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.

Burkholderia cenocepacia epigenetic regulator M.BceJIV simultaneously engages two DNA recognition sequences for methylation.

Burkholderia cenocepacia is an opportunistic and infective bacterium containing an orphan DNA methyltransferase (M.BceJIV) with roles in regulating gene expression and motility of the bacterium. M.BceJIV recognizes a GTWWAC motif (where W can be an adenine or a thymine) and methylates the N6 of the adenine at the fifth base position (GTWWAC). Here, we present a high-resolution crystal structure of M.BceJIV/DNA/sinefungin ternary complex and allied biochemical, computational, and thermodynamic analyses. Remarkably, the structure shows not one, but two DNA substrates bound to the M.BceJIV dimer, wherein each monomer contributes to the recognition of two recognition sequences. This unexpected mode of DNA binding and methylation has not been observed previously and sets a new precedent for a DNA methyltransferase. We also show that methylation at two recognition sequences occurs independently, and that GTWWAC motifs are enriched in intergenic regions of a strain of B. cenocepacia's genome. We further computationally assess the interactions underlying the affinities of different ligands (SAM, SAH, and sinefungin) for M.BceJIV, as a step towards developing selective inhibitors for limiting B. cenocepacia infection.

Structural basis for partial agonism in 5-HT3A receptors.

Hyperactivity of serotonin 3 receptors (5-HT3R) underlies pathologies associated with irritable bowel syndrome and chemotherapy-induced nausea and vomiting. Setrons, a class of high-affinity competitive antagonists, are used in the treatment of these conditions. Although generally effective for chemotherapy-induced nausea and vomiting, the use of setrons for treating irritable bowel syndrome has been impaired by adverse side effects. Partial agonists are now being considered as an alternative strategy, with potentially less severe side effects than full antagonists. However, a structural understanding of how these ligands work is lacking. Here, we present high-resolution cryogenic electron microscopy structures of the mouse 5-HT3AR in complex with partial agonists (SMP-100 and ALB-148471) captured in pre-activated and open-like conformational states. Molecular dynamics simulations were used to assess the stability of drug-binding poses and interactions with the receptor over time. Together, these studies reveal mechanisms for the functional differences between orthosteric partial agonists, full agonists and antagonists of the 5-HT3AR.

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.

Deciphering downstream receptor signaling.

Advancing drug discovery requires increasingly integrative structural biology approaches.

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.

Structural basis of allosteric modulation of metabotropic glutamate receptor activation and desensitization.

The metabotropic glutamate receptors (mGluRs) are neuromodulatory family C G protein coupled receptors which assemble as dimers and allosterically couple extracellular ligand binding domains (LBDs) to transmembrane domains (TMDs) to drive intracellular signaling. Pharmacologically, mGluRs can be targeted either at the LBDs by glutamate and synthetic orthosteric compounds or at the TMDs by allosteric modulators. Despite the potential of allosteric TMD-targeting compounds as therapeutics, an understanding of the functional and structural basis of their effects on mGluRs is limited. Here we use a battery of approaches to dissect the distinct functional and structural effects of orthosteric versus allosteric ligands. We find using electrophysiological and live cell imaging assays that both agonists and positive allosteric modulators (PAMs) can drive activation and desensitization of mGluRs. The effects of PAMs are pleiotropic, including both the ability to boost the maximal response to orthosteric agonists and to serve independently as desensitization-biased agonists across mGluR subtypes. Conformational sensors reveal PAM-driven inter-subunit re-arrangements at both the LBD and TMD. Motivated by this, we determine cryo-electron microscopy structures of mGluR3 in the presence of either an agonist or antagonist alone or in combination with a PAM. These structures reveal PAM-driven re-shaping of intra- and inter-subunit conformations and provide evidence for a rolling TMD dimer interface activation pathway that controls G protein and beta-arrestin coupling. Highlights: -Agonists and PAMs drive mGluR activation, desensitization, and endocytosis-PAMs are desensitization-biased and synergistic with agonists-Four combinatorial ligand conditions reveal an ensemble of full-length mGluR structures with novel interfaces-Activation and desensitization involve rolling TMD interfaces which are re-shaped by PAM.

