Structural insights into the unexpected agonism of tetracyclic antidepressants at serotonin receptors 5-HT1eR and 5-HT1FR.

Serotonin [5-hydroxytryptamine (5-HT)] acts via 13 different receptors in humans. Of these receptor subtypes, all but 5-HT1eR have confirmed roles in native tissue and are validated drug targets. Despite 5-HT1eR's therapeutic potential and plausible druggability, the mechanisms of its activation remain elusive. To illuminate 5-HT1eR's pharmacology in relation to the highly homologous 5-HT1FR, we screened a library of aminergic receptor ligands at both receptors and observe 5-HT1eR/5-HT1FR agonism by multicyclic drugs described as pan-antagonists at 5-HT receptors. Potent agonism by tetracyclic antidepressants mianserin, setiptiline, and mirtazapine suggests a mechanism for their clinically observed antimigraine properties. Using cryo-EM and mutagenesis studies, we uncover and characterize unique agonist-like binding poses of mianserin and setiptiline at 5-HT1eR distinct from similar drug scaffolds in inactive-state 5-HTR structures. Together with computational studies, our data suggest that these binding poses alongside receptor-specific allosteric coupling in 5-HT1eR and 5-HT1FR contribute to the agonist activity of these antidepressants.

Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques.

The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, using these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning in building robust deep learning models to enhance ligand bioactivity prediction for each individual opioid receptor (OR) subtype. This is achieved by leveraging knowledge obtained from pretraining a model using supervised learning on a larger data set of bioactivity data combined with ligand-based and structure-based molecular descriptors related to the entire OR subfamily. Our studies hold the potential to advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics.

Structural Insights into the Unexpected Agonism of Tetracyclic Antidepressants at Serotonin Receptors 5-HT1eR and 5-HT1FR.

Serotonin (5-hydroxytryptamine, 5-HT) acts via 13 different receptors in humans. Of these receptor subtypes, all but 5-HT1eR have confirmed roles in native tissue and are validated drug targets. Despite 5-HT1eR's therapeutic potential and plausible druggability, the mechanisms of its activation remain elusive. To illuminate 5-HT1eR's pharmacology in relation to the highly homologous 5-HT1FR, we screened a library of aminergic receptor ligands at both receptors and observe 5-HT1e/1FR agonism by multicyclic drugs described as pan-antagonists at 5-HT receptors. Potent agonism by tetracyclic antidepressants mianserin, setiptiline, and mirtazapine suggests a mechanism for their clinically observed anti-migraine properties. Using cryoEM and mutagenesis studies, we uncover and characterize unique agonist-like binding poses of mianserin and setiptiline at 5-HT1eR distinct from similar drug scaffolds in inactive-state 5-HTR structures. Together with computational studies, our data suggest that these binding poses alongside receptor-specific allosteric coupling in 5-HT1eR and 5-HT1FR contribute to the agonist activity of these antidepressants.

Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques.

The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, utilizing these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning using combined ligand-based and structure-based molecular descriptors from the entire opioid receptor (OR) subfamily in building robust deep learning models for enhanced bioactivity prediction of opioid ligands at each individual OR subtype. Our studies hold the potential to greatly advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics.

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit significant safety concerns. Here, we used a generative deep-learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep Learning Framework.

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit significant safety concerns. Here, we used a generative deep learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.

Gαs slow conformational transition upon GTP binding and a novel Gαs regulator.

G proteins are major signaling partners for G protein-coupled receptors (GPCRs). Although stepwise structural changes during GPCR-G protein complex formation and guanosine diphosphate (GDP) release have been reported, no information is available with regard to guanosine triphosphate (GTP) binding. Here, we used a novel Bayesian integrative modeling framework that combines data from hydrogen-deuterium exchange mass spectrometry, tryptophan-induced fluorescence quenching, and metadynamics simulations to derive a kinetic model and atomic-level characterization of stepwise conformational changes incurred by the ß2-adrenergic receptor (ß2AR)-Gs complex after GDP release and GTP binding. Our data suggest rapid GTP binding and GTP-induced dissociation of Gαs from ß2AR and Gßγ, as opposed to a slow closing of the Gαs α-helical domain (AHD). Yeast-two-hybrid screening using Gαs AHD as bait identified melanoma-associated antigen D2 (MAGE D2) as a novel AHD-binding protein, which was also shown to accelerate the GTP-induced closing of the Gαs AHD.

