Literature DB >> 31125569

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing.

Richard D Smith1, Jordan J Clark1, Aqeel Ahmed1, Zachary J Orban1, James B Dunbar1, Heather A Carlson2.   

Abstract

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (Ka, Kd, Ki, IC50) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  binding affinities; binding-site similarity; drug discovery; ligand similarity; protein-ligand complexes

Mesh:

Substances:

Year:  2019        PMID: 31125569      PMCID: PMC6589129          DOI: 10.1016/j.jmb.2019.05.024

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  54 in total

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Journal:  Proteins       Date:  2005-01-01

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Authors:  Elke Michalsky; Mathias Dunkel; Andrean Goede; Robert Preissner
Journal:  BMC Bioinformatics       Date:  2005-05-19       Impact factor: 3.169

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  11 in total

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10.  Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK.

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