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PLoS One. 2018 Oct 2;13(10):e0203840. doi: 10.1371/journal.pone.0203840. eCollection 2018.

Prediction of prkC-mediated protein serine/threonine phosphorylation sites for bacteria.

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Key Laboratory of Oral Medicine, Guangzhou Institute of Oral Disease, Stomatology Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Department of Thoracic Surgery, China Meitan General Hospital, Beijing, China.
School of Arts and Media, Hefei Normal University, Hefei, Anhui, China.
Healthtimegene Institute, Shenzhen, China.


As an abundant post-translational modification, reversible phosphorylation is critical for the dynamic regulation of various biological processes. prkC, a critical serine/threonine-protein kinase in bacteria, plays important roles in regulation of signaling transduction. Identification of prkC-specific phosphorylation sites is fundamental for understanding the molecular mechanism of phosphorylation-mediated signaling. However, experimental identification of substrates for prkC is time-consuming and labor-intensive, and computational methods for kinase-specific phosphorylation prediction in bacteria have yet to be developed. In this study, we manually curated the experimentally identified substrates and phosphorylation sites of prkC from the published literature. The analyses of the sequence preferences showed that the substrate recognition pattern for prkC might be miscellaneous, and a complex strategy should be employed to predict potential prkC-specific phosphorylation sites. To develop the predictor, the amino acid location feature extraction method and the support vector machine algorithm were employed, and the methods achieved promising performance. Through 10-fold cross validation, the predictor reached a sensitivity of 91.67% at the specificity of 95.12%. Then, we developed freely accessible software, which is provided at Based on the predictor, hundreds of potential prkC-specific phosphorylation sites were annotated based on the known bacterial phosphorylation sites. prkC-PSP was the first predictor for prkC-specific phosphorylation sites, and its prediction performance was promising. We anticipated that these analyses and the predictor could be helpful for further studies of prkC-mediated phosphorylation.

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