Journal Article

Prediction of kinase-specific phosphorylation sites using conditional random fields

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

in Bioinformatics

Volume 24, issue 24, pages 2857-2864
Published in print December 2008 | ISSN: 1367-4803
Published online October 2008 | e-ISSN: 1460-2059 | DOI:

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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


Suplementary Information: Supplementary data are available at

Journal Article.  5115 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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