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. 2012 Jul 16;3(9):675–690. doi: 10.1007/s13238-012-2048-z

Prediction of functional phosphorylation sites by incorporating evolutionary information

Shen Niu 1, Zhen Wang 1, Dongya Ge 1, Guoqing Zhang 1,2,✉, Yixue Li 1,2,✉
PMCID: PMC4875371  PMID: 22802047

Abstract

Protein phosphorylation is a ubiquitous protein post-translational modification, which plays an important role in cellular signaling systems underlying various physiological and pathological processes. Current in silico methods mainly focused on the prediction of phosphorylation sites, but rare methods considered whether a phosphorylation site is functional or not. Since functional phosphorylation sites are more valuable for further experimental research and a proportion of phosphorylation sites have no direct functional effects, the prediction of functional phosphorylation sites is quite necessary for this research area. Previous studies have shown that functional phosphorylation sites are more conserved than non-functional phosphorylation sites in evolution. Thus, in our method, we developed a web server by integrating existing phosphorylation site prediction methods, as well as both absolute and relative evolutionary conservation scores to predict the most likely functional phosphorylation sites. Using our method, we predicted the most likely functional sites of the human, rat and mouse proteomes and built a database for the predicted sites. By the analysis of overall prediction results, we demonstrated that protein phosphorylation plays an important role in all the enriched KEGG pathways. By the analysis of protein-specific prediction results, we demonstrated the usefulness of our method for individual protein studies. Our method would help to characterize the most likely functional phosphorylation sites for further studies in this research area.

Keywords: protein, phosphorylation, function, conservation, evolution

Footnotes

These authors contributed equally to the work.

Contributor Information

Guoqing Zhang, Email: gqzhang@scbit.org.

Yixue Li, Email: yxli@sibs.ac.cn.

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