{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T09:08:55Z","timestamp":1781773735844,"version":"3.54.5"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871361"],"award-info":[{"award-number":["61871361"]}],"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":["61471331"],"award-info":[{"award-number":["61471331"]}],"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":["61971393"],"award-info":[{"award-number":["61971393"]}],"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":["61571414"],"award-info":[{"award-number":["61571414"]}],"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":["61932008"],"award-info":[{"award-number":["61932008"]}],"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":["61772368"],"award-info":[{"award-number":["61772368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2020YFA0712403"],"award-info":[{"award-number":["2020YFA0712403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFC0910500"],"award-info":[{"award-number":["2018YFC0910500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Science and Technology Innovation Fund","award":["19511101404"],"award-info":[{"award-number":["19511101404"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Phosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but most of them are based on convolutional neural network that may not capture enough information about long-range dependencies between residues in a protein sequence. In addition, existing deep learning methods only make use of sequence information for predicting phosphorylation sites, and it is highly desirable to develop a deep learning architecture that can combine heterogeneous sequence and protein\u2013protein interaction (PPI) information for more accurate phosphorylation site prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present a novel integrated deep neural network named PhosIDN, for phosphorylation site prediction by extracting and combining sequence and PPI information. In PhosIDN, a sequence feature encoding sub-network is proposed to capture not only local patterns but also long-range dependencies from protein sequences. Meanwhile, useful PPI features are also extracted in PhosIDN by a PPI feature encoding sub-network adopting a multi-layer deep neural network. Moreover, to effectively combine sequence and PPI information, a heterogeneous feature combination sub-network is introduced to fully exploit the complex associations between sequence and PPI features, and their combined features are used for final prediction. Comprehensive experiment results demonstrate that the proposed PhosIDN significantly improves the prediction performance of phosphorylation sites and compares favorably with existing general and kinase-specific phosphorylation site prediction methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>PhosIDN is freely available at https:\/\/github.com\/ustchangyuanyang\/PhosIDN.<\/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\/btab551","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T19:11:49Z","timestamp":1627413109000},"page":"4668-4676","source":"Crossref","is-referenced-by-count":61,"title":["PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein\u2013protein interaction information"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8406-3129","authenticated-orcid":false,"given":"Hangyuan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China , Hefei AH230027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China , Hefei AH230027, China"},{"name":"Centers for Biomedical Engineering, University of Science and Technology of China , Hefei AH230027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China , Hefei AH230027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4531-3970","authenticated-orcid":false,"given":"Xing-Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai 200433, China"},{"name":"MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and Frontiers Center for Brain Science , Shanghai 200433, China"},{"name":"Research Institute of Intelligent Complex Systems, Fudan University , Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China , Hefei AH230027, China"},{"name":"Centers for Biomedical Engineering, University of Science and Technology of China , Hefei AH230027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"2023051607144900700_btab551-B1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S0076-6879(09)05004-6","article-title":"32P labeling of protein phosphorylation and metabolite association in the mitochondria matrix","volume":"457","author":"Aponte","year":"2009","journal-title":"Methods Enzymol"},{"key":"2023051607144900700_btab551-B2","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1038\/nbt1240","article-title":"A probability-based approach for high-throughput protein phosphorylation analysis and site localization","volume":"24","author":"Beausoleil","year":"2006","journal-title":"Nat. Biotechnol"},{"key":"2023051607144900700_btab551-B3","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1002\/pmic.200300771","article-title":"Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence","volume":"4","author":"Blom","year":"2004","journal-title":"Proteomics"},{"key":"2023051607144900700_btab551-B4","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1093\/bib\/bby089","article-title":"Large-scale