Abstract
Computational strategies aimed at unveiling the thermodynamic and kinetic properties of G Protein-Coupled Receptor (GPCR) activation require extensive molecular dynamics simulations of the receptor embedded in an explicit lipid-water environment. A possible method for efficiently sampling the conformational space of such a complex system is metadynamics (MetaD) with path collective variables (CVs). Here, we applied well-tempered MetaD with path CVs to one of the few GPCRs for which both inactive and fully active experimental structures are available, the μ-opioid receptor (MOR), and assessed the ability of this enhanced sampling method to estimate the thermodynamic properties of receptor activation in line with those obtained by more computationally expensive adaptive sampling protocols. While n-body information theory analysis of these simulations confirmed that MetaD can efficiently characterize ligand-induced allosteric communication across the receptor, standard MetaD cannot be used directly to derive kinetic rates because transitions are accelerated by a bias potential. Applying the principle of Maximum Caliber (MaxCal) to the free-energy landscape of morphine-bound MOR reconstructed from MetaD, we obtained Markov state models that yield kinetic rates of MOR activation in agreement with those obtained by adaptive sampling. Taken together, these results suggest that the MetaD-MaxCal combination creates an efficient strategy for estimating the thermodynamic and kinetic properties of GPCR activation at an affordable computational cost.
Highlights
G protein-coupled receptors (GPCRs) are broadly expressed cell surface receptors whose functional role is to transmit signals from the exterior to the interior of the cell through recognition of different ligands, such as bioactive peptides, amines, nucleosides, and lipids.1,2 It is not surprising that about 30% of drugs available in the market today target G ProteinCoupled Receptor (GPCR) for the purpose of alleviating the effects of a wide range of diseases and conditions.3 Understanding the mechanistic, thermodynamic, and kinetic details of ligandinduced GPCR activation and consequent signaling through intracellular G proteins or β-arrestins is very important as it informs the rational design of improved therapeutics
These results suggest that the MetaD-Maximum Caliber (MaxCal) combination creates an efficient strategy for estimating the thermodynamic and kinetic properties of GPCR activation at an affordable computa
Enhanced sampling algorithms can help speed up these time scales as we demonstrated a few years ago by applying well-tempered metadynamics5 (MetaD) with path collective variables (CVs)6 to study the ligand-induced modulation of the free-energy landscape of two prototypic GPCRs, rhodopsin7 and the β2-adrenergic8 receptor embedded in an explicit 1-palmitoyl-2-oleoyl-snglycero-3-phosphocholine (POPC)/10% cholesterol bilayer
Summary
G protein-coupled receptors (GPCRs) are broadly expressed cell surface receptors whose functional role is to transmit signals from the exterior to the interior of the cell through recognition of different ligands, such as bioactive peptides, amines, nucleosides, and lipids. It is not surprising that about 30% of drugs available in the market today target GPCRs for the purpose of alleviating the effects of a wide range of diseases and conditions. Understanding the mechanistic, thermodynamic, and kinetic details of ligandinduced GPCR activation and consequent signaling through intracellular G proteins or β-arrestins is very important as it informs the rational design of improved therapeutics. Using a high-throughput molecular dynamics (HTMD) adaptive sampling protocol run on large distributed computational resources, we demonstrated that without introducing bias potentials, a total simulation time of ∼240 μs was still necessary to achieve convergence of the free-energy landscape of a morphine-bound μ-opioid receptor (MOR) system embedded in an explicit POPC/10% cholesterol bilayer and to build reliable Markov State Models (MSMs) of the system’s activation dynamics. This approach successfully revealed the presence of two distinct metastable regions of the conformational space in addition to metastable. The efficiency and accuracy of the proposed approach were evaluated by comparing these results with those obtained with a more expensive HTMD adaptive sampling protocol run on distributed computational resources.
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