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. We have developed 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 bio-chemical 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. 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.

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