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

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

Abstract

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 http://free.cancerbio.info/prkc/. 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bacillus subtilis / chemistry
  • Bacillus subtilis / metabolism*
  • Binding Sites
  • Computational Biology / methods
  • Data Curation
  • Phosphorylation
  • Protein-Serine-Threonine Kinases / chemistry*
  • Protein-Serine-Threonine Kinases / metabolism*
  • Substrate Specificity
  • Support Vector Machine

Substances

  • Protein-Serine-Threonine Kinases

Grant support

This work was supported by grants from the Natural Science Foundation of China (31501069), the Fundamental Research Funds for the Central Universities (SYSU:16ykzd06), and the Science and Technology Planning Project of Guangdong Province (201511013, 2016ZC0147). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.