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Bioinformatics Advance Access originally published online on March 16, 2006
Bioinformatics 2006 22(11):1343-1352; doi:10.1093/bioinformatics/btl098
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

A permissive secondary structure-guided superposition tool for clustering of protein fragments toward protein structure prediction via fragment assembly

Gilad Wainreb 1, Nurit Haspel 2, Haim J. Wolfson 2 and Ruth Nussinov 1,3,*

1 Sackler Institute of Molecular Medicine, Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University Tel Aviv 69978, Israel
2 School of Computer Science, Faculty of Exact Sciences, Tel Aviv University Tel Aviv 69978, Israel
3 Basic Research Program, SAIC-Frederick, Inc., Laboratory of Experimental and Computational Biology NCI-Frederick, Building 469, Rm 151, Frederick, MD 21702, USA

*To whom correspondence should be addressed.

Motivation: Secondary-Structure Guided Superposition tool (SSGS) is a permissive secondary structure-based algorithm for matching of protein structures and in particular their fragments. The algorithm was developed towards protein structure prediction via fragment assembly.

Results: In a fragment-based structural prediction scheme, a protein sequence is cut into building blocks (BBs). The BBs are assembled to predict their relative 3D arrangement. Finally, the assemblies are refined. To implement this prediction scheme, a clustered structural library representing sequence patterns for protein fragments is essential. To create a library, BBs generated by cutting proteins from the PDB are compared and structurally similar BBs are clustered. To allow structural comparison and clustering of the BBs, which are often relatively short with flexible loops, we have devised SSGS. SSGS maintains high similarity between cluster members and is highly efficient. When it comes to comparing BBs for clustering purposes, the algorithm obtains better results than other, non-secondary structure guided protein superimposition algorithms.

Availability: SSGS is available for download at http://www.cs.tau.ac.il/~wainreb

Contact: ruthn{at}ncifcrf.gov

Supplementary information: Supplementary data are available at Bioinformatics online.

Received on July 31, 2005; revised on February 16, 2006; accepted on March 11, 2006

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