Abstract
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 ONLY: BOLD-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.