Literature DB >> 17900106

Binding response: a descriptor for selecting ligand binding site on protein surfaces.

Shijun Zhong1, Alexander D MacKerell.   

Abstract

The identification of ligand binding sites on a protein is an essential step in the selection of inhibitors of protein-ligand or protein-protein interactions via virtual database screening. To facilitate binding site identification, a novel descriptor, the binding response, is proposed in the present paper to quantitatively evaluate putative binding sites on the basis of their response to a test set of probe compounds. The binding response is determined on the basis of contributions from both the ligand-protein interaction energy and the geometry of binding poses for a database of test ligands. A favorable binding response is obtained for binding sites with favorable ligand binding energies and with ligand geometries within the putative site for the majority of compounds in the test set. The utility of this descriptor is illustrated by applying it to a number of known protein-ligand complexes, showing the approach to identify the experimental binding sites as the highest scoring site in 26 out of 29 cases; in the remaining three cases, it was among the top three scoring sites. This method is combined with sphere-based site identification and clustering methods to yield an automated approach for the identification of binding sites on proteins suitable for database screen or de novo drug design.

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Year:  2007        PMID: 17900106     DOI: 10.1021/ci700149k

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  15 in total

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Journal:  Protein Sci       Date:  2011-01       Impact factor: 6.725

2.  Balancing target flexibility and target denaturation in computational fragment-based inhibitor discovery.

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Journal:  J Comput Chem       Date:  2012-05-28       Impact factor: 3.376

3.  Computer-Aided Drug Design Methods.

Authors:  Wenbo Yu; Alexander D MacKerell
Journal:  Methods Mol Biol       Date:  2017

4.  Protein pockets: inventory, shape, and comparison.

Authors:  Ryan G Coleman; Kim A Sharp
Journal:  J Chem Inf Model       Date:  2010-04-26       Impact factor: 4.956

5.  Novel Noncatalytic Substrate-Selective p38α-Specific MAPK Inhibitors with Endothelial-Stabilizing and Anti-Inflammatory Activity.

Authors:  Nirav G Shah; Mohan E Tulapurkar; Aparna Ramarathnam; Amanda Brophy; Ramon Martinez; Kellie Hom; Theresa Hodges; Ramin Samadani; Ishwar S Singh; Alexander D MacKerell; Paul Shapiro; Jeffrey D Hasday
Journal:  J Immunol       Date:  2017-03-15       Impact factor: 5.422

Review 6.  Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures.

Authors:  Dario Ghersi; Roberto Sanchez
Journal:  J Struct Funct Genomics       Date:  2011-05-03

Review 7.  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

8.  The Small Molecule IMR-1 Inhibits the Notch Transcriptional Activation Complex to Suppress Tumorigenesis.

Authors:  Luisana Astudillo; Thiago G Da Silva; Zhiqiang Wang; Xiaoqing Han; Ke Jin; Jeffrey VanWye; Xiaoxia Zhu; Kelly Weaver; Taiji Oashi; Pedro E M Lopes; Darren Orton; Leif R Neitzel; Ethan Lee; Ralf Landgraf; David J Robbins; Alexander D MacKerell; Anthony J Capobianco
Journal:  Cancer Res       Date:  2016-04-13       Impact factor: 12.701

9.  Molecular modeling studies demonstrate key mutations that could affect the ligand recognition by influenza AH1N1 neuraminidase.

Authors:  Gema L Ramírez-Salinas; J García-Machorro; Miguel Quiliano; Mirko Zimic; Verónica Briz; Saul Rojas-Hernández; J Correa-Basurto
Journal:  J Mol Model       Date:  2015-10-26       Impact factor: 1.810

10.  Fpocket: an open source platform for ligand pocket detection.

Authors:  Vincent Le Guilloux; Peter Schmidtke; Pierre Tuffery
Journal:  BMC Bioinformatics       Date:  2009-06-02       Impact factor: 3.169

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