Biology subjects

Shivnaraine, R. V.

Publications and source records attributed to Shivnaraine, R. V..

3 recordsLinked to original sources

bioRxiv2025

Ligand-dependent G protein dynamics underlying opioid signaling efficacy

Activation of heterotrimeric G proteins by G protein-coupled receptors (GPCRs) requires large-scale opening of the G -helical domain (AHD) to expose the nucleotide-binding site and facilitate GDP-GTP exchange. While orthosteric ligands are known to modulate GPCR conformation and signaling efficacy, how these effects propagate to the G protein itself remains unclear. Using single-molecule fluorescence resonance energy transfer (smFRET) imaging, we monitored AHD motions in Gi proteins coupled to the -opioid receptor (OR) across a spectrum of ligand- and nucleotide-bound states. We find that receptor ligands differentially modulate these dynamics from over 70 [A] away, with higher-efficacy agonists more effectively promoting transitions to an open, low-nucleotide-affinity conformation. These data also capture transient OR-Gi intermediates during nucleotide binding and suggest that -opioid ligand efficacy arises in part from allosteric control over G protein conformational equilibria that kinetically gate activation.

biophysics↗

bioRxiv2025

Membrane phosphoinositides allosterically tune β-arrestin dynamics to facilitate GPCR core engagement

Arrestin proteins bind active G protein-coupled receptors (GPCRs) through coordinated protein-protein, protein-phosphate, and protein-lipid interactions to attenuate G protein signaling and promote GPCR internalization and trafficking. While there are hundreds of diverse GPCRs, only two {beta}-arrestin isoforms ({beta}arrs) must recognize and engage this wide range of receptors with varied phosphorylation patterns. Traditional models suggest that {beta}arr activation requires displacement of its autoinhibitory C-tail by a phosphorylated GPCR C-terminus; however, this paradigm fails to explain how minimally phosphorylated GPCRs still complex with {beta}arrs. Using single-molecule Forster resonance energy transfer imaging and hydrogen-deuterium exchange mass spectrometry, we observe basal dynamics in which the {beta}arr1 C-tail spontaneously releases from the N-domain, transiently adopting an active conformation that can facilitate binding of the phosphorylated GPCR C-terminus. We further demonstrate the importance of an intermediate state of {beta}arr1 arising from spontaneous C-tail release stabilized by the membrane phosphoinositide PI(4,5)P2. Both PI(4,5)P2 and mutations in the proximal or middle regions of the C-tail shift {beta}arr1 towards a partially released state, revealing an allosteric connection that informs a refined model for {beta}arr activation. In this model, membrane engagement conformationally primes {beta}arrs prior to receptor binding, thereby explaining how {beta}arrs are recruited by diverse GPCRs, even those with limited C-terminal phosphorylation.

biophysics↗

bioRxiv2023

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries

SummaryThe emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Benchmark Group leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 hours. Availability and ImplementationThe ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418.

bioinformatics↗