Biology subjects

Stavitskaya, L.

Publications and source records attributed to Stavitskaya, L..

3 recordsLinked to original sources

bioRxiv2024

Computational Analysis of the Binding Poses of Nitazene Derivatives at the mu-Opioid Receptor

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of {micro}OR-opioid receptor ({micro}OR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the {micro}OR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of {micro}OR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for {micro}OR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln1242.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a{pi} -hole interaction with the conserved Tyr751.39. Our computational analysis provides new insights into the mechanism of {micro}OR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/616560v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@f3a933org.highwire.dtl.DTLVardef@e98f41org.highwire.dtl.DTLVardef@1bc0962org.highwire.dtl.DTLVardef@c416b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor

Opioids are small-molecule agonists of {micro}-opioid receptor ({micro}OR), while reversal agents such as naloxone are antagonists of {micro}OR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human {micro}OR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured Emax values at the human {micro}OR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 {+/-} 3.9% and 91.8 {+/-} 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat {micro}OR,{kappa} OR, or{delta} OR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of {micro}OR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/588485v2_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1bf13f7org.highwire.dtl.DTLVardef@1b7f04aorg.highwire.dtl.DTLVardef@1009569org.highwire.dtl.DTLVardef@1515139_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning

The nations opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is a FDA-approved reversal agent, antagonizes opioids through competitive binding at the -opioid receptor (mOR). Thus, knowledge of opioids residence time is important for assessing the effectiveness of naloxone. Here we estimated the residence times of 15 fentanyl and 4 morphine analogs using metadynamics, and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann, Li et al, Clin. Pharmacol. Therapeut. 2022). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning (ML) approach to analyze the kinetic impact of fentanyls substituents based on the interactions with mOR residues. This proof-of-concept approach is general; for example, it may be used to tune ligand residence times in computeraided drug discovery. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/531338v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@15e4093org.highwire.dtl.DTLVardef@e17398org.highwire.dtl.DTLVardef@16c2aaaorg.highwire.dtl.DTLVardef@6527a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