comparative assessment of computational predictors for lysine post-translational modification sites","volume":"20","author":"Chen","year":"2019","journal-title":"Brief. Bioinf"},{"key":"2023051607144900700_btab551-B5","doi-asserted-by":"crossref","first-page":"E127","DOI":"10.1038\/ncb0502-e127","article-title":"The origins of protein phosphorylation","volume":"4","author":"Cohen","year":"2002","journal-title":"Nat. Cell Biol"},{"key":"2023051607144900700_btab551-B6","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/nar\/gkq973","article-title":"The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored","volume":"39","author":"Damian","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2023051607144900700_btab551-B7","doi-asserted-by":"crossref","first-page":"3652","DOI":"10.1093\/bioinformatics\/btaa013","article-title":"DeepKinZero: zero-shot learning for predicting kinase\u2013phosphosite associations involving understudied kinases","volume":"36","author":"Deznabi","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B8","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1007\/s00726-014-1711-5","article-title":"PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine","volume":"46","author":"Dou","year":"2014","journal-title":"Amino Acids"},{"key":"2023051607144900700_btab551-B9","first-page":"265","author":"Dou","year":"2017"},{"key":"2023051607144900700_btab551-B10","doi-asserted-by":"crossref","first-page":"e1004049","DOI":"10.1371\/journal.pcbi.1004049","article-title":"The roles of post-translational modifications in the context of protein interaction networks","volume":"11","author":"Duan","year":"2015","journal-title":"PLoS Comput. Biol"},{"key":"2023051607144900700_btab551-B11","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/978-1-60327-241-4_21","article-title":"Prediction of posttranslational modification of proteins from their amino acid sequence","volume":"609","author":"Eisenhaber","year":"2010","journal-title":"Methods Mol. Biol"},{"key":"2023051607144900700_btab551-B12","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1007\/s00726-014-1669-3","article-title":"Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest","volume":"46","author":"Fan","year":"2014","journal-title":"Amino Acids"},{"key":"2023051607144900700_btab551-B13","doi-asserted-by":"crossref","first-page":"2586","DOI":"10.1074\/mcp.M110.001388","article-title":"Musite, a tool for global prediction of general and kinase-specific phosphorylation sites","volume":"9","author":"Gao","year":"2010","journal-title":"Mol. Cell. Proteomics"},{"key":"2023051607144900700_btab551-B14","first-page":"317","author":"Gao","year":"2016"},{"key":"2023051607144900700_btab551-B15","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1093\/bioinformatics\/btw678","article-title":"Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks","volume":"33","author":"Hanson","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B16","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-11-S1-S10","article-title":"PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship","volume":"11","author":"Jung","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023051607144900700_btab551-B17","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.1093\/bioinformatics\/bty166","article-title":"DeepSol: a deep learning framework for sequence-based protein solubility prediction","volume":"34","author":"Khurana","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B18","author":"Kingma","year":"2014"},{"key":"2023051607144900700_btab551-B19","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1093\/bioinformatics\/btx624","article-title":"DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier","volume":"34","author":"Kulmanov","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B20","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1093\/bioinformatics\/bty522","article-title":"Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome","volume":"34","author":"Li","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B21","doi-asserted-by":"crossref","first-page":"e15411","DOI":"10.1371\/journal.pone.0015411","article-title":"Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources","volume":"5","author":"Li","year":"2010","journal-title":"PLoS One"},{"key":"2023051607144900700_btab551-B22","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1016\/j.cell.2007.05.052","article-title":"Systematic discovery of in vivo phosphorylation networks","volume":"129","author":"Linding","year":"2007","journal-title":"Cell"},{"key":"2023051607144900700_btab551-B23","doi-asserted-by":"crossref","first-page":"946","DOI":"10.7150\/ijbs.24121","article-title":"PTM-ssMP: a web server for predicting different types of post-translational modification sites using novel site-specific modification profile","volume":"14","author":"Liu","year":"2018","journal-title":"Int. J. Biol. Sci"},{"key":"2023051607144900700_btab551-B24","doi-asserted-by":"crossref","first-page":"2766","DOI":"10.1093\/bioinformatics\/bty1051","article-title":"DeepPhos: prediction of protein phosphorylation sites with deep learning","volume":"35","author":"Luo","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B25","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/nmeth.4627","article-title":"Using