Biology subjects

May, L. T.

Publications and source records attributed to May, L. T..

4 recordsLinked to original sources

bioRxiv2026

Bioactivity foundation model for hit identification and hit-to-lead optimization from sequence data

Learning from large-scale bioactivity archives is complicated by heterogeneous assay readouts and differences in data reliability. Here we show that a model that predicts probability distributions over binding affinities from sequence data opens model training to vast and diverse troves of heterogeneous bioactivity data. We develop PSICHIC+, a bioactivity foundation model trained on approximately 82 million protein-ligand records using a distributional objective that preserves quantitative resolution while incorporating variation in data reliability. Given a protein sequence and ligand SMILES, PSICHIC+ predicts binding affinity in milliseconds. In zero-shot virtual-screening benchmarks without target-specific training data, it improves enrichment over regression- and classification-based approaches and performs competitively with leading structure-based methods. In lab-in-the-loop campaigns, distribution-guided acquisition improves recovery of high-activity compounds under fixed screening budgets. Combining PSICHIC+ with Boltz-2 improves hit-to-lead affinity prediction over Boltz-2 alone, with mean correlation approaching that of FEP+ on the evaluated benchmarks, without requiring high-resolution protein structures. These results support distributional learning as a scalable approach to using heterogeneous bioactivity data for hit identification and hit-to-lead optimization.

bioinformatics↗

bioRxiv2025

Molecular basis of ligand binding and receptor activation at the human A3 adenosine receptor

Adenosine receptors (ARs: A1AR, A2AAR, A2BAR, and A3AR) are crucial therapeutic targets, yet developing selective, efficacious drugs remains challenging. Here, we present high-resolution cryo-electron microscopy (cryo-EM) structures of the human A3AR in three distinct functional states: bound to the endogenous agonist adenosine, the clinically relevant agonist Piclidenoson, and the covalent antagonist LUF7602. These structures, complemented by mutagenesis and pharmacological studies, reveal a unique A3AR activation mechanism involving an extensive hydrogen bond network from the extracellular surface down to the orthosteric binding site. In addition, we identify a cryptic pocket that accommodates the N6-iodobenzyl group of Piclidenoson through a ligand-dependent conformational change of M1745.35. Our comprehensive structural and functional characterization of A3AR advances understanding of adenosine receptor pharmacology and establishes a foundation for developing more selective therapeutics for various disorders including inflammatory diseases, cancer, and glaucoma. TeaserStructures of the A3AR in different conformations reveal a unique activation mechanism and cryptic binding pocket.

pharmacology and toxicology↗

bioRxiv2023

PSICHIC: physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data

In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein-ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. We introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework uniquely incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain first-of-its-kind emergent capabilities in deciphering mechanisms underlying protein-ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein-ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding affinity prediction. In a library screening for adenosine A1 receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHICs interpretable fingerprints identified protein residues and ligand atoms involved in interactions. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein-ligand interactions.

bioinformatics↗

bioRxiv2020

Pharmacological Insights into Safety and Efficacy Determinants for the Development of Adenosine Receptor Biased Agonists in the Treatment of Heart Failure

Adenosine A1 receptors (A1R) are a potential target for cardiac injury treatment due to their cardioprotective/antihypertrophic actions, but drug development has been hampered by on-target side effects such as bradycardia and altered renal haemodynamics. Biased agonism has emerged as an attractive mechanism for A1R-mediated cardioprotection that is haemodynamically safe. Here we investigate the pre-clinical pharmacology, efficacy and side-effect profile of the A1R agonist neladenoson, shown to be safe but ineffective in phase IIb trials for the treatment of heart failure. We compare this agent with the well-characterised, pan-adenosine receptor (AR) agonist NECA, capadenoson, and the A1R biased agonist VCP746, previously shown to be safe and cardioprotective in pre-clinical models of heart failure. We show that like VCP746, neladenoson is biased away from Ca2+ influx relative to NECA and the cAMP pathway at the A1R, a profile predictive of a lack of adenosine-like side effects. Additionally, neladenoson was also biased away from the MAPK pathway at the A1R. In contrast to VCP746, which displays more adenosine-like signalling at the A2BR, neladenoson was a highly selective A1R agonist, with biased, weak agonism at the A2BR. Together these results show that unwanted haemodynamic effects of A1R agonists can be avoided by compounds biased away from Ca2+ influx relative to cAMP, relative to NECA. The failure of neladenoson to reach primary endpoints in clinical trials suggests that A1R-mediated cAMP inhibition may be a poor indicator of effectiveness in chronic heart failure. This study provides additional information that can aid future screening and/or design of improved AR agonists that are safe and efficacious in treating heart failure in patients. ONE-SENTENCE SUMMARYBiased agonists that preference against calcium influx relative to the cyclic AMP pathway, when compared to a conventional agonist, confer clinical safety to A1 adenosine receptor ligands.

pharmacology and toxicology↗