\/s41467-022-28313-9","article-title":"Protein sequence design with a learned potential","volume":"13","author":"Anand","year":"2022","journal-title":"Nat Commun"},{"key":"2025071509023993800_btaf258-B2","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1038\/s41467-023-38519-0","article-title":"Identification of a covert evolutionary pathway between two protein folds","volume":"14","author":"Chakravarty","year":"2023","journal-title":"Nat 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