Literature DB >> 23807262

Protein pocket and ligand shape comparison and its application in virtual screening.

Matthias Wirth1, Andrea Volkamer, Vincent Zoete, Friedrich Rippmann, Olivier Michielin, Matthias Rarey, Wolfgang H B Sauer.   

Abstract

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

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Year:  2013        PMID: 23807262     DOI: 10.1007/s10822-013-9659-1

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  36 in total

1.  The Protein Data Bank.

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Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Virtual screening using protein-ligand docking: avoiding artificial enrichment.

Authors:  Marcel L Verdonk; Valerio Berdini; Michael J Hartshorn; Wijnand T M Mooij; Christopher W Murray; Richard D Taylor; Paul Watson
Journal:  J Chem Inf Comput Sci       Date:  2004 May-Jun

3.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

Authors:  Richard A Friesner; Jay L Banks; Robert B Murphy; Thomas A Halgren; Jasna J Klicic; Daniel T Mainz; Matthew P Repasky; Eric H Knoll; Mee Shelley; Jason K Perry; David E Shaw; Perry Francis; Peter S Shenkin
Journal:  J Med Chem       Date:  2004-03-25       Impact factor: 7.446

4.  Analyzing the topology of active sites: on the prediction of pockets and subpockets.

Authors:  Andrea Volkamer; Axel Griewel; Thomas Grombacher; Matthias Rarey
Journal:  J Chem Inf Model       Date:  2010-10-14       Impact factor: 4.956

Review 5.  Topography-biased compound library design: the shape of things to come?

Authors:  Irini Akritopoulou-Zanze; James T Metz; Stevan W Djuric
Journal:  Drug Discov Today       Date:  2007-10-17       Impact factor: 7.851

6.  Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design.

Authors:  J Liang; H Edelsbrunner; C Woodward
Journal:  Protein Sci       Date:  1998-09       Impact factor: 6.725

7.  sc-PDB: a database for identifying variations and multiplicity of 'druggable' binding sites in proteins.

Authors:  Jamel Meslamani; Didier Rognan; Esther Kellenberger
Journal:  Bioinformatics       Date:  2011-03-12       Impact factor: 6.937

8.  Bioactive Molecules: Perfectly Shaped for Their Target?

Authors:  Matthias Wirth; Wolfgang H B Sauer
Journal:  Mol Inform       Date:  2011-07-12       Impact factor: 3.353

9.  Molecular shape and medicinal chemistry: a perspective.

Authors:  Anthony Nicholls; Georgia B McGaughey; Robert P Sheridan; Andrew C Good; Gregory Warren; Magali Mathieu; Steven W Muchmore; Scott P Brown; J Andrew Grant; James A Haigh; Neysa Nevins; Ajay N Jain; Brian Kelley
Journal:  J Med Chem       Date:  2010-05-27       Impact factor: 7.446

10.  Cavities and atomic packing in protein structures and interfaces.

Authors:  Shrihari Sonavane; Pinak Chakrabarti
Journal:  PLoS Comput Biol       Date:  2008-09-26       Impact factor: 4.475

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  7 in total

1.  Considerations of Protein Subpockets in Fragment-Based Drug Design.

Authors:  Matthew Bartolowits; V Jo Davisson
Journal:  Chem Biol Drug Des       Date:  2015-08-31       Impact factor: 2.817

2.  CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.

Authors:  Jean-Rémy Marchand; Bernard Pirard; Peter Ertl; Finton Sirockin
Journal:  J Comput Aided Mol Des       Date:  2021-05-29       Impact factor: 3.686

3.  Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints.

Authors:  Mahendra Awale; Xian Jin; Jean-Louis Reymond
Journal:  J Cheminform       Date:  2015-02-10       Impact factor: 5.514

Review 4.  Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology.

Authors:  Bruno O Villoutreix; Melaine A Kuenemann; Jean-Luc Poyet; Heriberto Bruzzoni-Giovanelli; Céline Labbé; David Lagorce; Olivier Sperandio; Maria A Miteva
Journal:  Mol Inform       Date:  2014-06-02       Impact factor: 3.353

5.  POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics.

Authors:  Jacob D Durrant; Lane Votapka; Jesper Sørensen; Rommie E Amaro
Journal:  J Chem Theory Comput       Date:  2014-09-29       Impact factor: 6.006

6.  On the origins of three-dimensionality in drug-like molecules.

Authors:  Joshua Meyers; Michael Carter; N Yi Mok; Nathan Brown
Journal:  Future Med Chem       Date:  2016-08-30       Impact factor: 3.808

7.  PDB-Explorer: a web-based interactive map of the protein data bank in shape space.

Authors:  Xian Jin; Mahendra Awale; Michaël Zasso; Daniel Kostro; Luc Patiny; Jean-Louis Reymond
Journal:  BMC Bioinformatics       Date:  2015-10-23       Impact factor: 3.169

  7 in total

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