deep learning to model the hierarchical structure and function of a cell","volume":"15","author":"Ma","year":"2018","journal-title":"Nat. Methods"},{"key":"2023051607144900700_btab551-B26","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.cell.2005.02.031","article-title":"Phosphorylation and functional inactivation of TSC2 by Erk: implications for tuberous sclerosisand cancer pathogenesis","volume":"121","author":"Ma","year":"2005","journal-title":"Cell"},{"key":"2023051607144900700_btab551-B27","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res"},{"key":"2023051607144900700_btab551-B28","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1038\/nbt0303-255","article-title":"Proteomic analysis of post-translational modifications","volume":"21","author":"Mann","year":"2003","journal-title":"Nat. Biotechnol"},{"key":"2023051607144900700_btab551-B29","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/pro.3978","article-title":"The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions","volume":"30","author":"Oughtred","year":"2021","journal-title":"Protein Sci"},{"key":"2023051607144900700_btab551-B30","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: an open-source package for R and S+ to analyze and compare ROC curves","volume":"12","author":"Robin","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023051607144900700_btab551-B31","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1074\/mcp.M111.012625","article-title":"Systematic analysis of protein phosphorylation networks from phosphoproteomic data","volume":"11","author":"Song","year":"2012","journal-title":"Mol. Cell. Proteomics"},{"key":"2023051607144900700_btab551-B32","doi-asserted-by":"crossref","first-page":"6862","DOI":"10.1038\/s41598-017-07199-4","article-title":"PhosphoPredict: a bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection","volume":"7","author":"Song","year":"2017","journal-title":"Sci. Rep"},{"key":"2023051607144900700_btab551-B33","doi-asserted-by":"crossref","first-page":"2927","DOI":"10.1093\/bioinformatics\/btr525","article-title":"Computational prediction of eukaryotic phosphorylation sites","volume":"27","author":"Trost","year":"2011","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B34","doi-asserted-by":"crossref","first-page":"4599","DOI":"10.1093\/bioinformatics\/btaa531","article-title":"SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction","volume":"36","author":"Uddin","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B35","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"2023051607144900700_btab551-B36","doi-asserted-by":"crossref","first-page":"2386","DOI":"10.1093\/bioinformatics\/bty977","article-title":"Capsule network for protein post-translational modification site prediction","volume":"35","author":"Wang","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B37","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939753","article-title":"Structural Deep Network Embedding","author":"Wang","year":"2016"},{"key":"2023051607144900700_btab551-B38","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1093\/bioinformatics\/btx496","article-title":"MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction","volume":"33","author":"Wang","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B39","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1093\/bioinformatics\/btw377","article-title":"Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization","volume":"32","author":"Wen","year":"2016","journal-title":"Bioinformatics"},{"key":"2023051607144900700_btab551-B40","doi-asserted-by":"crossref","first-page":"428","DOI":"10.7150\/ijbs.5.428","article-title":"Nuclear localization of p38 MAPK in response to DNA damage","volume":"5","author":"Wood","year":"2009","journal-title":"Int. J. Biol. Sci"},{"key":"2023051607144900700_btab551-B41","author":"Xu","year":"2020"},{"key":"2023051607144900700_btab551-B42","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1186\/1471-2105-7-163","article-title":"PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory","volume":"7","author":"Xue","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023051607144900700_btab551-B43","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1074\/mcp.M700574-MCP200","article-title":"GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy","volume":"7","author":"Xue","year":"2008","journal-title":"Mol. Cell. Proteomics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab551\/39806602\/btab551.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/24\/4668\/50334890\/btab551.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/24\/4668\/50334890\/btab551.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T07:45:24Z","timestamp":1684223124000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/24\/4668\/6329824"}},"subtitle":[],"editor":[{"given":"Lenore","family":"Cowen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,7,28]]},"references-count":43,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2021,12,11]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab551","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,12,15]]},"published":{"date-parts":[[2021,7,28]]}}}