Journal article
LFM-Pro: a tool for detecting significant local structural sites in proteins
Bioinformatics (Oxford, England), v 23(6), pp 709-716
15 Mar 2007
PMID: 17237050
Featured in Collection : UN Sustainable Development Goals @ Drexel
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@ceng.metu.edu.tr, ozturk@cse.ohiostate.edu,hakan@cse.ohiostate.edu,yusu@cse.ohiostate.edu
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Details
- Title
- LFM-Pro: a tool for detecting significant local structural sites in proteins
- Creators
- Ahmet Sacan - 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, USAOzgur Ozturk - 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, USAHakan Ferhatosmanoglu - 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, USAYusu Wang - 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
- Publication Details
- Bioinformatics (Oxford, England), v 23(6), pp 709-716
- Publisher
- Oxford University Press
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000245511800008
- Scopus ID
- 2-s2.0-34147112346
- Other Identifier
- 991014878248704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability