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Bioinformatics Advance Access published online on March 16, 2006
Bioinformatics, 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
Received July 31, 2005
Revised February 16, 2006
Accepted March 11, 2006

Article

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 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 Sackler Institute of Molecular Medicine, Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; Basic Research Program, SAIC-Frederick, Inc., Laboratory of Experimental and Computational Biology, NCI-Frederick, Bldg 469, Rm 151, Frederick, MD 21702

* To whom correspondence should be addressed.
Ruth Nussinov, E-mail: ruthn{at}ncifcrf.gov


  Abstract

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

Associate Editor: Martin Bishop
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