{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T16:02:14Z","timestamp":1775750534463,"version":"3.50.1"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"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\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2020YFB0204803"],"award-info":[{"award-number":["2020YFB0204803"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772566"],"award-info":[{"award-number":["61772566"]}],"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":["62041209"],"award-info":[{"award-number":["62041209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Key Field R&D Plan","award":["2019B020228001"],"award-info":[{"award-number":["2019B020228001"]}]},{"name":"Guangdong Key Field R&D Plan","award":["2018B010109006"],"award-info":[{"award-number":["2018B010109006"]}]},{"name":"Introducing Innovative and Entrepreneurial Teams","award":["2016ZT06D211"],"award-info":[{"award-number":["2016ZT06D211"]}]},{"name":"Guangzhou S&T Research Plan","award":["202007030010"],"award-info":[{"award-number":["202007030010"]}]},{"name":"Shenzhen Science and Technology Program","award":["KQTD20170330155106581"],"award-info":[{"award-number":["KQTD20170330155106581"]}]},{"name":"Major Program of Shenzhen Bay Laboratory","award":["S201101001"],"award-info":[{"award-number":["S201101001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein\u2013protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a deep graph-based framework deep Graph convolutional network for Protein\u2013Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https:\/\/github.com\/biomed-AI\/GraphPPIS. The GraphPPIS web server is freely available at https:\/\/biomed.nscc-gz.cn\/apps\/GraphPPIS.<\/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\/btab643","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T11:46:10Z","timestamp":1631101570000},"page":"125-132","source":"Crossref","is-referenced-by-count":168,"title":["Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6098-9103","authenticated-orcid":false,"given":"Qianmu","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7999-2070","authenticated-orcid":false,"given":"Jianwen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000, China"}]},{"given":"Huiying","family":"Zhao","sequence":"additional","affiliation":[{"name":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University , Guangzhou 510000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9958-5699","authenticated-orcid":false,"given":"Yaoqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Peking University Shenzhen Graduate School , Shenzhen 518055, China"},{"name":"Shenzhen Bay Laboratory , Shenzhen 518055, China"},{"name":"Institute for Glycomics, Griffith University, Parklands Drive , Southport, QLD 4215, Australia"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000, China"},{"name":"Key Laboratory of Machine Intelligence and Advanced Computing of MOE, Sun Yat-sen University , Guangzhou 510000, China"}]}],"member":"286","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"2023020108410111000_btab643-B1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J. 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