Biology subjects

Provasi, D.

Publications and source records attributed to Provasi, D..

13 recordsLinked to original sources

bioRxiv2026

Transition Intermediates Encode Ligand Efficacy at the μ-Opioid Receptor-Gi Protein Complex

How ligands with different efficacies regulate G protein-coupled receptor signaling remains incompletely understood. Here, we combine Markov state modeling of nearly one millisecond of aggregate all-atom molecular dynamics simulations with time-resolved cryogenic-electron microscopy (cryo-EM) to reconstruct the most probable transition pathways connecting recently resolved structures of guanosine triphosphate (GTP)-bound -opioid receptor (MOR)-Gi1 complexes with the full agonist lofentanil (LFT) or partial agonist mitragynine pseudoindoxyl (MP). The models predict four previously unresolved intermediate conformations. Guided by these predictions, reanalysis of the cryo-EM particle ensemble identifies conformational populations consistent with all four intermediates. Remarkably, the largest ligand-dependent structural differences emerge within these intermediates rather than in the previously resolved states. LFT favors a predominantly sequential and kinetically efficient transition pathway, whereas MP stabilizes a broader intermediate ensemble along this pathway and slows progression toward later states. Together, these complementary approaches provide an enriched thermodynamic and kinetic description of MOR-mediated G protein activation, revealing that opioid efficacy is encoded, at least in part, in the differential stabilization and kinetics of transition intermediates extending beyond GTP binding but well before complete G protein dissociation.

biophysics↗

bioRxiv2025

Structural and dynamic studies uncover a distinct allosteric modulatory site at the mu-opioid receptor

Positive allosteric modulators (PAMs) of the opioid receptor (MOR) offer a promising path toward safer opioid therapeutics, yet their mechanisms of action remain poorly understood. Here, we uncover the structural and mechanistic basis of BMS-986187, a chemically distinct MOR PAM with in vivo efficacy, using an integrated approach combining cryogenic electron microscopy (cryo-EM), molecular dynamics (MD) simulations, signaling assays, and site-directed mutagenesis. We identify a previously uncharacterized allosteric site for BMS-986187, a lipid-facing pocket formed by MOR transmembrane helices 2, 3, and 4, distinct from sites occupied by other known MOR PAMs or negative allosteric modulators. BMS-986187 engages both receptor residues and a neighboring cholesterol molecule, suggesting a cooperative ligand-lipid mechanism. Our studies pinpoint residues essential for allosteric modulation, while information-theory analysis of MD trajectories uncovers specific allosteric communication pathways linking the PAM site to both the orthosteric agonist DAMGO and the G protein interface. Together, these findings redefine the landscape of MOR allosteric modulation by revealing a novel binding site, a potentially lipid-sensitive allosteric mechanism, and the molecular wiring of long-range communication within MOR. This work provides a new molecular framework for the rational design of PAMs targeting opioid receptors with improved precision and possible therapeutic potential.

