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Amino Acids. 2014 Jun;46(6):1459-69. doi: 10.1007/s00726-014-1711-5. Epub 2014 Mar 13.

PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine.

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1
Center for Plant Science and Innovation, School of Biological Sciences, University of Nebraska, Lincoln, NE, 68588, USA.

Abstract

Phosphorylation is one of the most essential post-translational modifications in eukaryotes. Studies on kinases and their substrates are important for understanding cellular signaling networks. Because of the cost in time and labor associated with large-scale wet-bench experiments, computational prediction of phosphorylation sites becomes important and many computational tools have been developed in the recent decades. The prediction tools can be grouped into two categories: kinase-specific and non-kinase-specific tools. With more kinases being discovered by the new sequencing technologies, accurate non-kinase-specific prediction tools are highly desirable for whole-genome annotation in a wider variety of species. In this manuscript, a support vector machine is used to combine eight different sequence level scoring functions to predict phosphorylation sites. The attributes used by this work, including Shannon entropy, relative entropy, predicted protein secondary structure, predicted protein disorder, solvent accessible area, overlapping properties, averaged cumulative hydrophobicity, and k-nearest neighbor, were able to obtain better results than the previously used attributes by other similar methods. This method achieved AUC values of 0.8405/0.8183/0.7383 for serine (S), threonine (T), and tyrosine (Y) phosphorylation sites, respectively, in animals with a tenfold cross-validation. The model trained by the animal phosphorylation sites was also applied to a plant phosphorylation site dataset as an independent test. The AUC values for the independent test dataset were 0.7761/0.6652/0.5958 for S/T/Y phosphorylation sites, which compared favorably with those of several existing methods. A web server based on our method was constructed for public use. The server, trained model, and all datasets used in the current study are available at http://sysbio.unl.edu/PhosphoSVM .

PMID:
24623121
DOI:
10.1007/s00726-014-1711-5
[Indexed for MEDLINE]
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