A Novel Mitragynine Analog with Low-Efficacy Mu Opioid Receptor Agonism Displays Antinociception with Attenuated Adverse Effects.

Mitragynine and 7-hydroxymitragynine (7OH) are the major alkaloids mediating the biological actions of the psychoactive plant kratom. To investigate the structure-activity relationships of mitragynine/7OH templates, we diversified the aromatic ring of the indole at the C9, C10, and C12 positions and investigated their G-protein and arrestin signaling mediated by mu opioid receptors (MOR). Three synthesized lead C9 analogs replacing the 9-OCH3 group with phenyl (4), methyl (5), or 3'-furanyl [6 (SC13)] substituents demonstrated partial agonism with a lower efficacy than DAMGO or morphine in heterologous G-protein assays and synaptic physiology. In assays limiting MOR reserve, the G-protein efficacy of all three was comparable to buprenorphine. 6 (SC13) showed MOR-dependent analgesia with potency similar to morphine without respiratory depression, hyperlocomotion, constipation, or place conditioning in mice. These results suggest the possibility of activating MOR minimally (G-protein Emax ≈ 10%) in cell lines while yet attaining maximal antinociception in vivo with reduced opioid liabilities.

Differences in interactions between transmembrane domains tune the activation of metabotropic glutamate receptors.

The metabotropic glutamate receptors (mGluRs) form a family of neuromodulatory G-protein-coupled receptors that contain both a seven-helix transmembrane domain (TMD) and a large extracellular ligand-binding domain (LBD) which enables stable dimerization. Although numerous studies have revealed variability across subtypes in the initial activation steps at the level of LBD dimers, an understanding of inter-TMD interaction and rearrangement remains limited. Here, we use a combination of single molecule fluorescence, molecular dynamics, functional assays, and conformational sensors to reveal that distinct TMD assembly properties drive differences between mGluR subtypes. We uncover a variable region within transmembrane helix 4 (TM4) that contributes to homo- and heterodimerization in a subtype-specific manner and tunes orthosteric, allosteric, and basal activation. We also confirm a critical role for a conserved inter-TM6 interface in stabilizing the active state during orthosteric or allosteric activation. Together this study shows that inter-TMD assembly and dynamic rearrangement drive mGluR function with distinct properties between subtypes.

Predicted Mode of Binding to and Allosteric Modulation of the µ-Opioid Receptor by Kratom's Alkaloids with Reported Antinociception In Vivo.

Pain management devoid of serious opioid adverse effects is still far from reach despite vigorous research and development efforts. Alternatives to classical opioids have been sought for years, and mounting reports of individuals finding pain relief with kratom have recently intensified research on this natural product. Although the composition of kratom is complex, the pharmacological characterization of its most abundant alkaloids has drawn attention to three molecules in particular, owing to their demonstrated antinociceptive activity and limited side effects in vivo. These three molecules are mitragynine (MG), its oxidized active metabolite, 7-hydroxymitragynine (7OH), and the indole-to-spiropseudoindoxy rearrangement product of MG known as mitragynine pseudoindoxyl (MP). Although these three alkaloids have been shown to preferentially activate the G protein signaling pathway by binding and allosterically modulating the µ-opioid receptor (MOP), a molecular level understanding of this process is lacking and yet important for the design of improved therapeutics. The molecular dynamics study and experimental validation reported here provide an atomic level description of how MG, 7OH, and MP bind and allosterically modulate the MOP, which can eventually guide structure-based drug design of improved therapeutics.

A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.

