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Bioinformatics 2005 21(Suppl 1):i328-i337; doi:10.1093/bioinformatics/bti1023
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

In silico identification of functional regions in proteins

Guy Nimrod 1, Fabian Glaser 2, David Steinberg 3, Nir Ben-Tal 1,* and Tal Pupko 4

1Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University Ramat Aviv 69978, Israel
2European Bioinformatics Institute, Wellcome Trust Genome Campus Cambridge CB10 1SD, UK
3Department of Statistics and Operations Research, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University Ramat Aviv 69978, Israel
4Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University Ramat Aviv 69978, Israel

*To whom correspondence should be addressed.

Motivation: In silico prediction of functional regions on protein surfaces, i.e. sites of interaction with DNA, ligands, substrates and other proteins, is of utmost importance in various applications in the emerging fields of proteomics and structural genomics. When a sufficient number of homologs is found, powerful prediction schemes can be based on the observation that evolutionarily conserved regions are often functionally important, typically, only the principal functionally important region of the protein is detected, while secondary functional regions with weaker conservation signals are overlooked. Moreover, it is challenging to unambiguously identify the boundaries of the functional regions.

Methods: We present a new methodology, called PatchFinder, that automatically identifies patches of conserved residues that are located in close proximity to each other on the protein surface. PatchFinder is based on the following steps: (1) Assignment of conservation scores to each amino acid position on the protein surface. (2) Assignment of a score to each putative patch, based on its likelihood to be functionally important. The patch of maximum likelihood is considered to be the main functionally important region, and the search is continued for non-overlapping patches of secondary importance.

Results: We examined the accuracy of the method using the IGPS enzyme, the SH2 domain and a benchmark set of 112 proteins. These examples demonstrated that PatchFinder is capable of identifying both the main and secondary functional patches.

Availability: The PatchFinder program is available at: http://ashtoret.tau.ac.il/~nimrodg/

Contact: NirB{at}tauex.tau.ac.il

Received on January 15, 2005; accepted on March 27, 2005

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