Ligand-induced modulation of the free-energy landscape of G protein-coupled receptors explored by adaptive biasing techniques

Determinants of Neutral Antagonism and Inverse Agonism in the β2-Adrenergic Receptor

Free-energy profiles for the activation/deactivation of the β2-adrenergic receptor (ADRB2) with neutral antagonist and inverse agonist ligands have been determined with well-tempered multiple-walker (MW) metadynamics simulations. The inverse agonists carazolol and ICI118551 clearly favor single inactive conformational minima in both the binary and ternary ligand-receptor-G-protein complexes, in accord with the inverse-agonist activity of the ligands. The behavior of neutral antagonists is more complex, as they seem also to affect the recruitment of the G-protein. The results are analyzed in terms of the conformational states of the well-known microswitches that have been proposed as indicators of receptor activity.

Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the β2AR Receptor

With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the β2 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the β2 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the β2 adrenergic receptor.

Flexible Topology: A Dynamic Model of a Continuous Chemical Space

Ligand design problems involve searching chemical space for a molecule with a set of desired properties. As chemical space is discrete, this search must be conducted in a pointwise manner, separately investigating one molecule at a time, which can be inefficient. We propose a method called "Flexible Topology", where a ligand is composed of a set of shapeshifting "ghost" atoms, whose atomic identities and connectivity can dynamically change over the course of a simulation. Ghost atoms are guided toward their target positions using a translation-, rotation-, and index-invariant restraint potential. This is the first step toward a continuous model of chemical space, where a dynamic simulation can move from one molecule to another by following gradients of a potential energy function. This builds on a substantial history of alchemy in the field of molecular dynamics simulation, including the Lambda dynamics method developed by Brooks and co-workers [X. Kong and C.L. Brooks III, J. Chem. Phys. 105, 2414 (1996)], but takes it to an extreme by associating a set of four dynamical attributes with each shapeshifting ghost atom that control not only its presence but also its atomic identity. Here, we outline the theoretical details of this method, its implementation using the OpenMM simulation package, and some preliminary studies of ghost particle assembly simulations in vacuum. We examine a set of 10 small molecules, ranging in size from 6 to 50 atoms, and show that Flexible Topology is able to consistently assemble all of these molecules to high accuracy, beginning from randomly initialized positions and attributes.

General Metadynamics Protocol To Simulate Activation/Deactivation of Class A GPCRs: Proof of Principle for the Serotonin Receptor

We present a generally applicable metadynamics protocol for characterizing the activation free-energy profiles of class A G-protein coupled receptors and a proof-of-principle study for the 5HT_1A-receptor. The almost universal A¹⁰⁰ activation index, which depends on five inter-helix distances, is used as the single collective variable in well-tempered multiple-walker metadynamics simulations. Here, we show free-energy profiles for the serotonin receptor as binary (apo-receptor + G-protein-α-subunit and receptor + ligand) and ternary complexes with two prototypical orthosteric ligands: the full agonist serotonin and the partial agonist aripiprazole. Our results are not only compatible with previously reported experimental and computational data, but they also allow differences between active and inactive conformations to be determined in unprecedented atomic detail, and with respect to the so-called microswitches that have been suggested as determinants of activation, giving insight into their role in the activation mechanism.

Metadynamics simulations leveraged by statistical analyses and artificial intelligence-based tools to inform the discovery of G protein-coupled receptor ligands

G Protein-Coupled Receptors (GPCRs) are a large family of membrane proteins with pluridimensional signaling profiles. They undergo ligand-specific conformational changes, which in turn lead to the differential activation of intracellular signaling proteins and the consequent triggering of a variety of biological responses. This conformational plasticity directly impacts our understanding of GPCR signaling and therapeutic implications, as do ligand-specific kinetic differences in GPCR-induced transducer activation/coupling or GPCR-transducer complex stability. High-resolution experimental structures of ligand-bound GPCRs in the presence or absence of interacting transducers provide important, yet limited, insights into the highly dynamic process of ligand-induced activation or inhibition of these receptors. We and others have complemented these studies with computational strategies aimed at characterizing increasingly accurate metastable conformations of GPCRs using a combination of metadynamics simulations, state-of-the-art algorithms for statistical analyses of simulation data, and artificial intelligence-based tools. This minireview provides an overview of these approaches as well as lessons learned from them towards the identification of conformational states that may be difficult or even impossible to characterize experimentally and yet important to discover new GPCR ligands.

Receptor-proximal effectors mediating GnRH actions in the goldfish pituitary: Involvement of G protein subunits and GRKs

In goldfish (Carassius auratus), two endogenous isoforms of gonadotropin-releasing hormone (GnRH) stimulate luteinizing hormone (LH) and growth hormone (GH) secretion. These isoforms, GnRH2 and GnRH3, act on a shared population of cell-surface GnRH receptors (GnRHRs) expressed on both gonadotrophs and somatotrophs, and can signal through unique, yet partially overlapping, suites of intracellular effectors, in a phenomenon known as functional selectivity or biased signalling. In this study, G-protein alpha (Gα) subunits were targeted with two inhibitors, YM-254890 and BIM-46187, to ascertain the contribution of specific G-protein subunits in GnRH signalling. Results with the Gα_q/11-specific inhibitor YM-254890 on primary cultures of goldfish pituitary cells revealed the use of these subunits in GnRH control of both LH and GH release, as well as GnRH-induced elevations in phospho-ERK levels. Results with the pan-Gα inhibitor BIM-46187 matched those using YM-254890 in LH release but GH responses differed, indicating additional, non-Gα_q/11 subunits may be involved in somatotrophs. BIM-46187 also elevated unstimulated LH and GH release suggesting that Gα subunits regulate basal hormone secretion. Furthermore, G-protein-coupled receptor kinase (GRK2/3) inhibition reduced LH responses to GnRH2 and GnRH3, and selectively enhanced GnRH2-stimulated GH release, indicating differential use of GRK2/3 in GnRH actions on gonadotrophs and somatotrophs. These findings in a primary untransformed system provide the first direct evidence to establish Gα_q/11 as an obligate driver of GnRH signalling in goldfish pituitary cells, and additionally describe the differential agonist- and cell type-selective involvement of GRK2/3 in this system.

