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Bioinformatics Advance Access originally published online on October 20, 2008
Bioinformatics 2008 24(24):2857-2864; doi:10.1093/bioinformatics/btn546
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Prediction of kinase-specific phosphorylation sites using conditional random fields

Thanh Hai Dang 1, Koenraad Van Leemput 2, Alain Verschoren 1 and Kris Laukens 1,*

1Intelligent Systems Laboratory and 2Advanced Database Research and Modelling, Department of Mathematics and Computer Science, Middelheimlaan 1, B-2020 Antwerpen, Belgium

*To whom correspondence should be addressed.


  Abstract

Motivation: Phosphorylation is a crucial post-translational protein modification mechanism with important regulatory functions in biological systems. It is catalyzed by a group of enzymes called kinases, each of which recognizes certain target sites in its substrate proteins. Several authors have built computational models trained from sets of experimentally validated phosphorylation sites to predict these target sites for each given kinase. All of these models suffer from certain limitations, such as the fact that they do not take into account the dependencies between amino acid motifs within protein sequences in a global fashion.

Results: We propose a novel approach to predict phosphorylation sites from the protein sequence. The method uses a positive dataset to train a conditional random field (CRF) model. The negative training dataset is used to specify the decision threshold corresponding to a desired false positive rate. Application of the method on experimentally verified benchmark phosphorylation data (Phospho.ELM) shows that it performs well compared to existing methods for most kinases. This is to our knowledge that the first report of the use of CRFs to predict post-translational modification sites in protein sequences.

Availability: The source code of the implementation, called CRPhos, is available from http://www.ptools.ua.ac.be/CRPhos/

Contact: kris.laukens{at}ua.ac.be

Suplementary Information: Supplementary data are available at http://www.ptools.ua.ac.be/CRPhos/

Associate Editor: Alex Bateman

Received on July 14, 2008; revised on September 12, 2008; accepted on October 17, 2008

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