Literature DB >> 19746440

Computational approaches to identifying and characterizing protein binding sites for ligand design.

Stefan Henrich1, Outi M H Salo-Ahen, Bingding Huang, Friedrich F Rippmann, Gabriele Cruciani, Rebecca C Wade.   

Abstract

Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The "druggability" of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations. 2009 John Wiley & Sons, Ltd.

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Year:  2010        PMID: 19746440     DOI: 10.1002/jmr.984

Source DB:  PubMed          Journal:  J Mol Recognit        ISSN: 0952-3499            Impact factor:   2.137


  53 in total

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3.  POVME 3.0: Software for Mapping Binding Pocket Flexibility.

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5.  Pharmacological chaperones in the age of proteomic pathology.

Authors:  Scott A Small
Journal:  Proc Natl Acad Sci U S A       Date:  2014-08-13       Impact factor: 11.205

6.  A spatiotemporal characterization of the effect of p53 phosphorylation on its interaction with MDM2.

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7.  AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms.

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Review 8.  Expanding the number of 'druggable' targets: non-enzymes and protein-protein interactions.

Authors:  Leah N Makley; Jason E Gestwicki
Journal:  Chem Biol Drug Des       Date:  2013-01       Impact factor: 2.817

9.  Mapping the druggable allosteric space of G-protein coupled receptors: a fragment-based molecular dynamics approach.

Authors:  Anthony Ivetac; J Andrew McCammon
Journal:  Chem Biol Drug Des       Date:  2010-07-05       Impact factor: 2.817

10.  Yeast biological networks unfold the interplay of antioxidants, genome and phenotype, and reveal a novel regulator of the oxidative stress response.

Authors:  Jose M Otero; Manos A Papadakis; D B R K Gupta Udatha; Jens Nielsen; Gianni Panagiotou
Journal:  PLoS One       Date:  2010-10-25       Impact factor: 3.240

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