Literature DB >> 22170280

In silico screening for agonists and blockers of the β(2) adrenergic receptor: implications of inactive and activated state structures.

Stefano Costanzi1, Santiago Vilar.   

Abstract

Ten crystal structures of the β(2) adrenergic receptor have been published, reflecting different signaling states. Here, through controlled-docking experiments, we examined the implications of using inactive or activated structures on the in silico screening for agonists and blockers of the receptor. Specifically, we targeted the crystal structures solved in complex with carazolol (2RH1), the neutral antagonist alprenalol, the irreversible agonist FAUC50 (3PDS), and the full agonist BI-167017 (3P0G). Our results indicate that activated structures favor agonists over blockers, whereas inactive structures favor blockers over agonists. This tendency is more marked for activated than for inactive structures. Additionally, agonists tend to receive more favorable docking scores when docked at activated rather than inactive structures, while blockers do the opposite. Hence, the difference between the docking scores attained with an activated and an inactive structure is an excellent means for the classification of ligands into agonists and blockers as we determined through receiver operating characteristic curves and linear discriminant analysis. With respect to virtual screening, all structures prioritized well agonists and blockers over nonbinders. However, inactive structures worked better for blockers and activated structures worked better for agonists, respectively. Notably, the combination of individual docking experiments through receptor ensemble docking resulted in an excellent performance in the retrieval of both agonists and blockers. Finally, we demonstrated that the induced-fit docking of agonists is a viable way of modifying an inactive crystal structure and bias it toward the in silico recognition of agonists rather than blockers.
Copyright © 2011 Wiley Periodicals, Inc.

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Year:  2011        PMID: 22170280      PMCID: PMC3265623          DOI: 10.1002/jcc.22893

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  45 in total

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Review 3.  Conformational complexity of G-protein-coupled receptors.

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5.  Cloning of the gene and cDNA for mammalian beta-adrenergic receptor and homology with rhodopsin.

Authors:  R A Dixon; B K Kobilka; D J Strader; J L Benovic; H G Dohlman; T Frielle; M A Bolanowski; C D Bennett; E Rands; R E Diehl; R A Mumford; E E Slater; I S Sigal; M G Caron; R J Lefkowitz; C D Strader
Journal:  Nature       Date:  1986 May 1-7       Impact factor: 49.962

Review 6.  Rhodopsin and the others: a historical perspective on structural studies of G protein-coupled receptors.

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7.  Conserved binding mode of human beta2 adrenergic receptor inverse agonists and antagonist revealed by X-ray crystallography.

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Review 9.  The structure and function of G-protein-coupled receptors.

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  11 in total

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2.  Towards predictive docking at aminergic G-protein coupled receptors.

Authors:  Jan Jakubík; Esam E El-Fakahany; Vladimír Doležal
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3.  Computational studies to predict or explain G protein coupled receptor polypharmacology.

Authors:  Kenneth A Jacobson; Stefano Costanzi; Silvia Paoletta
Journal:  Trends Pharmacol Sci       Date:  2014-11-14       Impact factor: 14.819

4.  Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study.

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Review 5.  Accelerating GPCR Drug Discovery With Conformation-Stabilizing VHHs.

Authors:  Toon Laeremans; Zara A Sands; Pieter Claes; Ann De Blieck; Stephane De Cesco; Sarah Triest; Andreas Busch; David Felix; Abhinav Kumar; Veli-Pekka Jaakola; Christel Menet
Journal:  Front Mol Biosci       Date:  2022-05-23

6.  Graph analysis of β2 adrenergic receptor structures: a "social network" of GPCR residues.

Authors:  Samuel Sheftel; Kathryn E Muratore; Michael Black; Stefano Costanzi
Journal:  In Silico Pharmacol       Date:  2013-12-05

7.  Improving virtual screening of G protein-coupled receptors via ligand-directed modeling.

Authors:  Thomas Coudrat; John Simms; Arthur Christopoulos; Denise Wootten; Patrick M Sexton
Journal:  PLoS Comput Biol       Date:  2017-11-13       Impact factor: 4.475

8.  Structural features embedded in G protein-coupled receptor co-crystal structures are key to their success in virtual screening.

Authors:  Thomas Coudrat; Arthur Christopoulos; Patrick Michael Sexton; Denise Wootten
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9.  Conformation guides molecular efficacy in docking screens of activated β-2 adrenergic G protein coupled receptor.

Authors:  Dahlia R Weiss; SeungKirl Ahn; Maria F Sassano; Andrew Kleist; Xiao Zhu; Ryan Strachan; Bryan L Roth; Robert J Lefkowitz; Brian K Shoichet
Journal:  ACS Chem Biol       Date:  2013-03-21       Impact factor: 5.100

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Journal:  Elife       Date:  2020-03-02       Impact factor: 8.140

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