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling

We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A₁ receptor (A₁AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A₁AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.

Structure based prediction of a novel GPR120 antagonist based on pharmacophore screening and molecular dynamics simulations

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Hypothesis of the important residues in conserving the GPR120S “ionic-lock”.

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Computational model targeting W277 and N313 for virtual screening of GPR120S ligands.

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Cpd 9 emerged as a potential GPR120S antagonist and anti-cancer treatment.

The G-protein coupled receptor, GPR120, has ubiquitous expression and multifaceted roles in modulating metabolic and anti-inflammatory processes. Recent implications of its role in cancer progression have presented GPR120 as an attractive oncogenic drug target. GPR120 gene knockdown in breast cancer studies revealed a role of GPR120-induced chemoresistance in epirubicin and cisplatin-induced DNA damage in tumour cells. Higher expression and activation levels of GPR120 is also reported to promote tumour angiogenesis and cell migration in colorectal cancer. Some agonists targeting GPR120 have been reported, such as TUG891 and Compound39, but to date development of small-molecule inhibitors of GPR120 is limited.

Herein, following homology modelling of the receptor a pharmacophore hypothesis was derived from 300 ns all-atomic molecular dynamics (MD) simulations on apo, TUG891-bound and Compound39-bound GPR120S (short isoform) receptor models embedded in a water solvated lipid bilayer system. We performed comparative MD analysis on protein–ligand interactions between the two agonist and apo simulations on the stability of the “ionic lock” – a Class A GPCRs characteristic of receptor activation and inactivation. The detailed analysis predicted that ligand interactions with W277 and N313 are critical to conserve the “ionic-lock” conformation (R136 of Helix 3) and prevent GPR120S receptor activation. The results led to generation of a W277 and N313 focused pharmacophore hypothesis and the screening of the ZINC15 database using ZINCPharmer through the structure-based pharmacophore.

100 ns all-atomic molecular dynamics (MD) simulations were performed on 9 small molecules identified and Cpd 9, (2-hydroxy-N-{4-[(6-hydroxy-2-methylpyrimidin-4-yl) amino] phenyl} benzamide) was predicted to be a small-molecule GPR120S antagonist. The conformational results from the collective all-atomic MD analysis provided structural information for further identification and optimisation of novel druggable inhibitors of GPR120S using this rational design approach, which could have future potential for anti-cancer drug development studies.

Delineating the conformational landscape of the adenosine A2A receptor during G protein coupling

G-protein-coupled receptors (GPCRs) represent a ubiquitous membrane protein family and are important drug targets. Their diverse signaling pathways are driven by complex pharmacology arising from a conformational ensemble rarely captured by structural methods. Here, fluorine nuclear magnetic resonance spectroscopy (¹⁹F NMR) is used to delineate key functional states of the adenosine A_2A receptor (A_2AR) complexed with heterotrimeric G protein (Gα_sβ₁γ₂) in a phospholipid membrane milieu. Analysis of A_2AR spectra as a function of ligand, G protein, and nucleotide identifies an ensemble represented by inactive states, a G-protein-bound activation intermediate, and distinct nucleotide-free states associated with either partial- or full-agonist-driven activation. The Gβγ subunit is found to be critical in facilitating ligand-dependent allosteric transmission, as shown by ¹⁹F NMR, biochemical, and computational studies. The results provide a mechanistic basis for understanding basal signaling, efficacy, precoupling, and allostery in GPCRs.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

Identification of ligand-specific G protein-coupled receptor states and prediction of downstream efficacy via data-driven modeling

Ligand binding stabilizes different G protein-coupled receptor states via a complex allosteric process that is not completely understood. Here, we have derived free energy landscapes describing activation of the β₂ adrenergic receptor bound to ligands with different efficacy profiles using enhanced sampling molecular dynamics simulations. These reveal shifts toward active-like states at the Gprotein-binding site for receptors bound to partial and full agonists, and that the ligands modulate the conformational ensemble of the receptor by tuning protein microswitches. We indeed find an excellent correlation between the conformation of the microswitches close to the ligand binding site and in the transmembrane region and experimentally reported cyclic adenosine monophosphate signaling responses. Dimensionality reduction further reveals the similarity between the unique conformational states induced by different ligands, and examining the output of classifiers highlights two distant hotspots governing agonism on transmembrane helices 5 and 7.

Free energy calculations of the functional selectivity of 5-HT2B G protein-coupled receptor

G Protein-Coupled Receptors (GPCRs) mediate intracellular signaling in response to extracellular ligand binding and are the target of one-third of approved drugs. Ligand binding modulates the GPCR molecular free energy landscape by preferentially stabilizing active or inactive conformations that dictate intracellular protein recruitment and downstream signaling. We perform enhanced sampling molecular dynamics simulations to recover the free energy surfaces of a thermostable mutant of the GPCR serotonin receptor 5-HT2B in the unliganded form and bound to a lysergic acid diethylamide (LSD) agonist and lisuride antagonist. LSD binding imparts a ∼110 kJ/mol driving force for conformational rearrangement into an active state. The lisuride-bound form is structurally similar to the apo form and only ∼24 kJ/mol more stable. This work quantifies ligand-induced conformational specificity and functional selectivity of 5-HT2B and presents a platform for high-throughput virtual screening of ligands and rational engineering of the ligand-bound molecular free energy landscape.

