Journal Article

Identification of substrates for Ser/Thr kinases using residue-based statistical pair potentials

Narendra Kumar and Debasisa Mohanty

in Bioinformatics

Volume 26, issue 2, pages 189-197
Published in print January 2010 | ISSN: 1367-4803
Published online November 2009 | e-ISSN: 1460-2059 | DOI:
Identification of substrates for Ser/Thr kinases using residue-based statistical pair potentials

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology


Show Summary Details


Motivation:In silico methods are being widely used for identifying substrates for various kinases and deciphering cell signaling networks. However, most of the available phosphorylation site prediction methods use motifs or profiles derived from a known data set of kinase substrates and hence, their applicability is limited to only those kinase families for which experimental substrate data is available. This prompted us to develop a novel multi-scale structure-based approach which does not require training using experimental substrate data.

Results:In this work, for the first time, we have used residue-based statistical pair potentials for scoring the binding energy of various substrate peptides in complex with kinases. Extensive benchmarking on Phospho.ELM data set indicate that our method outperforms other structure-based methods and has a prediction accuracy comparable to available sequence-based methods. We also demonstrate that the rank of the true substrate can be further improved, if the high-scoring candidate substrates that are short-listed based on pair potential score, are modeled using all atom forcefield and MM/PBSA approach.


Supplementary information: Supplementary data are available at Bioinformatics Online.

Journal Article.  7572 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.