Literature DB >> 21398668

sc-PDB: a database for identifying variations and multiplicity of 'druggable' binding sites in proteins.

Jamel Meslamani1, Didier Rognan, Esther Kellenberger.   

Abstract

BACKGROUND: The sc-PDB database is an annotated archive of druggable binding sites extracted from the Protein Data Bank. It contains all-atoms coordinates for 8166 protein-ligand complexes, chosen for their geometrical and physico-chemical properties. The sc-PDB provides a functional annotation for proteins, a chemical description for ligands and the detailed intermolecular interactions for complexes. The sc-PDB now includes a hierarchical classification of all the binding sites within a functional class.
METHOD: The sc-PDB entries were first clustered according to the protein name indifferent of the species. For each cluster, we identified dissimilar sites (e.g. catalytic and allosteric sites of an enzyme). SCOPE AND APPLICATIONS: The classification of sc-PDB targets by binding site diversity was intended to facilitate chemogenomics approaches to drug design. In ligand-based approaches, it avoids comparing ligands that do not share the same binding site. In structure-based approaches, it permits to quantitatively evaluate the diversity of the binding site definition (variations in size, sequence and/or structure). AVAILABILITY: The sc-PDB database is freely available at: http://bioinfo-pharma.u-strasbg.fr/scPDB.

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Year:  2011        PMID: 21398668     DOI: 10.1093/bioinformatics/btr120

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


  30 in total

1.  Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets.

Authors:  Elizabeth A Proctor; Shuangye Yin; Alexander Tropsha; Nikolay V Dokholyan
Journal:  Biophys J       Date:  2012-01-03       Impact factor: 4.033

2.  PDID: database of molecular-level putative protein-drug interactions in the structural human proteome.

Authors:  Chen Wang; Gang Hu; Kui Wang; Michal Brylinski; Lei Xie; Lukasz Kurgan
Journal:  Bioinformatics       Date:  2015-10-26       Impact factor: 6.937

3.  Comparison of ultra-fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology.

Authors:  Alvaro Cortés-Cabrera; Garrett M Morris; Paul W Finn; Antonio Morreale; Federico Gago
Journal:  Br J Pharmacol       Date:  2013-10       Impact factor: 8.739

4.  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

5.  Binding site matching in rational drug design: algorithms and applications.

Authors:  Misagh Naderi; Jeffrey Mitchell Lemoine; Rajiv Gandhi Govindaraj; Omar Zade Kana; Wei Pan Feinstein; Michal Brylinski
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

Review 6.  Pocket-based drug design: exploring pocket space.

Authors:  Xiliang Zheng; Linfeng Gan; Erkang Wang; Jin Wang
Journal:  AAPS J       Date:  2012-11-22       Impact factor: 4.009

7.  Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries.

Authors:  David Xu; Samy O Meroueh
Journal:  J Chem Inf Model       Date:  2016-05-24       Impact factor: 4.956

Review 8.  Bioinformatics and variability in drug response: a protein structural perspective.

Authors:  Jennifer L Lahti; Grace W Tang; Emidio Capriotti; Tianyun Liu; Russ B Altman
Journal:  J R Soc Interface       Date:  2012-05-02       Impact factor: 4.118

9.  Identification and characterization of PKF118-310 as a KDM4A inhibitor.

Authors:  Gianluigi Franci; Federica Sarno; Angela Nebbioso; Lucia Altucci
Journal:  Epigenetics       Date:  2016-10-21       Impact factor: 4.528

10.  RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions.

Authors:  Naiem T Issa; Oakland J Peters; Stephen W Byers; Sivanesan Dakshanamurthy
Journal:  Comb Chem High Throughput Screen       Date:  2015       Impact factor: 1.339

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