G-Protein coupled receptors: structure and function in drug discovery

The G-protein coupled receptors (GPCRs) superfamily comprise similar proteins arranged into families or classes thus making it one of the largest in the mammalian genome. GPCRs take part in many vital physiological functions making them targets for numerous novel drugs. GPCRs share some distinctive features, such as the seven transmembrane domains, they also differ in the number of conserved residues in their transmembrane domain. Here we provide an introductory and accessible review detailing the computational advances in GPCR pharmacology and drug discovery. An overview is provided on family A-C GPCRs; their structural differences, GPCR signalling, allosteric binding and cooperativity. The dielectric constant (relative permittivity) of proteins is also discussed in the context of site-specific environmental effects.

Energy Landscapes Reveal Agonist Control of G Protein-Coupled Receptor Activation via Microswitches

Agonist binding to G protein-coupled receptors (GPCRs) leads to conformational changes in the transmembrane region that activate cytosolic signaling pathways. Although high-resolution structures of different receptor states are available, atomistic details of allosteric signaling across the membrane remain elusive. We calculated free energy landscapes of β₂ adrenergic receptor activation using atomistic molecular dynamics simulations in an optimized string of swarms framework, which shed new light on how microswitches govern the equilibrium between conformational states. Contraction of the extracellular binding site in the presence of the agonist BI-167107 is obligatorily coupled to conformational changes in a connector motif located in the core of the transmembrane region. The connector is probabilistically coupled to the conformation of the intracellular region. An active connector promotes desolvation of a buried cavity, a twist of the conserved NPxxY motif, and an interaction between two conserved tyrosines in transmembrane helices 5 and 7 (Y–Y motif), which lead to a larger population of active-like states at the G protein binding site. This coupling is augmented by protonation of the strongly conserved Asp79^2.50. The agonist binding site hence communicates with the intracellular region via a cascade of locally connected microswitches. Characterization of these can be used to understand how ligands stabilize distinct receptor states and contribute to development drugs with specific signaling properties. The developed simulation protocol can likely be transferred to other class A GPCRs.

Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes

Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.

Allostery in G protein-coupled receptors investigated by molecular dynamics simulations

G-protein-coupled receptors (GPCRs) are allosteric signaling machines that trigger distinct functional responses depending on the particular conformational state they adopt upon binding. This so-called GPCR functional selectivity is prompted by ligands of different efficacy binding at orthosteric or allosteric sites on the receptor, as well as by interactions with intracellular protein partners or other receptor types. Molecular dynamics (MD) simulations can provide important mechanistic, thermodynamic, and kinetic insights into these interactions at a level of molecular detail that is necessary to rightly inform modern drug discovery. Here, we review the most recent MD contributions to understanding GPCR allostery, with an emphasis on their strengths and limitations.

Molecular Dynamics Simulations of the Allosteric Modulation of the Adenosine A2A Receptor by a Mini-G Protein

Through their coupling to G proteins, G Protein-Coupled Receptors (GPCRs) trigger cellular responses to various signals. Some recent experiments have interestingly demonstrated that the G protein can also act on the receptor by favoring a closed conformation of its orthosteric site, even in the absence of a bound agonist. In this work, we explored such an allosteric modulation by performing extensive molecular dynamics simulations on the adenosine A2 receptor (A2aR) coupled to the Mini-Gs protein. In the presence of the Mini-Gs, we confirmed a restriction of the receptor’s agonist binding site that can be explained by a modulation of the intrinsic network of contacts of the receptor. Of interest, we observed similar effects with the C-terminal helix of the Mini-Gs, showing that the observed effect on the binding pocket results from direct local contacts with the bound protein partner that cause a rewiring of the whole receptor’s interaction network.

An efficient strategy to estimate thermodynamics and kinetics of G protein-coupled receptor activation using metadynamics and maximum caliber

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.

Defining an Optimal Metric for the Path Collective Variables

Path Collective Variables (PCVs) are a set of path-like variables that have been successfully used to investigate complex chemical and biological processes and compute their associated free energy surfaces and kinetics. Their current implementation relies on general, but at times inefficient, metrics (such as RMSD or DRMSD) to evaluate the distance between the instantaneous conformational state during the simulation and the reference coordinates defining the path. In this work, we present a new algorithm to construct optimal PCVs metrics as linear combinations of different CVs weighted through a spectral gap optimization procedure. The method was tested first on a simple model, trialanine peptide, in vacuo and then on a more complex path of an anticancer inhibitor binding to its pharmacological target. We also compared the results to those obtained with other path-based algorithms. We find that not only our proposed approach is able to automatically select relevant CVs for the PCVs metric but also that the resulting PCVs allow for reconstructing the associated free energy very efficiently. What is more, at difference with other path-based methods, our algorithm is able to explore nonlocally the reaction path space.