pharmacology and toxicology↗

bioRxiv2025

BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects Across Class A GPCRs

G protein-coupled receptors (GPCRs) are important targets for drug discovery owing to their ability to respond to a broad range of stimuli and their involvement in numerous pathologies. Although traditional ligand-based and structure-based approaches have facilitated the development of effective therapeutics for many GPCRs, these approaches often fall short when applied to receptors with limited ligand or structural data. This limitation highlights the critical need for advanced strategies capable of accurately predicting ligand bioactivity across the entire GPCR family, especially for understudied receptor subtypes. In this study, we introduce BOLD-GPCRs (BERT-Optimized Ligand Discovery for GPCRs), a deep learning framework designed to enhance the prediction of ligand bioactivity across class A GPCRs. Accessible via a user-friendly web interface, BOLD-GPCRs employs transfer learning and leverages curated datasets of known class A GPCR ligands, receptor sequences, and signaling-relevant mutations. By integrating dense neural network classifiers with transformer-based protein language models, BOLD-GPCRs captures complex relationships between receptor sequence/function and ligand activity. Our results demonstrate that BOLD-GPCRs achieves robust predictive performance for both ligand bioactivity and mutational effects across a broad range of class A GPCRs, underscoring its potential as a valuable tool for ligand discovery, especially for poorly characterized receptors. For Table of Contents Use OnlyBOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects Across Class A GPCRs Davide Provasi, Kirill Konovalov, Nicholas Riina, Olivia Cullen, and Marta Filizola O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/668547v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@2026bborg.highwire.dtl.DTLVardef@cec535org.highwire.dtl.DTLVardef@f52438org.highwire.dtl.DTLVardef@1cfbe6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) remain a focal point of research due to their critical roles in cell signaling and their prominence as drug targets. However, directly linking drug efficacy to receptor-mediated activation of specific intracellular transducers and the resulting physiological outcomes remains challenging. It is unclear whether the enhanced therapeutic window of certain drugs -- defined as the dose range that provides effective therapy with minimal side effects -- stems from their low intrinsic efficacy across all signaling pathways or ligand bias, wherein specific transducer subtypes are preferentially activated in a given cellular system compared to a reference ligand. Accurately predicting safer compounds, whether through low intrinsic efficacy or ligand bias, would greatly advance drug development. While AI models hold promise for such predictions, the development of deep learning models capable of reliably forecasting GPCR ligands with defined bioactivities remains challenging, largely due to the limited availability of high-quality data. To address this, we pre-trained a model on receptor sequences and ligand datasets across all class A GPCRs, and then refined it to predict low-efficacy compounds or biased agonists for individual class A GPCRs. This was achieved using transfer learning and a neural network incorporating natural language processing of target sequences and receptor mutation effects on signaling. These two fine-tuned models--one for low-efficacy agonists and one for biased agonists--are available on demand for each class A GPCR and enable virtual screening of large chemical libraries, thereby facilitating the discovery of compounds with potentially improved safety profiles.

bioinformatics↗

bioRxiv2024

Structural basis for saxitoxin congener binding and neutralization by anuran saxiphilins

Dinoflagellates and cyanobacteria in harmful red tide algal blooms produce saxitoxin (STX) and [~]50 congeners that block voltage-gated sodium channel (NaV) function and disrupt bioelectrical signals1-4. Consuming seafood carrying these lethal toxins causes paralytic shellfish poisoning (PSP), a growing public health hazard due to climate change5-7 that motivates efforts to detect these toxins and counteract their noxious effects. Although structural studies of NaVs8,9 and anuran soluble STX binding proteins known as saxiphilins (Sxphs)10,11 revealed convergent binding modes for the bis-guanidinium STX core10,11, the structural basis for STX congener recognition is unknown. Here, we show that American bullfrog (Rana catesbeiana) RcSxph10,11 and High Himalaya frog (Nanorana parkeri) NpSxph10 use a pre-organized pocket to sequester STX congeners through a binding mode shared with STX. This lock and key recognition yields a tradeoff between a relatively rigid high-affinity toxin binding site in which bound waters are crucial and the ability of Sxphs to accommodate STX congener modifications. Importantly, functional studies show that Sxphs act as toxin sponges that reverse NaV block by multiple STX congeners and can detect these bis-guanidinium toxins in a radioligand receptor binding assay (RBA) for PSP toxin environmental testing12,13. Our findings establish how Sxphs sequester diverse neurotoxins and reveal structural factors underlying STX congener binding differences between Sxphs and NaVs that are rooted in the distinct toxin binding orientations on these two targets. These insights expand the molecular foundation required for understanding toxin sponge action and for guiding development of new means to monitor PSTs and mitigate their harmful effects.

biophysics↗

bioRxiv2023

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-HT1eRs therapeutic potential and plausible druggability, the mechanisms of its activation remain elusive. To illuminate 5-HT1eRs 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.

