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Bioinformatics Advance Access originally published online on May 31, 2009
Bioinformatics 2009 25(15):1876-1883; doi:10.1093/bioinformatics/btp339
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Using dynamics-based comparisons to predict nucleic acid binding sites in proteins: an application to OB-fold domains

Andrea Zen 1, Cesira de Chiara 2, Annalisa Pastore 2 and Cristian Micheletti 1,*

1 SISSA, CNR-INFM Democritos and Italian Institute of Technology, Via Beirut 2, I-34151 Trieste, Italy and 2 National Institute for Medical Research, MRC, The Ridgeway, London NW71AA, UK

* To whom correspondence should be addressed.


  Abstract

Motivation: We have previously demonstrated that proteins may be aligned not only by sequence or structural homology, but also using their dynamical properties. Dynamics-based alignments are sensitive and powerful tools to compare even structurally dissimilar protein families. Here, we propose to use this method to predict protein regions involved in the binding of nucleic acids. We have used the OB-fold, a motif known to promote protein–nucleic acid interactions, to validate our approach.

Results: We have tested the method using this well-characterized nucleic acid binding family. Protein regions consensually involved in statistically significant dynamics-based alignments were found to correlate with nucleic acid binding regions. The validated scheme was next used as a tool to predict which regions of the AXH-domain representatives (a sub-family of the OB-fold for which no DNA/RNA complex is yet available) are putatively involved in binding nucleic acids. The method, therefore, is a promising general approach for predicting functional regions in protein families on the basis of comparative large-scale dynamics.

Availability: The software is available upon request from the authors, free of charge for academic users.

Contact: michelet{at}sissa.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Thomas Lengauer

Received on February 13, 2009; revised on May 27, 2009; accepted on May 27, 2009

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