Exploring the free-energy landscape of GPCR activation

G-protein-coupled receptors (GPCRs) are a large group of membrane-bound receptor proteins that are involved in a plethora of diverse processes (e.g., vision, hormone response). In mammals, and particularly in humans, GPCRs are involved in many signal transduction pathways and, as such, are heavily studied for their immense pharmaceutical potential. Indeed, a large fraction of drugs target various GPCRs, and drug-development is often aimed at GPCRs. Therefore, understanding the activation of GPCRs is a challenge of major importance both from fundamental and practical considerations. And yet, despite the remarkable progress in structural understanding, we still do not have a translation of the structural information to an energy-based picture. Here we use coarse-grained (CG) modeling to chart the free-energy landscape of the activation process of the β-2 adrenergic receptor (β₂AR) as a representative GPCR. The landscape provides the needed tool for analyzing the processes that lead to activation of the receptor upon binding of the ligand (adrenaline) while limiting constitutive activation. Our results pave the way to better understand the biological mechanisms of action of the β₂AR and GPCRs, from a physical chemistry point of view rather than simply by observing the receptor's behavior physiologically.

Molecular Dynamics Simulation for All

The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other biomolecules in full atomic detail and at very fine temporal resolution. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of experimental structural data, have increased the appeal of biomolecular simulation to experimentalists-a trend particularly noticeable in, although certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomolecules, in uncovering the structural basis for disease, and in the design and optimization of small molecules, peptides, and proteins. Here we describe, in practical terms, the types of information MD simulations can provide and the ways in which they typically motivate further experimental work.

Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists

Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.

Kinetics of Huperzine A Dissociation from Acetylcholinesterase via Multiple Unbinding Pathways

The dissociation of huperzine A (hupA) from Torpedo californica acetylcholinesterase ( TcAChE) was investigated by 4 μs unbiased and biased all-atom molecular dynamics (MD) simulations in explicit solvent. We performed our study using memetic sampling (MS) for the determination of reaction pathways (RPs), metadynamics to calculate free energy, and maximum-likelihood estimation (MLE) to recover kinetic rates from unbiased MD simulations. Our simulations suggest that the dissociation of hupA occurs mainly via two RPs: a front door along the axis of the active-site gorge (pwf) and through a new transient side door (pws), i.e., formed by the Ω-loop (residues 67-94 of TcAChE). An analysis of the inhibitor unbinding along the RPs suggests that pws is opened transiently after hupA and the Ω-loop reach a low free-energy transition state characterized by the orientation of the pyridone group of the inhibitor directed toward the Ω-loop plane. Unlike pws, pwf does not require large structural changes in TcAChE to be accessible. The estimated free energies and rates agree well with available experimental data. The dissociation rates along the unbinding pathways are similar, suggesting that the dissociation of hupA along pws is likely to be relevant. This indicates that perturbations to hupA- TcAChE interactions could potentially induce pathway hopping. In summary, our results characterize the slow-onset inhibition of TcAChE by hupA, which may provide the structural and energetic bases for the rational design of the next-generation slow-onset inhibitors with optimized pharmacokinetic properties for the treatment of Alzheimer's disease.

GPCRs: What Can We Learn from Molecular Dynamics Simulations?

Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.

Patterns of coevolving amino acids unveil structural and dynamical domains

Patterns of interacting amino acids are so preserved within protein families that the sole analysis of evolutionary comutations can identify pairs of contacting residues. It is also known that evolution conserves functional dynamics, i.e., the concerted motion or displacement of large protein regions or domains. Is it, therefore, possible to use a pure sequence-based analysis to identify these dynamical domains? To address this question, we introduce here a general coevolutionary coupling analysis strategy and apply it to a curated sequence database of hundreds of protein families. For most families, the sequence-based method partitions amino acids into a few clusters. When viewed in the context of the native structure, these clusters have the signature characteristics of viable protein domains: They are spatially separated but individually compact. They have a direct functional bearing too, as shown for various reference cases. We conclude that even large-scale structural and functionally related properties can be recovered from inference methods applied to evolutionary-related sequences. The method introduced here is available as a software package and web server (spectrus.sissa.it/spectrus-evo_webserver).

Conformational landscapes of membrane proteins delineated by enhanced sampling molecular dynamics simulations

The expansion of computational power, better parameterization of force fields, and the development of novel algorithms to enhance the sampling of the free energy landscapes of proteins have allowed molecular dynamics (MD) simulations to become an indispensable tool to understand the function of biomolecules. The temporal and spatial resolution of MD simulations allows for the study of a vast number of processes of interest. Here, we review the computational efforts to uncover the conformational free energy landscapes of a subset of membrane proteins: ion channels, transporters and G-protein coupled receptors. We focus on the various enhanced sampling techniques used to study these questions, how the conclusions come together to build a coherent picture, and the relationship between simulation outcomes and experimental observables.

Membrane-induced organization and dynamics of the N-terminal domain of chemokine receptor CXCR1: insights from atomistic simulations

The CXC chemokine receptor 1 (CXCR1) is an important member of the G protein-coupled receptor (GPCR) family in which the extracellular N-terminal domain has been implicated in ligand binding and selectivity. The structure of this domain has not yet been elucidated due to its inherent dynamics, but experimental evidence points toward membrane-dependent organization and dynamics. To gain molecular insight into the interaction of the N-terminal domain with the membrane bilayer, we performed a series of microsecond time scale atomistic simulations of the N-terminal domain of CXCR1 in the presence and absence of POPC bilayers. Our results show that the peptide displays a high propensity to adopt a β-sheet conformation in the presence of the membrane bilayer. The interaction of the peptide with the membrane bilayer was found to be transient in our simulations. Interestingly, a scrambled peptide, containing the same residues in a randomly varying sequence, did not exhibit membrane-modulated structural dynamics. These results suggest that sequence-dependent electrostatics, modulated by the membrane, could play an important role in folding of the N-terminal domain. We believe that our results reinforce the emerging paradigm that cellular membranes could be important modulators of function of G protein-coupled receptors such as CXCR1.