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.

Implementation of an Online Reporting System to Identify Unprofessional Behaviors and Mistreatment Directed at Trainees at an Academic Medical Center.

Importance: Unprofessional behaviors and mistreatment directed at trainees continue to challenge the learning environment. Academic medical institutions should encourage reports of inappropriate behavior and address such reports directly to create a safe learning environment. Objective: To determine the feasibility of creating and implementing an online reporting system for receiving and reviewing complaints of unprofessional behavior directed toward or experienced by students, postdoctoral trainees, and residents. Design, Setting, and Participants: This cohort study assessed implementation of an online reporting system (feedback form) with a method for triaging reports, providing both positive and negative feedback, as well as adjudication and transparent public disclosure of aggregate data. The system was launched at a large urban academic medical center with numerous trainees that is fully integrated with a health system of 8 hospitals. Participants included faculty who interact with trainees, medical students, graduate students and postdoctoral fellows, and residents and clinical fellows. Follow-up began in October 2019 (at the time of tool launch) and lasted through December 2021. Data were analyzed from January to March 2022. Main Outcomes and Measures: The primary outcomes were the numbers and types of reports according to the reporter and the person reported about. Results: Participants included 2900 faculty who interact with trainees, 600 medical students, more than 1000 graduate students and postdoctoral fellows, and 2600 residents and clinical fellows. Trainees submitted 196 reports, 173 (88.3%) of which described unprofessional interactions. Among the reports describing unprofessional behavior, 60 (34.7%) were from medical students, 96 (55.5%) were from residents and fellows, 17 (9.8%) were from graduate students or postdoctoral trainees, and 78 (45.1%) were from men. The majority of negative reports described behaviors by faculty (106 [61.3%]), followed by residents and fellows (24 [13.9%]). Twenty faculty (<1.0%) accounted for 52 (50.0%) of the 104 reports describing unprofessional behaviors. Since implementation, most trainees are aware of this process. An increasing number have reported instances of mistreatment, and those who shared concerns through the online system report satisfaction with the outcome of the response to the report. Conclusions and Relevance: In this cohort study, the new reporting mechanism facilitated identification of the small number of individuals associated with unprofessional behaviors toward trainees and increased awareness of the school's commitment to creating a safe learning environment.

Structure-Based Discovery of a Novel Class of Small-Molecule Pure Antagonists of Integrin αVß3.

Inhibitors of integrin αVß3 have therapeutic promise for a variety of diseases. Most αVß3-targeting small molecules patterned after the RGD motif are partial agonists because they induce a high-affinity, ligand-binding conformation and prime the receptor to bind the ligand without an activating stimulus, in part via a charge-charge interaction between their aspartic acid carboxyl group and the metal ion in the metal-ion-dependent adhesion site (MIDAS). Building upon our previous studies on the related integrin αIIbß3, we searched for pure αVß3 antagonists that lack this typical aspartic acid carboxyl group and instead engage through direct binding to one of the coordinating residues of the MIDAS metal ion, specifically ß3 E220. By in silico screening of two large chemical libraries for compounds interacting with ß3 E220, we indeed discovered a novel molecule that does not contain an acidic carboxyl group and does not induce the high-affinity, ligand-binding state of the receptor. Functional and structural characterization of a chemically optimized version of this compound led to the discovery of a novel small-molecule pure αVß3 antagonist that (i) does not prime the receptor to bind the ligand and does not induce hybrid domain swing-out or receptor extension as judged by antibody binding and negative-stain electron microscopy, (ii) binds at the RGD-binding site as predicted by metadynamics rescoring of induced-fit docking poses and confirmed by a cryo-electron microscopy structure of the compound-bound integrin, and (iii) coordinates the MIDAS metal ion via a quinoline moiety instead of an acidic carboxyl group.

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.

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.

SOX11 Inhibitors Are Cytotoxic in Mantle Cell Lymphoma.

