Literature DB >> 16522669

Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interfaces.

Nicholas J Burgoyne1, Richard M Jackson.   

Abstract

MOTIVATION: Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding 'hot-spots', and rank them according to sequence conservation and simple measures of physical properties including hydrophobicity, desolvation, electrostatic and van der Waals potentials, to predict which are involved in binding in the native complex.
RESULTS: The resulting differences between predicting binding-sites at protein-protein and protein-ligand interfaces are striking. There is a high level of prediction accuracy (< or =93%) for protein-ligand interactions, based on the following attributes: van der Waals potential, electrostatic potential, desolvation and surface conservation. Generally, the prediction accuracy for protein-protein interactions is lower, with the exception of enzymes. Our results show that the ease of cleft desolvation is strongly predictive of interfaces and strongly maintained across all classes of protein-binding interface.

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Year:  2006        PMID: 16522669     DOI: 10.1093/bioinformatics/btl079

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  53 in total

1.  Prediction of protein-protein interaction sites using electrostatic desolvation profiles.

Authors:  Sébastien Fiorucci; Martin Zacharias
Journal:  Biophys J       Date:  2010-05-19       Impact factor: 4.033

2.  Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

Authors:  Guang-Hui Liu; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-11-12       Impact factor: 1.843

3.  Analysis of the structural and molecular basis of voltage-sensitive sodium channel inhibition by the spider toxin huwentoxin-IV (μ-TRTX-Hh2a).

Authors:  Natali A Minassian; Alan Gibbs; Amy Y Shih; Yi Liu; Robert A Neff; Steven W Sutton; Tara Mirzadegan; Judith Connor; Ross Fellows; Matthew Husovsky; Serena Nelson; Michael J Hunter; Mack Flinspach; Alan D Wickenden
Journal:  J Biol Chem       Date:  2013-06-12       Impact factor: 5.157

4.  F99 is critical for dimerization and activation of South African HIV-1 subtype C protease.

Authors:  Previn Naicker; Palesa Seele; Heini W Dirr; Yasien Sayed
Journal:  Protein J       Date:  2013-10       Impact factor: 2.371

5.  Using protein-ligand docking to assess the chemical tractability of inhibiting a protein target.

Authors:  Richard A Ward
Journal:  J Mol Model       Date:  2010-03-11       Impact factor: 1.810

6.  Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

Authors:  John A Capra; Roman A Laskowski; Janet M Thornton; Mona Singh; Thomas A Funkhouser
Journal:  PLoS Comput Biol       Date:  2009-12-04       Impact factor: 4.475

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

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

8.  Predicting protein-protein binding sites in membrane proteins.

Authors:  Andrew J Bordner
Journal:  BMC Bioinformatics       Date:  2009-09-24       Impact factor: 3.169

9.  Common physical basis of macromolecule-binding sites in proteins.

Authors:  Yao Chi Chen; Carmay Lim
Journal:  Nucleic Acids Res       Date:  2008-11-06       Impact factor: 16.971

10.  SitesIdentify: a protein functional site prediction tool.

Authors:  Tracey Bray; Pedro Chan; Salim Bougouffa; Richard Greaves; Andrew J Doig; Jim Warwicker
Journal:  BMC Bioinformatics       Date:  2009-11-18       Impact factor: 3.169

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