Ligand diffusion in proteins via enhanced sampling in molecular dynamics

Computational simulations in biophysics describe the dynamics and functions of biological macromolecules at the atomic level. Among motions particularly important for life are the transport processes in heterogeneous media. The process of ligand diffusion inside proteins is an example of a complex rare event that can be modeled using molecular dynamics simulations. The study of physical interactions between a ligand and its biological target is of paramount importance for the design of novel drugs and enzymes. Unfortunately, the process of ligand diffusion is difficult to study experimentally. The need for identifying the ligand egress pathways and understanding how ligands migrate through protein tunnels has spurred the development of several methodological approaches to this problem. The complex topology of protein channels and the transient nature of the ligand passage pose difficulties in the modeling of the ligand entry/escape pathways by canonical molecular dynamics simulations. In this review, we report a methodology involving a reconstruction of the ligand diffusion reaction coordinates and the free-energy profiles along these reaction coordinates using enhanced sampling of conformational space. We illustrate the above methods on several ligand-protein systems, including cytochromes and G-protein-coupled receptors. The methods are general and may be adopted to other transport processes in living matter.

The Allostery Landscape: Quantifying Thermodynamic Couplings in Biomolecular Systems

Allostery plays a fundamental role in most biological processes. However, little theory is available to describe it outside of two-state models. Here we use a statistical mechanical approach to show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the Φ/Ψ free energy surface, and by identifying the interactions that are necessary for this coupling.

Graded activation and free energy landscapes of a muscarinic G-protein-coupled receptor

G-protein-coupled receptors (GPCRs) recognize ligands of widely different efficacies, from inverse to partial and full agonists, which transduce cellular signals at differentiated levels. However, the mechanism of such graded activation remains unclear. Using the Gaussian accelerated molecular dynamics (GaMD) method that enables both unconstrained enhanced sampling and free energy calculation, we have performed extensive GaMD simulations (∼19 μs in total) to investigate structural dynamics of the M₂ muscarinic GPCR that is bound by the full agonist iperoxo (IXO), the partial agonist arecoline (ARC), and the inverse agonist 3-quinuclidinyl-benzilate (QNB), in the presence or absence of the G-protein mimetic nanobody. In the receptor-nanobody complex, IXO binding leads to higher fluctuations in the protein-coupling interface than ARC, especially in the receptor transmembrane helix 5 (TM5), TM6, and TM7 intracellular domains that are essential elements for GPCR activation, but less flexibility in the receptor extracellular region due to stronger binding compared with ARC. Two different binding poses are revealed for ARC in the orthosteric pocket. Removal of the nanobody leads to GPCR deactivation that is characterized by inward movement of the TM6 intracellular end. Distinct low-energy intermediate conformational states are identified for the IXO- and ARC-bound M₂ receptor. Both dissociation and binding of an orthosteric ligand are observed in a single all-atom GPCR simulation in the case of partial agonist ARC binding to the M₂ receptor. This study demonstrates the applicability of GaMD for exploring free energy landscapes of large biomolecules and the simulations provide important insights into the GPCR functional mechanism.

Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor

Design of ligands that provide receptor selectivity has emerged as a new paradigm for drug discovery of G protein-coupled receptors, and may, for certain families of receptors, only be achieved via identification of chemically diverse allosteric modulators. Here, the extracellular vestibule of the M2 muscarinic acetylcholine receptor (mAChR) is targeted for structure-based design of allosteric modulators. Accelerated molecular dynamics (aMD) simulations were performed to construct structural ensembles that account for the receptor flexibility. Compounds obtained from the National Cancer Institute (NCI) were docked to the receptor ensembles. Retrospective docking of known ligands showed that combining aMD simulations with Glide induced fit docking (IFD) provided much-improved enrichment factors, compared with the Glide virtual screening workflow. Glide IFD was thus applied in receptor ensemble docking, and 38 top-ranked NCI compounds were selected for experimental testing. In [(3)H]N-methylscopolamine radioligand dissociation assays, approximately half of the 38 lead compounds altered the radioligand dissociation rate, a hallmark of allosteric behavior. In further competition binding experiments, we identified 12 compounds with affinity of ≤30 μM. With final functional experiments on six selected compounds, we confirmed four of them as new negative allosteric modulators (NAMs) and one as positive allosteric modulator of agonist-mediated response at the M2 mAChR. Two of the NAMs showed subtype selectivity without significant effect at the M1 and M3 mAChRs. This study demonstrates an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies.

Cholesterol-dependent Conformational Plasticity in GPCR Dimers

The organization and function of the serotonin_1A receptor, an important member of the GPCR family, have been shown to be cholesterol-dependent, although the molecular mechanism is not clear. We performed a comprehensive structural and dynamic analysis of dimerization of the serotonin_1A receptor by coarse-grain molecular dynamics simulations totaling 3.6 ms to explore the molecular details of its cholesterol-dependent association. A major finding is that the plasticity and flexibility of the receptor dimers increase with increased cholesterol concentration. In particular, a dimer interface formed by transmembrane helices I-I was found to be sensitive to cholesterol. The modulation of dimer interface appears to arise from a combination of direct cholesterol occupancy and indirect membrane effects. Interestingly, the presence of cholesterol at the dimer interface is correlated with increased dimer plasticity and flexibility. These results represent an important step in characterizing the molecular interactions in GPCR organization with potential relevance to therapeutic interventions.