PURPOSE: Mantle cell lymphoma (MCL) is a fatal subtype of non-Hodgkin lymphoma. SOX11 transcription factor is overexpressed in the majority of nodal MCL. We have previously reported that B cell-specific overexpression of SOX11 promotes MCL pathogenesis via critically increasing BCR signaling in vivo. SOX11 is an attractive target for MCL therapy; however, no small-molecule inhibitor of SOX11 has been identified to date. Although transcription factors are generally considered undruggable, the ability of SOX11 to bind to the minor groove of DNA led us to hypothesize that there may exist cavities at the protein-DNA interface that are amenable to targeting by small molecules. EXPERIMENTAL DESIGN: Using a combination of in silico predictions and experimental validations, we report here the discovery of three structurally related compounds (SOX11i) that bind SOX11, perturb its interaction with DNA, and effect SOX11-specific anti-MCL cytotoxicity. RESULTS: We find mechanistic validation of on-target activity of these SOX11i in the inhibition of BCR signaling and the transcriptional modulation of SOX11 target genes, specifically, in SOX11-expressing MCL cells. One of the three SOX11i exhibits relatively superior in vitro activity and displays cytotoxic synergy with ibrutinib in SOX11-expressing MCL cells. Importantly, this SOX11i induces cytotoxicity specifically in SOX11-positive ibrutinib-resistant MCL patient samples and inhibits Bruton tyrosine kinase phosphorylation in a xenograft mouse model derived from one of these subjects. CONCLUSIONS: Taken together, our results provide a foundation for therapeutically targeting SOX11 in MCL by a novel class of small molecules.

A promising chemical series of positive allosteric modulators of the µ-opioid receptor that enhance the antinociceptive efficacy of opioids but not their adverse effects.

Positive allosteric modulators (PAMs) of the µ-opioid receptor (MOR) have been proposed to exhibit therapeutic potential by maximizing the analgesic properties of clinically used opioid drugs while limiting their adverse effects or risk of overdose as a result of using lower drug doses. We herein report in vitro and in vivo characterization of two small molecules from a chemical series of MOR PAMs that exhibit: (i) MOR PAM activity and receptor subtype selectivity in vitro, (ii) a differential potentiation of the antinociceptive effect of oxycodone, morphine, and methadone in mouse models of pain that roughly correlates with in vitro activity, and (iii) a lack of potentiation of adverse effects associated with opioid administration, such as somatic withdrawal, respiratory depression, and analgesic tolerance. This series of MOR PAMs holds promise for the development of adjuncts to opioid therapy to mitigate against overdose and opioid use disorders.

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.

Electron microscopy shows that binding of monoclonal antibody PT25-2 primes integrin αIIbß3 for ligand binding.

The murine monoclonal antibody (mAb) PT25-2 induces αIIbß3 to bind ligand and initiate platelet aggregation. The underlying mechanism is unclear, because previous mutagenesis studies suggested that PT25-2 binds to the αIIb ß propeller, a site distant from the Arg-Gly-Asp-binding pocket. To elucidate the mechanism, we studied the αIIbß3-PT25-2 Fab complex by negative-stain and cryo-electron microscopy (EM). We found that PT25-2 binding results in αIIbß3 partially exposing multiple ligand-induced binding site epitopes and adopting extended conformations without swing-out of the ß3 hybrid domain. The cryo-EM structure showed PT25-2 binding to the αIIb residues identified by mutagenesis but also to 2 additional regions. Overlay of the cryo-EM structure with the bent αIIbß3 crystal structure showed that binding of PT25-2 creates clashes with the αIIb calf-1/calf-2 domains, suggesting that PT25-2 selectively binds to partially or fully extended receptor conformations and prevents a return to its bent conformation. Kinetic studies of the binding of PT25-2 compared with mAbs 10E5 and 7E3 support this hypothesis. We conclude that PT25-2 induces αIIbß3 ligand binding by binding to extended conformations and by preventing the interactions between the αIIb and ß3 leg domains and subsequently the ßI and ß3 leg domains required for the bent-closed conformation.