Literature DB >> 16304646

A method for localizing ligand binding pockets in protein structures.

Fabian Glaser1, Richard J Morris, Rafael J Najmanovich, Roman A Laskowski, Janet M Thornton.   

Abstract

The accurate identification of ligand binding sites in protein structures can be valuable in determining protein function. Once the binding site is known, it becomes easier to perform in silico and experimental procedures that may allow the ligand type and the protein function to be determined. For example, binding pocket shape analysis relies heavily on the correct localization of the ligand binding site. We have developed SURFNET-ConSurf, a modular, two-stage method for identifying the location and shape of potential ligand binding pockets in protein structures. In the first stage, the SURFNET program identifies clefts in the protein surface that are potential binding sites. In the second stage, these clefts are trimmed in size by cutting away regions distant from highly conserved residues, as defined by the ConSurf-HSSP database. The largest clefts that remain tend to be those where ligands bind. To test the approach, we analyzed a nonredundant set of 244 protein structures from the PDB and found that SURFNET-ConSurf identifies a ligand binding pocket in 75% of them. The trimming procedure reduces the original cleft volumes by 30% on average, while still encompassing an average 87% of the ligand volume. From the analysis of the results we conclude that for those cases in which the ligands are found in large, highly conserved clefts, the combined SURFNET-ConSurf method gives pockets that are a better match to the ligand shape and location. We also show that this approach works better for enzymes than for nonenzyme proteins. 2005 Wiley-Liss, Inc.

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Year:  2006        PMID: 16304646     DOI: 10.1002/prot.20769

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  63 in total

1.  Sequence and structure continuity of evolutionary importance improves protein functional site discovery and annotation.

Authors:  A D Wilkins; R Lua; S Erdin; R M Ward; O Lichtarge
Journal:  Protein Sci       Date:  2010-07       Impact factor: 6.725

2.  Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library.

Authors:  Yoshifumi Fukunishi; Haruki Nakamura
Journal:  Protein Sci       Date:  2011-01       Impact factor: 6.725

3.  Residue centrality, functionally important residues, and active site shape: analysis of enzyme and non-enzyme families.

Authors:  Antonio del Sol; Hirotomo Fujihashi; Dolors Amoros; Ruth Nussinov
Journal:  Protein Sci       Date:  2006-08-01       Impact factor: 6.725

4.  Functional insights from structural genomics.

Authors:  Farhad Forouhar; Alexandre Kuzin; Jayaraman Seetharaman; Insun Lee; Weihong Zhou; Mariam Abashidze; Yang Chen; Wei Yong; Haleema Janjua; Yingyi Fang; Dongyan Wang; Kellie Cunningham; Rong Xiao; Thomas B Acton; Eran Pichersky; Daniel F Klessig; Carl W Porter; Gaetano T Montelione; Liang Tong
Journal:  J Struct Funct Genomics       Date:  2007-06-23

5.  A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  2007-12-28       Impact factor: 11.205

Review 6.  FINDSITE: a combined evolution/structure-based approach to protein function prediction.

Authors:  Jeffrey Skolnick; Michal Brylinski
Journal:  Brief Bioinform       Date:  2009-03-26       Impact factor: 11.622

7.  Assessment of ligand binding site predictions in CASP10.

Authors:  Tiziano Gallo Cassarino; Lorenza Bordoli; Torsten Schwede
Journal:  Proteins       Date:  2014-02

8.  Prediction of ligand binding sites using homologous structures and conservation at CASP8.

Authors:  Mark N Wass; Michael J E Sternberg
Journal:  Proteins       Date:  2009

9.  Fragment-based identification of druggable 'hot spots' of proteins using Fourier domain correlation techniques.

Authors:  Ryan Brenke; Dima Kozakov; Gwo-Yu Chuang; Dmitri Beglov; David Hall; Melissa R Landon; Carla Mattos; Sandor Vajda
Journal:  Bioinformatics       Date:  2009-01-28       Impact factor: 6.937

10.  Predicting small ligand binding sites in proteins using backbone structure.

Authors:  Andrew J Bordner
Journal:  Bioinformatics       Date:  2008-10-21       Impact factor: 6.937

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