Update on melatonin receptors: IUPHAR Review 20

Melatonin receptors are seven transmembrane-spanning proteins belonging to the GPCR superfamily. In mammals, two melatonin receptor subtypes exist - MT1 and MT2 - encoded by the MTNR1A and MTNR1B genes respectively. The current review provides an update on melatonin receptors by the corresponding subcommittee of the International Union of Basic and Clinical Pharmacology. We will highlight recent developments of melatonin receptor ligands, including radioligands, and give an update on the latest phenotyping results of melatonin receptor knockout mice. The current status and perspectives of the structure of melatonin receptor will be summarized. The physiological importance of melatonin receptor dimers and biologically important and type 2 diabetes-associated genetic variants of melatonin receptors will be discussed. The role of melatonin receptors in physiology and disease will be further exemplified by their functions in the immune system and the CNS. Finally, antioxidant and free radical scavenger properties of melatonin and its relation to melatonin receptors will be critically addressed.

G-protein coupled receptors: advances in simulation and drug discovery

G-protein coupled receptors (GPCRs), the largest family of human membrane proteins, mediate cellular signaling and represent primary targets of about one third of currently marketed drugs. GPCRs undergo highly dynamic structural transitions during signal transduction, from binding of extracellular ligands to coupling with intracellular effector proteins. Molecular dynamics (MD) simulations have been utilized to investigate GPCR signaling mechanisms (such as pathways of ligand binding and receptor activation/deactivation) and to design novel small-molecule drug candidates. Future research directions point towards modeling cooperative binding of multiple orthosteric and allosteric ligands to GPCRs, GPCR oligomerization and interactions of GPCRs with different intracellular signaling proteins. Through methodological and supercomputing advances, MD simulations will continue to provide important insights into GPCR signaling mechanisms and further facilitate structure-based drug design.

In silico Exploration of the Conformational Universe of GPCRs

The structural plasticity of G protein coupled receptors (GPCRs) leads to a conformational universe going from inactive to active receptor states with several intermediate states. Many of them have not been captured yet and their role for GPCR activation is not well understood. The study of this conformational space and the transition dynamics between different receptor populations is a major challenge in molecular biophysics. The rational design of effector molecules that target such receptor populations allows fine-tuning receptor signalling with higher specificity to produce drugs with safer therapeutic profiles. In this minireview, we outline highly conserved receptor regions which are considered determinant for the establishment of distinct receptor states. We then discuss in-silico approaches such as dimensionality reduction methods and Markov State Models to explore the GPCR conformational universe and exploit the obtained conformations through structure-based drug design.

Allosteric Mechanisms of Molecular Machines at the Membrane: Transport by Sodium-Coupled Symporters

Solute transport across cell membranes is ubiquitous in biology as an essential physiological process. Secondary active transporters couple the unfavorable process of solute transport against its concentration gradient to the energetically favorable transport of one or several ions. The study of such transporters over several decades indicates that their function involves complex allosteric mechanisms that are progressively being revealed in atomistic detail. We focus on two well-characterized sodium-coupled symporters: the bacterial amino acid transporter LeuT, which is the prototype for the "gated pore" mechanism in the mammalian synaptic monoamine transporters, and the archaeal GltPh, which is the prototype for the "elevator" mechanism in the mammalian excitatory amino acid transporters. We present the evidence for the role of allostery in the context of a quantitative formalism that can reconcile biochemical and biophysical data and thereby connects directly to recent insights into the molecular structure and dynamics of these proteins. We demonstrate that, while the structures and mechanisms of these transporters are very different, the available data suggest a common role of specific models of allostery in their functions. We argue that such allosteric mechanisms appear essential not only for sodium-coupled symport in general but also for the function of other types of molecular machines in the membrane.

General trends of dihedral conformational transitions in a globular protein

Dihedral conformational transitions are analyzed systematically in a model globular protein, cytochrome P450cam, to examine their structural and chemical dependences through combined conventional molecular dynamics (cMD), accelerated molecular dynamics (aMD) and adaptive biasing force (ABF) simulations. The aMD simulations are performed at two acceleration levels, using dihedral and dual boost, respectively. In comparison with cMD, aMD samples protein dihedral transitions approximately two times faster on average using dihedral boost, and ∼ 3.5 times faster using dual boost. In the protein backbone, significantly higher dihedral transition rates are observed in the bend, coil, and turn flexible regions, followed by the β bridge and β sheet, and then the helices. Moreover, protein side chains of greater length exhibit higher transition rates on average in the aMD-enhanced sampling. Side chains of the same length (particularly Nχ = 2) exhibit decreasing transition rates with residues when going from hydrophobic to polar, then charged and aromatic chemical types. The reduction of dihedral transition rates is found to be correlated with increasing energy barriers as identified through ABF free energy calculations. These general trends of dihedral conformational transitions provide important insights into the hierarchical dynamics and complex free energy landscapes of functional proteins.

Allosteric effects of sodium ion binding on activation of the m3 muscarinic g-protein-coupled receptor

G-protein-coupled receptors (GPCRs) are important membrane proteins that mediate cellular signaling and represent primary targets for about one-third of currently marketed drugs. Recent x-ray crystallographic studies identified distinct conformations of GPCRs in the active and inactive states. An allosteric sodium ion was found bound to a highly conserved D2.50 residue in inactive GPCRs, whereas the D2.50 allosteric pocket became collapsed in active GPCR structures. However, the dynamic mechanisms underlying these observations remain elusive. In this study, we aimed to understand the mechanistic effects of sodium ion binding on dynamic activation of the M3 muscarinic GPCR through long-timescale accelerated molecular dynamics (aMD) simulations. Results showed that with the D2.50 residue deprotonated, the M3 receptor is bound by an allosteric sodium ion and confined mostly in the inactive state with remarkably reduced flexibility. In contrast, the D2.50-protonated receptor does not exhibit sodium ion binding to the D2.50 allosteric site and samples a significantly larger conformational space. The receptor activation is captured and characterized by large-scale structural rearrangements of the transmembrane helices via dynamic hydrogen bond and salt bridge interactions. The residue motions are highly correlated during receptor activation. Further network analysis revealed that the allosteric signaling between residue D2.50 and key residues in the intracellular, extracellular, and orthosteric pockets is significantly weakened upon sodium ion binding.

