{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T14:57:02Z","timestamp":1761490622438,"version":"3.37.3"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2018,8,4]],"date-time":"2018-08-04T00:00:00Z","timestamp":1533340800000},"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\/501100004052","name":"King Abdullah University of Science and Technology","doi-asserted-by":"publisher","award":["FCC\/1\/1976-04","URF\/1\/2602-01","URF\/1\/3007-01"],"award-info":[{"award-number":["FCC\/1\/1976-04","URF\/1\/2602-01","URF\/1\/3007-01"]}],"id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM089753"],"award-info":[{"award-number":["R01GM089753"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI-1564955"],"award-info":[{"award-number":["DBI-1564955"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>PredMP is the first web service, to our knowledge, that aims at de novo prediction of the membrane protein (MP) 3D structure followed by the embedding of the MP into the lipid bilayer for visualization. Our approach is based on a high-throughput Deep Transfer Learning (DTL) method that first predicts MP contacts by learning from non-MPs and then predicts the 3D model of the MP using the predicted contacts as distance restraints. This algorithm is derived from our previous Deep Learning (DL) method originally developed for soluble protein contact prediction, which has been officially ranked No. 1 in CASP12. The DTL framework in our approach overcomes the challenge that there are only a limited number of solved MP structures for training the deep learning model. There are three modules in the PredMP server: (i) The DTL framework followed by the contact-assisted folding protocol has already been implemented in RaptorX-Contact, which serves as the key module for 3D model generation; (ii) The 1D annotation module, implemented in RaptorX-Property, is used to predict the secondary structure and disordered regions; and (iii) the visualization module to display the predicted MPs embedded in the lipid bilayer guided by the predicted transmembrane topology.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Tested on 510 non-redundant MPs, our server predicts correct folds for \u223c290 MPs, which significantly outperforms existing methods. Tested on a blind and live benchmark CAMEO from September 2016 to January 2018, PredMP can successfully model all 10 MPs belonging to the hard category.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>PredMP is freely accessed on the web at http:\/\/www.predmp.com.<\/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\/bty684","type":"journal-article","created":{"date-parts":[[2018,8,2]],"date-time":"2018-08-02T19:34:38Z","timestamp":1533238478000},"page":"691-693","source":"Crossref","is-referenced-by-count":26,"title":["PredMP: a web server for <i>de novo<\/i> prediction and visualization of membrane proteins"],"prefix":"10.1093","volume":"35","author":[{"given":"Sheng","family":"Wang","sequence":"first","affiliation":[{"name":"Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia"}]},{"given":"Shiyang","family":"Fei","sequence":"additional","affiliation":[{"name":"COMPASS, New York, NY, USA"}]},{"given":"Zongan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Chemistry, James Franck Institute, University of Chicago, Chicago, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3664-6722","authenticated-orcid":false,"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia"}]},{"given":"Jinbo","family":"Xu","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute at Chicago, Chicago, IL, USA"}]},{"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Prospect Institute of Fatty Acids and Health, Qingdao University, Ningxia, China"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia"}]}],"member":"286","published-online":{"date-parts":[[2018,8,4]]},"reference":[{"key":"2023051601060024700_bty684-B1","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1107\/S0907444998003254","article-title":"Crystallography & NMR system: a new software suite for macromolecular structure determination","volume":"54","author":"Brunger","year":"1998","journal-title":"Acta Crystallogr. 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