Literature DB >> 17951830

Effective labeling of molecular surface points for cavity detection and location of putative binding sites.

Mary Ellen Bock1, Claudio Garutti, Conettina Guerra.   

Abstract

We present a method for detecting and comparing cavities on protein surfaces that is useful for protein binding site recognition. The method is based on a representation of the protein structures by a collection of spin-images and their associated spin-image profiles. Results of the cavity detection procedure are presented for a large set of non-redundant proteins and compared with SURFNET-ConSurf. Our comparison method is used to find a surface region in one cavity of a protein that is geometrically similar to a surface region in the cavity of another protein. Such a finding would be an indication that the two regions likely bind to the same ligand. Our overall approach for cavity detection and comparison is benchmarked on several pairs of known complexes, obtaining a good coverage of the atoms of the binding sites.

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Year:  2007        PMID: 17951830

Source DB:  PubMed          Journal:  Comput Syst Bioinformatics Conf        ISSN: 1752-7791


  4 in total

1.  Protein pocket detection via convex hull surface evolution and associated Reeb graph.

Authors:  Rundong Zhao; Zixuan Cang; Yiying Tong; Guo-Wei Wei
Journal:  Bioinformatics       Date:  2018-09-01       Impact factor: 6.937

2.  A global optimization algorithm for protein surface alignment.

Authors:  Paola Bertolazzi; Concettina Guerra; Giampaolo Liuzzi
Journal:  BMC Bioinformatics       Date:  2010-09-29       Impact factor: 3.169

3.  Fpocket: an open source platform for ligand pocket detection.

Authors:  Vincent Le Guilloux; Peter Schmidtke; Pierre Tuffery
Journal:  BMC Bioinformatics       Date:  2009-06-02       Impact factor: 3.169

4.  MolLoc: a web tool for the local structural alignment of molecular surfaces.

Authors:  Stefano Angaran; Mary Ellen Bock; Claudio Garutti; Concettina Guerra
Journal:  Nucleic Acids Res       Date:  2009-05-22       Impact factor: 16.971

  4 in total

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