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Konovalov, K.

Publications and source records attributed to Konovalov, K..

5 recordsLinked to original sources

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↗

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↗

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↗

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↗

Convergent adaptation in mitochondria of phylogenetically distant birds: does it exist?

In a wide range of taxa, proteins encoded by mitochondrial genomes are involved in adaptation to lifestyle that requires oxygen starvation or elevation of metabolism rate. It remains poorly understood to what extent adaptation to similar conditions is associated with parallel changes in these proteins. We search for genetic signal of parallel or convergent evolution in recurrent molecular adaptation to high altitude, migration, diving, wintering, unusual flight abilities, or loss of flight in mitochondrial genomes of birds. Developing on previous work, we design an approach for detection of recurrent coincident changes in genotype and phenotype, indicative of an association between the two. We describe a number of candidate sites involved in recurrent adaptation in ND genes. However, we find that the majority of convergence events can be explained by random coincidences without invoking adaptation.

bioinformatics↗