{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T18:00:36Z","timestamp":1773684036951,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T00:00:00Z","timestamp":1567728000000},"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":["61832019, 61622213 and 61728211"],"award-info":[{"award-number":["61832019, 61622213 and 61728211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["B18059, G20190018001"],"award-info":[{"award-number":["B18059, G20190018001"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provincial Science and Technology Program","award":["2018WK4001"],"award-info":[{"award-number":["2018WK4001"]}]},{"name":"Central Universities of Central South University","award":["502221903"],"award-info":[{"award-number":["502221903"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Protein\u2013protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to predict PPI sites. Existing computational methods usually use local contextual features to predict PPI sites. Actually, global features of protein sequences are critical for PPI site prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A new end-to-end deep learning framework, named DeepPPISP, through combining local contextual and global sequence features, is proposed for PPI site prediction. For local contextual features, we use a sliding window to capture features of neighbors of a target amino acid as in previous studies. For global sequence features, a text convolutional neural network is applied to extract features from the whole protein sequence. Then the local contextual and global sequence features are combined to predict PPI sites. By integrating local contextual and global sequence features, DeepPPISP achieves the state-of-the-art performance, which is better than the other competing methods. In order to investigate if global sequence features are helpful in our deep learning model, we remove or change some components in DeepPPISP. Detailed analyses show that global sequence features play important roles in DeepPPISP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The DeepPPISP web server is available at http:\/\/bioinformatics.csu.edu.cn\/PPISP\/. The source code can be obtained from https:\/\/github.com\/CSUBioGroup\/DeepPPISP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz699","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T19:28:55Z","timestamp":1567625335000},"page":"1114-1120","source":"Crossref","is-referenced-by-count":261,"title":["Protein\u2013protein interaction site prediction through combining local and global features with deep neural networks"],"prefix":"10.1093","volume":"36","author":[{"given":"Min","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, People\u2019s Republic of China"}]},{"given":"Fuhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, People\u2019s Republic of China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-9332","authenticated-orcid":false,"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan , Saskatoon SKS7N5A9, Canada"}]},{"given":"Yaohang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Old Dominion University , Norfolk, VA 23529, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, People\u2019s Republic of China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, People\u2019s Republic of China"}]}],"member":"286","published-online":{"date-parts":[[2019,9,6]]},"reference":[{"key":"2023013110103816400_btz699-B1","doi-asserted-by":"crossref","first-page":"82.","DOI":"10.1186\/1471-2105-15-82","article-title":"Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor","volume":"15","author":"Bendell","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"2023013110103816400_btz699-B2","doi-asserted-by":"crossref","first-page":"128.","DOI":"10.1186\/1752-0509-6-128","article-title":"Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast","volume":"6","author":"Brettner","year":"2012","journal-title":"BMC Syst. 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