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Since experimental methods to detect kinases at specific phosphorylation sites are expensive and accidental, some computational methods have been proposed to predict the kinase of these sites, but most methods only consider single sequence information or single functional network information. In this study, a new method Predicting Kinase of Specific Phosphorylation Sites (PKSPS) is developed to predict kinases of specific phosphorylation sites in human proteins by combining PKSPS-Net with PKSPS-Seq, which considers protein\u2013protein interaction (PPI) network information and sequence information. For PKSPS-Net, kinase\u2013kinase and substrate\u2013substrate similarity are quantified based on the topological similarity of proteins in the PPI network, and maximum weighted bipartite matching algorithm is proposed to predict kinase\u2013substrate relationship. In PKSPS-Seq, phosphorylation sequence enrichment analysis is used to analyze the similarity of local sequences around phosphorylation sites and predict the kinase of specific phosphorylation sites (KSP). PKSPS has been proved to be more effective than the PKSPS-Net or PKSPS-Seq on different sets of kinases. Further comparison results show that the PKSPS method performs better than existing methods. Finally, the case study demonstrates the effectiveness of the PKSPS in predicting kinases of specific phosphorylation sites. The open source code and data of the PKSPS can be obtained from https:\/\/github.com\/guoxinyunncu\/PKSPS.<\/jats:p>","DOI":"10.1093\/bib\/bbab436","type":"journal-article","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T12:47:13Z","timestamp":1632401233000},"source":"Crossref","is-referenced-by-count":5,"title":["PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis"],"prefix":"10.1093","volume":"23","author":[{"given":"Xinyun","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"He","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Numerical Simulation and High-Performance 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