Literature DB >> 19927322

On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins.

Abdullah Kahraman1, Richard J Morris, Roman A Laskowski, Angelo D Favia, Janet M Thornton.   

Abstract

Most function prediction methods that identify cognate ligands from binding site analyses work on the assumption of molecular complementarity. These approaches build on the conjectured complementarity of geometrical and physicochemical properties between ligands and binding sites so that similar binding sites will bind similar ligands. We found that this assumption does not generally hold for protein-ligand interactions and observed that it is not the chemical composition of ligand molecules that dictates the complementarity between protein and ligand molecules, but that the ligand's share within the functional mechanism of a protein determines the degree of complementarity. Here, we present for a set of cognate ligands a descriptive analysis and comparison of the physicochemical properties that each ligand experiences in various nonhomologous binding pockets. The comparisons in each ligand set reveal large variations in their experienced physicochemical properties, suggesting that the same ligand can bind to distinct physicochemical environments. In some protein ligand complexes, the variation was found to correlate with the electrochemical characteristic of ligand molecules, whereas in others it was disclosed as a prerequisite for the biochemical function of the protein. To achieve binding, proteins were observed to engage in subtle balancing acts between electrostatic and hydrophobic interactions to generate stabilizing free energies of binding. For the presented analysis, a new method for scoring hydrophobicity from molecular environments was developed showing high correlations with experimental determined desolvation energies. The presented results highlight the complexities of molecular recognition and underline the challenges of computational structural biology in developing methods to detect these important subtleties.

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Year:  2010        PMID: 19927322     DOI: 10.1002/prot.22633

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


  28 in total

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

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

2.  Self-complementarity within proteins: bridging the gap between binding and folding.

Authors:  Sankar Basu; Dhananjay Bhattacharyya; Rahul Banerjee
Journal:  Biophys J       Date:  2012-06-05       Impact factor: 4.033

3.  The Recognition of Identical Ligands by Unrelated Proteins.

Authors:  Sarah Barelier; Teague Sterling; Matthew J O'Meara; Brian K Shoichet
Journal:  ACS Chem Biol       Date:  2015-10-12       Impact factor: 5.100

4.  Structure- and sequence-based function prediction for non-homologous proteins.

Authors:  Lee Sael; Meghana Chitale; Daisuke Kihara
Journal:  J Struct Funct Genomics       Date:  2012-01-22

5.  Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.

Authors:  Lee Sael; Daisuke Kihara
Journal:  Proteins       Date:  2012-01-24

6.  Ligand binding site similarity identification based on chemical and geometric similarity.

Authors:  Haibo Tu; Tieliu Shi
Journal:  Protein J       Date:  2013-06       Impact factor: 2.371

7.  Prediction and experimental validation of enzyme substrate specificity in protein structures.

Authors:  Shivas R Amin; Serkan Erdin; R Matthew Ward; Rhonald C Lua; Olivier Lichtarge
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-21       Impact factor: 11.205

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

Authors:  Matthias Wirth; Andrea Volkamer; Vincent Zoete; Friedrich Rippmann; Olivier Michielin; Matthias Rarey; Wolfgang H B Sauer
Journal:  J Comput Aided Mol Des       Date:  2013-06-27       Impact factor: 3.686

9.  A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs).

Authors:  Christiane Ehrt; Tobias Brinkjost; Oliver Koch
Journal:  PLoS Comput Biol       Date:  2018-11-08       Impact factor: 4.475

10.  Charge density distributions derived from smoothed electrostatic potential functions: design of protein reduced point charge models.

Authors:  Laurence Leherte; Daniel P Vercauteren
Journal:  J Comput Aided Mol Des       Date:  2011-09-14       Impact factor: 3.686

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