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Bioinformatics Advance Access originally published online on January 19, 2007
Bioinformatics 2007 23(6):709-716; doi:10.1093/bioinformatics/btl685
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

LFM-Pro: a tool for detecting significant local structural sites in proteins{ddagger}

Ahmet Sacan 1,*,{dagger}, Ozgur Ozturk 2, Hakan Ferhatosmanoglu 2,3 and Yusu Wang 2

1Department of Computer Engineering, Middle East Technical University, Ankara, Turkey, 2Computer Science and Engineering Department and 3Biomedical Informatics Department, The Ohio State University, Columbus, OH, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features.

Results: We propose Local Feature Mining in Proteins (LFM-Pro) as a framework for automatically discovering family-specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and biochemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on trypsin-like serine proteases family of proteins and on a challenging classification dataset via comparison with DALI. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features.

Availability: The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro

Contact: ahmet{at}ceng.metu.edu.tr, ozturk{at}cse.ohiostate.edu, hakan{at}cse.ohiostate.edu, yusu{at}cse.ohiostate.edu

Associate Editor: Martin Bishop

{ddagger} This work was supported by DOE DE-FG02-03ER25573, DOE DE-FG02-06ER25735, and NSF IIS-0546713 awards.

{dagger} This study was conducted while the author was a Visiting Scholar at the Database Lab of The Ohio State University.

Received on May 25, 2006; revised on December 25, 2006; accepted on January 8, 2007

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