Literature DB >> 1476996

POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids.

D G Levitt1, L J Banaszak.   

Abstract

A new interactive graphics program is described that provides a quick and simple procedure for identifying, displaying, and manipulating the indentations, cavities, or holes in a known protein structure. These regions are defined as, e.g., the xo, yo, zo values at which a test sphere of radius r can be placed without touching the centers of any protein atoms, subject to the condition that there is some x < xo and some x > xo where the sphere does touch the protein atoms. The surfaces of these pockets are modeled using a modification of the marching cubes algorithm. This modification provides identification of each closed surface so that by "clicking" on any line of the surface, the entire surface can be selected. The surface can be displayed either as a line grid or as a solid surface. After the desired "pocket" has been selected, the amino acid residues and atoms that surround this pocket can be selected and displayed. The protein database that is input can have more than one protein "segment," allowing identification of the pockets at the interface between proteins. The use of the program is illustrated with several specific examples. The program is written in C and requires Silicon Graphics graphics routines.

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Year:  1992        PMID: 1476996     DOI: 10.1016/0263-7855(92)80074-n

Source DB:  PubMed          Journal:  J Mol Graph        ISSN: 0263-7855


  84 in total

1.  Fast prediction and visualization of protein binding pockets with PASS.

Authors:  G P Brady; P F Stouten
Journal:  J Comput Aided Mol Des       Date:  2000-05       Impact factor: 3.686

2.  Real-time ligand binding pocket database search using local surface descriptors.

Authors:  Rayan Chikhi; Lee Sael; Daisuke Kihara
Journal:  Proteins       Date:  2010-07

3.  Determination of the interfacial water content in protein-protein complexes from free energy simulations.

Authors:  Peter Monecke; Thorsten Borosch; Jürgen Brickmann; Stefan M Kast
Journal:  Biophys J       Date:  2005-11-11       Impact factor: 4.033

4.  Comparative docking studies of CYP1b1 and its PCG-associated mutant forms.

Authors:  Malkaram Sridhar Achary; Hampapathalu Adimurthy Nagarajam
Journal:  J Biosci       Date:  2008-12       Impact factor: 1.826

5.  Automated identification of binding sites for phosphorylated ligands in protein structures.

Authors:  Dario Ghersi; Roberto Sanchez
Journal:  Proteins       Date:  2012-07-07

Review 6.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

7.  eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands.

Authors:  Michal Brylinski; Wei P Feinstein
Journal:  J Comput Aided Mol Des       Date:  2013-07-10       Impact factor: 3.686

8.  Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble.

Authors:  Edon Sung; Sangsoo Kim; Whanchul Shin
Journal:  BMC Bioinformatics       Date:  2010-05-18       Impact factor: 3.169

9.  McVol - a program for calculating protein volumes and identifying cavities by a Monte Carlo algorithm.

Authors:  Mirco S Till; G Matthias Ullmann
Journal:  J Mol Model       Date:  2009-07-22       Impact factor: 1.810

10.  On the role of physics and evolution in dictating protein structure and function.

Authors:  Jeffrey Skolnick; Mu Gao; Hongyi Zhou
Journal:  Isr J Chem       Date:  2014-08-01       Impact factor: 3.333

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