{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T07:55:16Z","timestamp":1772697316067,"version":"3.50.1"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T00:00:00Z","timestamp":1559865600000},"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":["11871290"],"award-info":[{"award-number":["11871290"]}],"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":["61873185"],"award-info":[{"award-number":["61873185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004806","name":"Fok Ying-Tong Education Foundation","doi-asserted-by":"crossref","award":["161003"],"award-info":[{"award-number":["161003"]}],"id":[{"id":"10.13039\/501100004806","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"KLMDASR"},{"name":"Thousand Youth Talents Plan of China"},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Based on the improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning-based contact prediction method. MapPred consists of two component methods, DeepMSA and DeepMeta, both trained with the residual neural networks. DeepMSA was inspired by the recent method DeepCov, which was trained on 441 matrices of covariance features. By considering the symmetry of contact map, we reduced the number of matrices to 231, which makes the training more efficient in DeepMSA. Experiments show that DeepMSA outperforms DeepCov by 10\u201313% in precision. DeepMeta works by combining predicted contacts and other sequence profile features. Experiments on three benchmark datasets suggest that the contribution from the metagenome sequence data is significant with P-values less than 4.04E-17. MapPred is shown to be complementary and comparable the state-of-the-art methods. The success of MapPred is attributed to three factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA and optimized training by the residual neural networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>http:\/\/yanglab.nankai.edu.cn\/mappred\/.<\/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\/btz477","type":"journal-article","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T19:12:29Z","timestamp":1559675549000},"page":"41-48","source":"Crossref","is-referenced-by-count":74,"title":["Protein contact prediction using metagenome sequence data and residual neural networks"],"prefix":"10.1093","volume":"36","author":[{"given":"Qi","family":"Wu","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenling","family":"Peng","sequence":"additional","affiliation":[{"name":"Center for Applied Mathematics, Tianjin University , Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Anishchenko","sequence":"additional","affiliation":[{"name":"Department of Biochemistry , Seattle, WA 98105, USA"},{"name":"Institute for Protein Design, University of Washington , Seattle, WA 98105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Cong","sequence":"additional","affiliation":[{"name":"Department of Biochemistry , Seattle, WA 98105, USA"},{"name":"Institute for Protein Design, University of Washington , Seattle, WA 98105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Baker","sequence":"additional","affiliation":[{"name":"Department of Biochemistry , Seattle, WA 98105, USA"},{"name":"Institute for Protein Design, University of Washington , Seattle, WA 98105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2912-7737","authenticated-orcid":false,"given":"Jianyi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,6,7]]},"reference":[{"key":"2023013109504987300_btz477-B1","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1093\/bioinformatics\/btx781","article-title":"DNCON2: improved protein contact prediction using two-level deep convolutional neural networks","volume":"34","author":"Adhikari","year":"2018","journal-title":"Bioinformatics"},{"key":"2023013109504987300_btz477-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":"2023013109504987300_btz477-B3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/1471-2105-8-113","article-title":"Improved residue contact prediction using support vector machines and a large feature set","volume":"8","author":"Cheng","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023013109504987300_btz477-B4","doi-asserted-by":"crossref","first-page":"012707","DOI":"10.1103\/PhysRevE.87.012707","article-title":"Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models","volume":"87","author":"Ekeberg","year":"2013","journal-title":"Phys. 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