{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T01:42:49Z","timestamp":1784857369136,"version":"3.55.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["T2225007"],"award-info":[{"award-number":["T2225007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["T2222012"],"award-info":[{"award-number":["T2222012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"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>It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/yanglab.nankai.edu.cn\/UniDoc\/.<\/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\/btad070","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T15:27:06Z","timestamp":1675351626000},"source":"Crossref","is-referenced-by-count":42,"title":["A unified approach to protein domain parsing with inter-residue distance matrix"],"prefix":"10.1093","volume":"39","author":[{"given":"Kun","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Su","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0303-6693","authenticated-orcid":false,"given":"Zhenling","family":"Peng","sequence":"additional","affiliation":[{"name":"Ministry of Education Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University , Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2912-7737","authenticated-orcid":false,"given":"Jianyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Ministry of Education Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University , Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"2023021110513793500_btad070-B1","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1093\/bioinformatics\/btg006","article-title":"PDP: protein domain parser","volume":"19","author":"Alexandrov","year":"2003","journal-title":"Bioinformatics"},{"key":"2023021110513793500_btad070-B2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2023021110513793500_btad070-B3","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1093\/nar\/gkn944","article-title":"FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator","volume":"37","author":"Bondugula","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023021110513793500_btad070-B4","doi-asserted-by":"crossref","first-page":"e1003926","DOI":"10.1371\/journal.pcbi.1003926","article-title":"ECOD: an evolutionary classification of protein domains","volume":"10","author":"Cheng","year":"2014","journal-title":"PLoS Comput. 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