Determining the drug-target residence time (RT) is of major interest in drug discovery given that this kinetic parameter often represents a better indicator of in vivo drug efficacy than binding affinity. However, obtaining drug-target unbinding rates poses significant challenges, both computationally and experimentally. This is particularly palpable for complex systems like G Protein-Coupled Receptors (GPCRs) whose ligand unbinding typically requires very long timescales oftentimes inaccessible by standard molecular dynamics simulations. Enhanced sampling methods offer a useful alternative, and their efficiency can be further improved by using machine learning tools to identify optimal reaction coordinates. Here, we test the combination of two machine learning techniques, automatic mutual information noise omission and reweighted autoencoded variational Bayes for enhanced sampling, with infrequent metadynamics to efficiently study the unbinding kinetics of two classical drugs with different RTs in a prototypic GPCR, the µ-opioid receptor. Dissociation rates derived from these computations are within one order of magnitude from experimental values. We also use the simulation data to uncover the dissociation mechanisms of these drugs, shedding light on the structures of rate-limiting transition states, which, alongside metastable poses, are difficult to obtain experimentally but important to visualize when designing drugs with a desired kinetic profile.

Atomic-Level Characterization of the Methadone-Stabilized Active Conformation of µ-Opioid Receptor.

Methadone is a synthetic opioid agonist with notoriously unique properties, such as lower abuse liability and induced relief of withdrawal symptoms and drug cravings, despite acting on the same opioid receptors triggered by classic opioids-in particular the µ-opioid receptor (MOR). Its distinct pharmacologic properties, which have recently been attributed to the preferential activation of ß-arrestin over G proteins, make methadone a standard-of-care maintenance medication for opioid addiction. Although a recent biophysical study suggests that methadone stabilizes different MOR active conformations from those stabilized by classic opioid drugs or G protein-biased agonists, how this drug modulates the conformational equilibrium of MOR and what specific active conformation of the receptor it stabilizes are unknown. Here, we report the results of submillisecond adaptive sampling molecular dynamics simulations of a predicted methadone-bound MOR complex and compare them with analogous data obtained for the classic opioid morphine and the G protein-biased ligand TRV130. The model, which is supported by existing experimental data, is analyzed using Markov state models and transfer entropy analysis to provide testable hypotheses of methadone-specific conformational dynamics and activation kinetics of MOR. SIGNIFICANCE STATEMENT: Opioid addiction has reached epidemic proportions in both industrialized and developing countries. Although methadone maintenance treatment represents an effective therapeutic approach for opioid addiction, it is not as widely used as needed. In this study, we contribute an atomic-level understanding of how methadone exerts its unique function in pursuit of more accessible treatments for opioid addiction. In particular, we present details of a methadone-specific active conformation of the µ-opioid receptor that has thus far eluded experimental structural characterization.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Cryo-Electron Microscopy Structure of the αIIbß3-Abciximab Complex.

OBJECTIVE: The αIIbß3 antagonist antiplatelet drug abciximab is the chimeric antigen-binding fragment comprising the variable regions of murine monoclonal antibody 7E3 and the constant domains of human IgG1 and light chain κ. Previous mutagenesis studies suggested that abciximab binds to the ß3 C177-C184 specificity-determining loop (SDL) and Trp129 on the adjacent ß1-α1 helix. These studies could not, however, assess whether 7E3 or abciximab prevents fibrinogen binding by steric interference, disruption of either the αIIbß3-binding pocket for fibrinogen or the ß3 SDL (which is not part of the binding pocket but affects fibrinogen binding), or some combination of these effects. To address this gap, we used cryo-electron microscopy to determine the structure of the αIIbß3-abciximab complex at 2.8 Å resolution. Approach and Results: The interacting surface of abciximab is comprised of residues from all 3 complementarity-determining regions of both the light and heavy chains, with high representation of aromatic residues. Binding is primarily to the ß3 SDL and neighboring residues, the ß1-α1 helix, and ß3 residues Ser211, Val212 and Met335. Unexpectedly, the structure also indicated several interactions with αIIb. As judged by the cryo-electron microscopy model, molecular-dynamics simulations, and mutagenesis, the binding of abciximab does not appear to rely on the interaction with the αIIb residues and does not result in disruption of the fibrinogen-binding pocket; it does, however, compress and reduce the flexibility of the SDL. CONCLUSIONS: We deduce that abciximab prevents ligand binding by steric interference, with a potential contribution via displacement of the SDL and limitation of the flexibility of the SDL residues.

Mechanism of µ-Opioid Receptor-Magnesium Interaction and Positive Allosteric Modulation.

