LFM-Pro: a tool for detecting significant local structural sites in proteins. 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

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References in zbMATH (referenced in 4 articles )

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  1. Gamble, Jennifer; Heo, Giseon: Exploring uses of persistent homology for statistical analysis of landmark-based shape data (2010)
  2. Regad, Leslie; Martin, Juliette; Nuel, Grégory; Camproux, Anne-Claude: Mining protein loops using a structural alphabet and statistical exceptionality (2010) ioport
  3. Sarac, Omer Sinan; Gürsoy-Yüzügüllü, Özge; Cetin-Atalay, Rengul; Atalay, Volkan: Subsequence-based feature map for protein function classification (2008)
  4. Sacan, Ahmet; Ozturk, Ozgur; Ferhatosmanoglu, Hakan; Wang, Yusu: Lfm-pro: A tool for detecting significant local structural sites in proteins (2007) ioport