title: LFM-Pro: a tool for detecting significant local structural sites in proteins.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro.
authors.affiliation: Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. ahmet@ceng.metu.edu.tr
authors.forename: Ahmet
authors.initials: A
authors.lastname: Sacan
authors.forename: Ozgur
authors.initials: O
authors.lastname: Ozturk
authors.forename: Hakan
authors.initials: H
authors.lastname: Ferhatosmanoglu
authors.forename: Yusu
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000648:ultrastructure*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins