Dataset Information

In silico analysis of the binding of agonists and blockers to the ?2-adrenergic receptor.

ABSTRACT: Activation of G protein-coupled receptors (GPCRs) is a complex phenomenon. Here, we applied Induced Fit Docking (IFD) in tandem with linear discriminant analysis (LDA) to generate hypotheses on the conformational changes induced to the ?(2)-adrenergic receptor by agonist binding, preliminary to the sequence of events that characterize activation of the receptor. This analysis, corroborated by a follow-up molecular dynamics study, suggested that agonists induce subtle movements to the fifth transmembrane domain (TM5) of the receptor. Furthermore, molecular dynamics also highlighted a correlation between movements of TM5 and the second extracellular loop (EL2), suggesting that freedom of motion of EL2 is required for the agonist-induced TM5 displacement. Importantly, we also showed that the IFD/LDA procedure can be used as a computational means to distinguish agonists from blockers on the basis of the differential conformational changes induced to the receptor. In particular, the two most predictive models obtained are based on the RMSD induced to Ser207 and on the counterclockwise rotation induced to TM5.

SUBMITTER: Vilar S

PROVIDER: S-EPMC3119336 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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In silico analysis of the binding of agonists and blockers to the β2-adrenergic receptor.

Vilar Santiago S Karpiak Joel J Berk Barkin B Costanzi Stefano S

Journal of molecular graphics & modelling 20110119 6

Activation of G protein-coupled receptors (GPCRs) is a complex phenomenon. Here, we applied Induced Fit Docking (IFD) in tandem with linear discriminant analysis (LDA) to generate hypotheses on the conformational changes induced to the β(2)-adrenergic receptor by agonist binding, preliminary to the sequence of events that characterize activation of the receptor. This analysis, corroborated by a follow-up molecular dynamics study, suggested that agonists induce subtle movements to the fifth trans ...[more]

PMID: 21334234

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Project description:G protein-coupled receptors are involved in many biological processes, relaying the extracellular signal inside the cell. Signaling is regulated by the interactions between receptors and their ligands, it can be stimulated by agonists, or inhibited by antagonists or inverse agonists. The development of a new drug targeting a member of this family requires to take into account the pharmacological profile of the designed ligands in order to elicit the desired response. The structure-based virtual screening of chemical libraries may prioritize a specific class of ligands by combining docking results and ligand binding information provided by crystallographic structures. The performance of the method depends on the relevance of the structural data, in particular the conformation of the targeted site, the binding mode of the reference ligand, and the approach used to compare the interactions formed by the docked ligand with those formed by the reference ligand in the crystallographic structure. Here, we propose a new method based on the conformational dynamics of a single protein-ligand reference complex to improve the biased selection of ligands with specific pharmacological properties in a structure-based virtual screening exercise. Interactions patterns between a reference agonist and the receptor, here exemplified on the β2 adrenergic receptor, were extracted from molecular dynamics simulations of the agonist/receptor complex and encoded in graphs used to train a one-class machine learning classifier. Different conditions were tested: low to high affinity agonists, varying simulation duration, considering or ignoring hydrophobic contacts, and tuning of the classifier parametrization. The best models applied to post-process raw data from retrospective virtual screening obtained by docking of test libraries effectively filtered out irrelevant poses, discarding inactive and non-agonist ligands while identifying agonists. Taken together, our results suggest that consistency of the binding mode during the simulation is a key to the success of the method.

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Dataset Information

In silico analysis of the binding of agonists and blockers to the ?2-adrenergic receptor.

Publications

In silico analysis of the binding of agonists and blockers to the β2-adrenergic receptor.

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