Subsequence-based feature map for protein function classification
Computational Biology and Chemistry
Extraction of Binding Sites in Proteins by Searching for Similar Local Molecular Surfaces
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Protein Structure Classification Based on Conserved Hydrophobic Residues
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Exploring uses of persistent homology for statistical analysis of landmark-based shape data
Journal of Multivariate Analysis
Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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