In the era of opioid abuse epidemics, there is an increased demand for understanding how opioid receptors can be allosterically modulated to guide the development of more effective and safer opioid therapies. Among the modulators of the µ-opioid (MOP) receptor, which is the pharmacological target for the majority of clinically used opioid drugs, are monovalent and divalent cations. Specifically, the monovalent sodium cation (Na+) has been known for decades to affect MOP receptor signaling by reducing agonist binding, whereas the divalent magnesium cation (Mg2+) has been shown to have the opposite effect, notwithstanding the presence of sodium chloride. Although ultra-high-resolution opioid receptor crystal structures have revealed a specific Na+ binding site and molecular dynamics (MD) simulation studies have supported the idea that this monovalent ion reduces agonist binding by stabilizing the receptor inactive state, the putative binding site of Mg2+ on the MOP receptor, as well as the molecular determinants responsible for its positive allosteric modulation of the receptor, are unknown. In this work, we carried out tens of microseconds of all-atom MD simulations to investigate the simultaneous binding of Mg2+ and Na+ cations to inactive and active crystal structures of the MOP receptor embedded in an explicit lipid-water environment and confirmed adequate sampling of Mg2+ ion binding with a grand canonical Monte Carlo MD method. Analyses of these simulations shed light on 1) the preferred binding sites of Mg2+ on the MOP receptor, 2) details of the competition between Mg2+ and Na+ cations for specific sites, 3) estimates of binding affinities, and 4) testable hypotheses of the molecular mechanism underlying the positive allosteric modulation of the MOP receptor by the Mg2+ cation.

Ligand-Binding Calculations with Metadynamics.

All-atom molecular dynamics simulations can capture the dynamic degrees of freedom that characterize molecular recognition, the knowledge of which constitutes the cornerstone of rational approaches to drug design and optimization. In particular, enhanced sampling algorithms, such as metadynamics, are powerful tools to dramatically reduce the computational cost required for a mechanistic description of the binding process. Here, we describe the essential details characterizing these simulation strategies, focusing on the critical step of identifying suitable reaction coordinates, as well as on the different analysis algorithms to estimate binding affinity and residence times. We conclude with a survey of published applications that provides explicit examples of successful simulations for several targets.

Kinetic and thermodynamic insights into sodium ion translocation through the µ-opioid receptor from molecular dynamics and machine learning analysis.

The differential modulation of agonist and antagonist binding to opioid receptors (ORs) by sodium (Na+) has been known for decades. To shed light on the molecular determinants, thermodynamics, and kinetics of Na+ translocation through the µ-OR (MOR), we used a multi-ensemble Markov model framework combining equilibrium and non-equilibrium atomistic molecular dynamics simulations of Na+ binding to MOR active or inactive crystal structures embedded in an explicit lipid bilayer. We identify an energetically favorable, continuous ion pathway through the MOR active conformation only, and provide, for the first time: i) estimates of the energy differences and required timescales of Na+ translocation in inactive and active MORs, ii) estimates of Na+-induced changes to agonist binding validated by radioligand measurements, and iii) testable hypotheses of molecular determinants and correlated motions involved in this translocation, which are likely to play a key role in MOR signaling.

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.

Single Proteoliposome High-Content Analysis Reveals Differences in the Homo-Oligomerization of GPCRs.

G-protein-coupled receptors (GPCRs) control vital cellular signaling pathways. GPCR oligomerization is proposed to increase signaling diversity. However, many reports have arrived at disparate conclusions regarding the existence, stability, and stoichiometry of GPCR oligomers, partly because of cellular complexity and ensemble averaging of intrareconstitution heterogeneities that complicate the interpretation of oligomerization data. To overcome these limitations, we exploited fluorescence-microscopy-based high-content analysis of single proteoliposomes. This allowed multidimensional quantification of intrinsic monomer-monomer interactions of three class A GPCRs (ß2-adrenergic receptor, cannabinoid receptor type 1, and opsin). Using a billion-fold less protein than conventional assays, we quantified oligomer stoichiometries, association constants, and the influence of two ligands and membrane curvature on oligomerization, revealing key similarities and differences for three GPCRs with decidedly different physiological functions. The assays introduced here will assist with the quantitative experimental observation of oligomerization for transmembrane proteins in general.