Ligand induced change of β2 adrenergic receptor from active to inactive conformation and its implication for the closed/open state of the water channel: insight from molecular dynamics simulation, free energy calculation and Markov state model analysis

The reported crystal structures of β2 adrenergic receptor (β2AR) reveal that the open and closed states of the water channel are correlated with the inactive and active conformations of β2AR. However, more details about the process by which the water channel states are affected by the active to inactive conformational change of β2AR remain illusive. In this work, molecular dynamics simulations are performed to study the dynamical inactive and active conformational change of β2AR induced by inverse agonist ICI 118,551. Markov state model analysis and free energy calculation are employed to explore the open and close states of the water channel. The simulation results show that inverse agonist ICI 118,551 can induce water channel opening during the conformational transition of β2AR. Markov state model (MSM) analysis proves that the energy contour can be divided into seven states. States S1, S2 and S5, which represent the active conformation of β2AR, show that the water channel is in the closed state, while states S4 and S6, which correspond to the intermediate state conformation of β2AR, indicate the water channel opens gradually. State S7, which represents the inactive structure of β2AR, corresponds to the full open state of the water channel. The opening mechanism of the water channel is involved in the ligand-induced conformational change of β2AR. These results can provide useful information for understanding the opening mechanism of the water channel and will be useful for the rational design of potent inverse agonists of β2AR.

AIM for Allostery: Using the Ising Model to Understand Information Processing and Transmission in Allosteric Biomolecular Systems

In performing their biological functions, molecular machines must process and transmit information with high fidelity. Information transmission requires dynamic coupling between the conformations of discrete structural components within the protein positioned far from one another on the molecular scale. This type of biomolecular “action at a distance” is termed allostery. Although allostery is ubiquitous in biological regulation and signal transduction, its treatment in theoretical models has mostly eschewed quantitative descriptions involving the system's underlying structural components and their interactions. Here, we show how Ising models can be used to formulate an approach to allostery in a structural context of interactions between the constitutive components by building simple allosteric constructs we termed Allosteric Ising Models (AIMs). We introduce the use of AIMs in analytical and numerical calculations that relate thermodynamic descriptions of allostery to the structural context, and then show that many fundamental properties of allostery, such as the multiplicative property of parallel allosteric channels, are revealed from the analysis of such models. The power of exploring mechanistic structural models of allosteric function in more complex systems by using AIMs is demonstrated by building a model of allosteric signaling for an experimentally well-characterized asymmetric homodimer of the dopamine D2 receptor.

Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms

CONSPECTUS: This Account highlights recent advances and discusses major challenges in the field of drug-target recognition, binding, and unbinding studied using metadynamics-based approaches, with particular emphasis on their role in structure-based design. Computational chemistry has significantly contributed to drug design and optimization in an extremely broad range of areas, including prediction of target druggability and drug likeness, de novo design, fragment screening, ligand docking, estimation of binding affinity, and modulation of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. Computationally driven drug discovery must continuously adapt to keep pace with the evolving knowledge of the factors that modulate the pharmacological action of drugs. There is thus an urgent need for novel computational approaches that integrate the vast amount of complex information currently available for small (bio)organic compounds, biologically relevant targets and their complexes, while also accounting accurately for the thermodynamics and kinetics of drug-target association, the intrinsic dynamical behavior of biomolecular systems, and the complexity of protein-protein networks. Understanding the mechanism of drug binding to and unbinding from biological targets is fundamental for optimizing lead compounds and designing novel biologically active ones. One major challenge is the accurate description of the conformational complexity prior to and upon formation of drug-target complexes. Recently, enhanced sampling methods, including metadynamics and related approaches, have been successfully applied to investigate complex mechanisms of drugs binding to flexible targets. Metadynamics is a family of enhanced sampling techniques aimed at enhancing the rare events and reconstructing the underlying free energy landscape as a function of a set of order parameters, usually referred to as collective variables. Studies of drug binding mechanisms have predicted the most probable association and dissociation pathways and the related binding free energy profile. In addition, the availability of an efficient open-source implementation, running on cost-effective GPU (i.e., graphical processor unit) architectures, has considerably decreased the learning curve and the computational costs of the methods, and increased their adoption by the community. Here, we review the recent contributions of metadynamics and other enhanced sampling methods to the field of drug-target recognition and binding. We discuss how metadynamics has been used to search for transition states, to predict binding and unbinding paths, to treat conformational flexibility, and to compute free energy profiles. We highlight the importance of such predictions in drug discovery. Major challenges in the field and possible solutions will finally be discussed.

