{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T16:36:53Z","timestamp":1785343013218,"version":"3.55.0"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61725302"],"award-info":[{"award-number":["61725302"]}],"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":["62073219"],"award-info":[{"award-number":["62073219"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state and register. However, most of the existing computational tools only focus on one of them.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e. CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b and e are more crucial for the oligomeric state prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>CoCoPRED is available at http:\/\/www.csbio.sjtu.edu.cn\/bioinf\/CoCoPRED. The datasets used in this research can also be downloaded from the website.<\/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\/btab744","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T11:15:47Z","timestamp":1635333347000},"page":"720-729","source":"Crossref","is-referenced-by-count":22,"title":["CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9659-2781","authenticated-orcid":false,"given":"Shi-Hao","family":"Feng","sequence":"first","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China , Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Qiu","family":"Xia","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China , Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4029-3325","authenticated-orcid":false,"given":"Hong-Bin","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China , Shanghai 200240, China"},{"name":"Department of Computer Science, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering , Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"2023020108505407200_btab744-B1","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"2023020108505407200_btab744-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1741-7007-7-50","article-title":"Mapping the human membrane proteome: a majority of the human membrane proteins can be classified according to function and evolutionary origin","volume":"7","author":"Alm\u00e9n","year":"2009","journal-title":"BMC Biol"},{"key":"2023020108505407200_btab744-B3","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023020108505407200_btab744-B4","doi-asserted-by":"crossref","first-page":"1908","DOI":"10.1093\/bioinformatics\/btr299","article-title":"SCORER 2.0: an algorithm for distinguishing parallel dimeric and trimeric coiled-coil sequences","volume":"27","author":"Armstrong","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020108505407200_btab744-B5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1093\/bioinformatics\/9.2.141","article-title":"Prediction of protein secondary structure by the hidden Markov model","volume":"9","author":"Asai","year":"1993","journal-title":"Bioinformatics"},{"key":"2023020108505407200_btab744-B6","article-title":"Layer normalization","author":"Ba","year":"2016"},{"key":"2023020108505407200_btab744-B7","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1093\/bioinformatics\/btp539","article-title":"CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information","volume":"25","author":"Bartoli","year":"2009","journal-title":"Bioinformatics"},{"key":"2023020108505407200_btab744-B8","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0022-2836(77)80200-3","article-title":"The Protein Data Bank: a computer-based archival file for macromolecular structures","volume":"112","author":"Bernstein","year":"1977","journal-title":"J. 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