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Identifying ligand binding conformations of the ?2-adrenergic receptor by using its agonists as computational probes.

ABSTRACT: Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, ?(2)-adrenergic receptor (?(2)AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of ?(2)AR. We show that the select ligands bind preferentially to different predicted conformers of ?(2)AR, and identify a role of ?(2)AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance.

SUBMITTER: Isin B

PROVIDER: S-EPMC3534076 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Identifying ligand binding conformations of the β2-adrenergic receptor by using its agonists as computational probes.

Isin Basak B Estiu Guillermina G Wiest Olaf O Oltvai Zoltán N ZN

PloS one 20121231 12

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β(2)-adrenergic receptor (β(2)AR). The resulting co ...[more]

PMID: 23300522

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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.

Project description:The drugs salmeterol, formoterol, and salbutamol constitute the frontline treatment of asthma and other chronic pulmonary diseases. These drugs activate the β2-adrenergic receptors (β2-AR), a class A G protein-coupled receptor (GPCR), and differ significantly in their clinical onset and duration of actions. According to the microkinetic model, the long duration of action of salmeterol and formoterol compared with salbutamol were attributed, at least in part, to their high lipophilicity and increased local concentrations in the membrane near the receptor. However, the structural and molecular bases of how the lipophilic drugs reach the binding site of the receptor from the surrounding membrane remain unknown. Using a variety of classic and enhanced molecular dynamics simulation techniques, we investigated the membrane partitioning characteristics, binding, and unbinding mechanisms of the ligands. The obtained results offer remarkable insight into the functional role of membrane lipids in the ligand association process. Strikingly, salmeterol entered the binding site from the bilayer through transmembrane helices 1 and 7. The entry was preceded by membrane-facilitated rearrangement and presentation of its phenyl-alkoxy-alkyl tail as a passkey to an access route gated by F193, a residue known to be critical for salmeterol's affinity. Formoterol's access is through the aqueous path shared by other β2-AR agents. We observed a novel secondary path for salbutamol that is distinct from its primary route. Our study offers a mechanistic description for the membrane-facilitated access and binding of ligands to a membrane protein and establishes a groundwork for recognizing membrane lipids as an integral component in the molecular recognition process. SIGNIFICANCE STATEMENT: The cell membrane's functional role behind the duration of action of long-acting β2-adrenergic receptor (β2-AR) agonists such as salmeterol has been a subject of debate for a long time. This study investigated the binding and unbinding mechanisms of the three commonly used β2-AR agonists, salmeterol, formoterol, and salbutamol, using advanced simulation techniques. The obtained results offer unprecedented insights into the active role of membrane lipids in facilitating access and binding of the ligands, affecting the molecular recognition process and thus their pharmacology.

Dataset Information

Identifying ligand binding conformations of the ?2-adrenergic receptor by using its agonists as computational probes.

Publications

Identifying ligand binding conformations of the β2-adrenergic receptor by using its agonists as computational probes.

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