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Bioinformatics Advance Access published online on January 19, 2007
Bioinformatics, 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

Ahmet Sacan a,*,{dagger}, Ozgur Ozturk b, Hakan Ferhatosmanoglu b,c and Yusu Wang b

aDepartment of Computer Engineering, Middle East Technical University, Ankara, Turkey,
bComputer Science and Engineering Department, The Ohio State University, Columbus, OH
cBiomedical Informatics Department, The Ohio State University, Columbus, OH

*To whom correspondence should be addressed. Ahmet Sacan, E-mail: ahmet{at}ceng.metu.edu.tr, fozturk{at}cse.ohiostate.edu, hakan{at}cse.ohiostate.edu, yusug{at}cse.ohiostate.edu


  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 LFM-Pro (Local Feature Mining in Proteins) 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

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

Associate Editor: Martin Bishop

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

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A. Sacan, I. H. Toroslu, and H. Ferhatosmanoglu
Integrated search and alignment of protein structures
Bioinformatics, December 15, 2008; 24(24): 2872 - 2879.
[Abstract] [Full Text] [PDF]


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