{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T12:48:02Z","timestamp":1784724482260,"version":"3.55.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"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\/100016909","name":"Microsoft Research Asia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016909","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Gradient descent-based protein modeling is a popular protein structure prediction approach that takes as input the predicted inter-residue distances and other necessary constraints and folds protein structures by minimizing protein-specific energy potentials. The constraints from multiple predicted protein properties provide redundant and sometime conflicting information that can trap the optimization process into local minima and impairs the modeling efficiency.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address these issues, we developed a self-adaptive protein modeling framework, SAMF. It eliminates redundancy of constraints and resolves conflicts, folds protein structures in an iterative way, and picks up the best structures by a deep quality analysis system. Without a large amount of complicated domain knowledge and numerous patches as barriers, SAMF achieves the state-of-the-art performance by exploiting the power of cutting-edge techniques of deep learning. SAMF has a modular design and can be easily customized and extended. As the quality of input constraints is ever growing, the superiority of SAMF will be amplified over time.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and data for reproducing the results is available at https:\/\/msracb.blob.core.windows.net\/pub\/psp\/SAMF.zip.<\/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\/btab411","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T11:28:46Z","timestamp":1622028526000},"page":"4075-4082","source":"Crossref","is-referenced-by-count":9,"title":["SAMF: a self-adaptive protein modeling framework"],"prefix":"10.1093","volume":"37","author":[{"given":"Wenze","family":"Ding","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University , Beijing 100084, China"},{"name":"Beijing Advanced Innovation Center for Structural Biology, Tsinghua University , Beijing 100084, China"},{"name":"Microsoft Research Asia , Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qijiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University , Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou 510006, China"},{"name":"Microsoft Research Asia , Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9483-0050","authenticated-orcid":false,"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Shao","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5532-1640","authenticated-orcid":false,"given":"Haipeng","family":"Gong","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University , Beijing 100084, China"},{"name":"Beijing Advanced Innovation Center for Structural Biology, Tsinghua University , Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tie-Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"2023051607110706700_btab411-B1","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"2023051607110706700_btab411-B2","author":"Agarap","year":"2018"},{"key":"2023051607110706700_btab411-B3","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1021\/acs.jctc.7b00125","article-title":"The Rosetta all-atom energy function for macromolecular modeling and design","volume":"13","author":"Alford","year":"2017","journal-title":"J. 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