neuroscience↗

bioRxiv2023

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.

bioinformatics↗

bioRxiv2023

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 {kappa}-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.

bioinformatics↗

bioRxiv2022

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 {beta}2-adrenergic receptor ({beta}2AR)-Gs complex after GDP release and GTP binding. Our data suggest rapid GTP binding and GTP-induced dissociation of Gs from {beta}2AR and G{beta}{gamma}, as opposed to a slow closing of the Gs -helical domain (AHD). Yeast-two-hybrid screening using Gs 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 Gs AHD.

molecular biology↗

bioRxiv2021

A novel mitragynine analog with low efficacy mu-opioid receptor agonism displays antinociception with attenuated adverse effects

Dried kratom leaves are anecdotally used for the treatment of pain, opioid dependence, and alcohol use disorder. We have previously shown that kratoms natural products (mitragynine) and semi-synthetic analogs (7-hydroxy mitragynine (7OH) and mitragynine pseudoindoxyl) are mu opioid receptor (MOR) agonists that show minimal {beta}-arrestin2 recruitment. To further investigate the structure activity relationships of G-protein potency, efficacy, and {beta}-arrestin2 recruitment, we diversified the mitragynine/7OH templates at the C9, -10 and -12 positions of the aromatic ring of the indole moiety. Three lead C9 analogs, synthesized by swapping the 9-methoxy group with varied substituents, namely phenyl (SC11), methyl (SC12), 3-furanyl (SC13), were further characterized using a panel of in vitro and ex vivo electrophysiology assays. All three compounds were partial agonists with lower efficacy than both DAMGO and morphine in heterologous G-protein assays and synaptic physiology. SC11-13 also showed lower recruitment of both {beta}-arrestin subtypes compared to DAMGO, and in assays with limited MOR receptor reserve, the G-protein efficacy of SC11, SC12 and SC13 was comparable to buprenorphine. In mouse models, at equianalgesic doses SC13 showed MOR-dependent analgesia with potency similar to morphine without respiratory depression, hyperlocomotion, constipation, or place conditioning. Taken together, these results suggest that MOR agonists with a G-protein efficacy profile similar to buprenorphine can be developed into opioids that are effective analgesics with greatly reduced liabilities.

pharmacology and toxicology↗

bioRxiv2021

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. While 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 informs a working model of inter-TMD rearrangement that drives mGluR function.

neuroscience↗

bioRxiv2019

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 {micro}-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. While 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. Analyses of these simulations shed light on (a) the preferred binding sites of Mg2+ on the MOP receptor, (b) details of the competition between Mg2+ and Na+ cations for specific sites, (c) estimates of binding affinities, and (d) testable hypotheses of the molecular mechanism underlying the positive allosteric modulation of the MOP receptor by the Mg2+ cation.\n\nStatement of SignificanceOverprescription of opioid drugs in the late 1990s, followed by abuse of both prescription and illicit opioid drugs, has led to what is nowadays called \"opioid overdose crisis\" or \"opioid epidemic\", with more than 130 Americans dying daily from opioid overdose at the time of writing. To reduce opioid doses, and thereby prevent or treat overdose and opioid use disorders, attention has recently shifted to the use of co-analgesics. Understanding how opioid receptor targets can be allosterically modulated by these elements, including cations, is key to the development of improved therapeutics. Here, we provide an atomic-level understanding of the mechanism by which magnesium binds to the {micro}-opioid receptor and enhances opioid drug efficacy by stabilizing the receptor activated state.

biophysics↗

bioRxiv2018

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 (CV). 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 -opioid receptor (MOR), and assessed the ability of this enhanced sampling method to estimate thermodynamic properties of receptor activation in line with those obtained by more computationally expensive adaptive sampling protocols. While n-body information theory (nBIT) 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 (MSMs) 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 thermodynamic and kinetic properties of GPCR activation at an affordable computational cost.

biophysics↗