A novel and efficient tool for locating and characterizing protein cavities and binding sites Export

@article{citeulike:5984933, abstract = {Systematic investigation of a protein and its binding site characteristics are crucial for designing small molecules that modulate protein functions. However, fundamental uncertainties in binding site interactions and insufficient knowledge of the properties of even well-defined binding pockets can make it difficult to design optimal drugs. Herein, we report the development and implementation of a cavity detection algorithm built with HINT toolkit functions that we are naming Vectorial Identification of Cavity Extents (VICE). This very efficient algorithm is based on geometric criteria applied to simple integer grid maps. In testing, we carried out a systematic investigation on a very diverse data set of proteins and protein-protein/protein-polynucleotide complexes for locating and characterizing the indentations, cavities, pockets, grooves, channels, and surface regions. Additionally, we evaluated a curated data set of unbound proteins for which a ligand-bound protein structures are also known; here the VICE algorithm located the actual ligand in the largest cavity in 83\% of the cases and in one of the three largest in 90\% of the cases. An interactive front-end provides a quick and simple procedure for locating, displaying and manipulating cavities in these structures. Information describing the cavity, including its volume and surface area metrics, and lists of atoms, residues, and/or chains lining the binding pocket, can be easily obtained and analyzed. For example, the relative cross-sectional surface area (to total surface area) of cavity openings in well-enclosed cavities is 0.06 ± 0.04 and in surface clefts or crevices is 0.25 ± 0.09. Proteins 2010. {\copyright} 2009 Wiley-Liss, Inc.}, address = {Department of Medicinal Chemistry and Institute for Structural Biology and Drug Discovery, Virginia Commonwealth University, Richmond, Virginia 23298-0540}, author = {Tripathi, Ashutosh and Kellogg, Glen E.}, citeulike-article-id = {5984933}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/prot.22608}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19847777}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19847777}, citeulike-linkout-3 = {http://www3.interscience.wiley.com/cgi-bin/abstract/122593994/ABSTRACT}, day = {11}, doi = {10.1002/prot.22608}, issn = {1097-0134}, journal = {Proteins: Structure, Function, and Bioinformatics}, keywords = {binding\_site, identification, nov09}, month = {September}, number = {9999}, pages = {NA+}, posted-at = {2009-11-03 20:50:02}, priority = {0}, title = {A novel and efficient tool for locating and characterizing protein cavities and binding sites}, url = {http://dx.doi.org/10.1002/prot.22608}, volume = {9999}, year = {2009} }

TY - JOUR ID - citeulike:5984933 L3 - citeulike-article-id:5984933 N2 - Systematic investigation of a protein and its binding site characteristics are crucial for designing small molecules that modulate protein functions. However, fundamental uncertainties in binding site interactions and insufficient knowledge of the properties of even well-defined binding pockets can make it difficult to design optimal drugs. Herein, we report the development and implementation of a cavity detection algorithm built with HINT toolkit functions that we are naming Vectorial Identification of Cavity Extents (VICE). This very efficient algorithm is based on geometric criteria applied to simple integer grid maps. In testing, we carried out a systematic investigation on a very diverse data set of proteins and protein-protein/protein-polynucleotide complexes for locating and characterizing the indentations, cavities, pockets, grooves, channels, and surface regions. Additionally, we evaluated a curated data set of unbound proteins for which a ligand-bound protein structures are also known; here the VICE algorithm located the actual ligand in the largest cavity in 83% of the cases and in one of the three largest in 90% of the cases. An interactive front-end provides a quick and simple procedure for locating, displaying and manipulating cavities in these structures. Information describing the cavity, including its volume and surface area metrics, and lists of atoms, residues, and/or chains lining the binding pocket, can be easily obtained and analyzed. For example, the relative cross-sectional surface area (to total surface area) of cavity openings in well-enclosed cavities is 0.06 ± 0.04 and in surface clefts or crevices is 0.25 ± 0.09. Proteins 2010. © 2009 Wiley-Liss, Inc. IS - 9999 JF - Proteins: Structure, Function, and Bioinformatics SN - 1097-0134 AD - Department of Medicinal Chemistry and Institute for Structural Biology and Drug Discovery, Virginia Commonwealth University, Richmond, Virginia 23298-0540 TI - A novel and efficient tool for locating and characterizing protein cavities and binding sites VL - 9999 SP - NA KW - binding_site KW - identification KW - nov09 AU - Tripathi, Ashutosh AU - Kellogg, Glen PY - 2009/09/11/ UR - http://dx.doi.org/10.1002/prot.22608 ER -