{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T14:13:32Z","timestamp":1783433612379,"version":"3.54.6"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"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":["32070659"],"award-info":[{"award-number":["32070659"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["JCYJ20200109150003938"],"award-info":[{"award-number":["JCYJ20200109150003938"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Province Basic and Applied Basic Research Fund","award":["2021A1515012447"],"award-info":[{"award-number":["2021A1515012447"]}]},{"name":"Ganghong Young Scholar Development Fund","award":["2021E007"],"award-info":[{"award-number":["2021E007"]}]},{"name":"Shenzhen-Hong Kong Cooperation Zone for Technology and Innovation","award":["HZQB-KCZYB-2020056"],"award-info":[{"award-number":["HZQB-KCZYB-2020056"]}]},{"name":"Warshel Institute for Computational Biology","award":["XX"],"award-info":[{"award-number":["XX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase\u2013kinase similarity network was generated by merging the sequence-, functional-, protein-domain- and \u2018STRING\u2019-related similarities. Thus, besides sequence data, protein\u2013protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the \u2018TK\u2019, \u2018Other\u2019, \u2018STE\u2019, \u2018CAMK\u2019, \u2018TKL\u2019, \u2018CMGC\u2019, \u2018AGC\u2019, \u2018CK1\u2019 and \u2018Atypical\u2019 groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.<\/jats:p>","DOI":"10.1093\/bib\/bbac624","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T01:59:10Z","timestamp":1677031150000},"source":"Crossref","is-referenced-by-count":6,"title":["Holistic similarity-based prediction of phosphorylation sites for understudied kinases"],"prefix":"10.1093","volume":"24","author":[{"given":"Renfei","family":"Ma","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen Warshel Institute for Computational Biology, School of Medicine, , Longcheng Street, 518172, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangfu","family":"Li","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, 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