Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)

Open Access Research

PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship

Inkyung Jung1, Akihisa Matsuyama2, Minoru Yoshida2 and Dongsup Kim13*

Author Affiliations

1 Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, S. Korea

2 Chemical Genetics Laboratory, RIKEN, Wako, Saitama 351-0198, Japan

3 KAIST Institute for BioCentury, KAIST, Daejeon 305-701, S. Korea

For all author emails, please log on.

BMC Bioinformatics 2010, 11(Suppl 1):S10  doi:10.1186/1471-2105-11-S1-S10

Published: 18 January 2010

Abstract

Background

Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.

Results

Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors.

Conclusion

Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.