Exploration of the antagonist CP-376395 escape pathway for the corticotropin-releasing factor receptor 1 by random acceleration molecular dynamics simulations

Corticotropin-releasing factor receptor 1 (CRF1R), a member of class B G-protein-coupled receptors (GPCRs), plays an important role in the treatment of osteoporosis, diabetes, depression, migraine and anxiety. To explore the escape pathway of the antagonist CP-376395 in the binding pocket of CRF1R, molecular dynamics (MD) simulations, dynamical network analysis, random acceleration molecular dynamics (RAMD) simulations and adaptive biasing force (ABF) calculations were performed on the crystal structure of CRF1R in complex with CP-376395. The results of dynamical network analysis show that TM7 of CRF1R has the strongest edges during MD simulation. The bent part of TM7 forms a V-shape pocket with Gly356(7.50). Asn283(5.50) has high hydrogen bond occupancy during 100 ns MD simulations and is the key interaction residue with the antagonist in the binding pocket of CRF1R. RAMD simulation has identified three possible pathways (PW1, PW2 and PW3) for CP-376395 to escape from the binding pocket of CRF1R. The PW3 pathway was proved to be the most likely escape pathway for CP-376395. The free energy along the PW3 pathway was calculated by using ABF simulations. Two energy barriers were found along the reaction coordinates. Residues Leu323(6.49), Asn283(5.50) and Met206(3.47) contribute to the steric hindrance for the first energy barrier. Residues His199(3.40) and Gln355(7.49) contribute to the second energy barrier through the hydrogen bonding interaction between CP-376395 and CRF1R. The results of our study can not only provide useful information to understand the interaction mechanism between CP-376395 and CRF1R, but also provide the details about the possible escape pathway and the free energy profile of CP-376395 in the pocket of CRF1R.

Computational structural biology of opioid receptors

The publication of high-resolution structures for all of the opioid receptor subfamilies has unveiled exciting opportunities for mechanistic insight into the molecular mechanisms underlying the biology of nociception, reward, and higher cognitive functions, as well as promises for progress in several clinical areas such as pain management, physiological dependence, addiction, and mood disorders. To turn this promise into novel and improved therapeutic entities, however, this information needs to be supplemented with research strategies that explore the dynamic behavior of the proteins and their interactions with other receptors and ligands in their physiological environment.Here we describe state-of-the-art molecular dynamics computational protocols, based on all-atom and coarse-grained modeling techniques, designed to estimate crucial thermodynamic and kinetic parameters describing the binding of small-molecule ligands and the formation of supramolecular complexes.

Search for β2 adrenergic receptor ligands by virtual screening via grid computing and investigation of binding modes by docking and molecular dynamics simulations

We designed a program called MolGridCal that can be used to screen small molecule database in grid computing on basis of JPPF grid environment. Based on MolGridCal program, we proposed an integrated strategy for virtual screening and binding mode investigation by combining molecular docking, molecular dynamics (MD) simulations and free energy calculations. To test the effectiveness of MolGridCal, we screened potential ligands for β2 adrenergic receptor (β2AR) from a database containing 50,000 small molecules. MolGridCal can not only send tasks to the grid server automatically, but also can distribute tasks using the screensaver function. As for the results of virtual screening, the known agonist BI-167107 of β2AR is ranked among the top 2% of the screened candidates, indicating MolGridCal program can give reasonable results. To further study the binding mode and refine the results of MolGridCal, more accurate docking and scoring methods are used to estimate the binding affinity for the top three molecules (agonist BI-167107, neutral antagonist alprenolol and inverse agonist ICI 118,551). The results indicate agonist BI-167107 has the best binding affinity. MD simulation and free energy calculation are employed to investigate the dynamic interaction mechanism between the ligands and β2AR. The results show that the agonist BI-167107 also has the lowest binding free energy. This study can provide a new way to perform virtual screening effectively through integrating molecular docking based on grid computing, MD simulations and free energy calculations. The source codes of MolGridCal are freely available at http://molgridcal.codeplex.com.

Structure-based simulations reveal concerted dynamics of GPCR activation

G protein-coupled receptors (GPCRs) are a vital class of proteins that transduce biological signals across the cell membrane. However, their allosteric activation mechanism is not fully understood; crystal structures of active and inactive receptors have been reported, but the functional pathway between these two states remains elusive. Here, we use structure-based (Gō-like) models to simulate activation of two GPCRs, rhodopsin and the β₂ adrenergic receptor (β₂AR). We used data-derived reaction coordinates that capture the activation mechanism for both proteins, showing that activation proceeds through quantitatively different paths in the two systems. Both reaction coordinates are determined from the dominant concerted motions in the simulations so the technique is broadly applicable. There were two surprising results. First, the main structural changes in the simulations were distributed throughout the transmembrane bundle, and not localized to the obvious areas of interest, such as the intracellular portion of Helix 6. Second, the activation (and deactivation) paths were distinctly nonmonotonic, populating states that were not simply interpolations between the inactive and active structures. These transitions also suggest a functional explanation for β₂AR's basal activity: it can proceed through a more broadly defined path during the observed transitions.

Agouti signalling protein is an inverse agonist to the wildtype and agonist to the melanic variant of the melanocortin-1 receptor in the grey squirrel (Sciurus carolinensis)

The melanocortin-1 receptor (MC1R) is a key regulator of mammalian pigmentation. Melanism in the grey squirrel is associated with an eight amino acid deletion in the mutant melanocortin-1 receptor with 24 base pair deletion (MC1RΔ24) variant. We demonstrate that the MC1RΔ24 exhibits a higher basal activity than the wildtype MC1R (MC1R-wt). We demonstrate that agouti signalling protein (ASIP) is an inverse agonist to the MC1R-wt but is an agonist to the MC1RΔ24. We conclude that the deletion in the MC1RΔ24 leads to a receptor with a high basal activity which is further activated by ASIP. This is the first report of ASIP acting as an agonist to MC1R.