Abstract
G-protein-coupled receptors (GPCRs) constitute as much as 30% of the overall proteins targeted by FDA-approved drugs. However, paucity of structural experimental information and low sequence identity between members of the family impair the reliability of traditional docking approaches and atomistic molecular dynamics simulations for in silico pharmacological applications. We present here a dual-resolution approach tailored for such low-resolution models. It couples a hybrid molecular mechanics/coarse-grained (MM/CG) scheme, previously developed by us for GPCR–ligand complexes, with a Hamiltonian-based adaptive resolution scheme (H-AdResS) for the solvent. This dual-resolution approach removes potentially inaccurate atomistic details from the model while building a rigorous statistical ensemble—the grand canonical one—in the high-resolution region. We validate the method on a well-studied GPCR–ligand complex, for which the 3D structure is known, against atomistic simulations. This implementation paves the way for future accurate in silico studies of low-resolution ligand/GPCRs models.
Highlights
Membrane proteins constitute as much as 60% of the overall proteins targeted by FDA-approved drugs.[1]
After the Theory section, where the OB-molecular mechanics/ coarse-grained (MM/CG) method is presented in detail, and a Methods section with the simulation details, we validate the approach by comparing structural and dynamical properties computed with the OB-MM/CG scheme with those from allatom molecular dynamics (MD) simulations of a G-protein-coupled receptors (GPCRs)−ligand complex, namely, the β2-adrenergic receptor in complex with its inverse agonist Scarazolol.[53]
Our approach is validated by comparing structural and dynamical properties computed with the OB-MM/CG scheme with those from all-atoms MD simulations of the human β2adrenergic receptor[76] (β2-AR) rhodopsin-like hGPCR. β2-AR is an important target for a variety of drugs, including the FDA-approved antiasthma agonist salbutamol.[77]
Summary
Membrane proteins constitute as much as 60% of the overall proteins targeted by FDA-approved drugs.[1]. An alternative strategy to address this issue consists of including the minimum number of degrees of freedom of the system for the specific problem that one has in mind, leaving out unreliable information that could bias the simulation results.[18,22,23] In this context, hybrid multiscale simulation methods, transcending a single, uniform resolution, represent a highly optimized approach to predict ligands poses;[24−27] on one hand, a high-resolution, atomistic description of the region of interest (which includes the binding site) allows unveiling of the interactions between the receptor and the ligand; on the other hand, the surrounding environment can be described in a coarse-grained, less computationally intensive way
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