Abstract

Gaining insight into the pharmacology of ligand engagement with G-protein coupled receptors (GPCRs) under biologically relevant conditions is vital to both drug discovery and basic research. NanoLuc-based bioluminescence resonance energy transfer (NanoBRET) monitoring competitive binding between fluorescent tracers and unmodified test compounds has emerged as a robust and sensitive method to quantify ligand engagement with specific GPCRs genetically fused to NanoLuc luciferase or the luminogenic HiBiT peptide. However, development of fluorescent tracers is often challenging and remains the principal bottleneck for this approach. One way to alleviate the burden of developing a specific tracer for each receptor is using promiscuous tracers, which is made possible by the intrinsic specificity of BRET. Here, we devised an integrated tracer discovery workflow that couples machine learning-guided in silico screening for scaffolds displaying promiscuous binding to GPCRs with a blend of synthetic strategies to rapidly generate multiple tracer candidates. Subsequently, these candidates were evaluated for binding in a NanoBRET ligand-engagement screen across a library of HiBiT-tagged GPCRs. Employing this workflow, we generated several promiscuous fluorescent tracers that can effectively engage multiple GPCRs, demonstrating the efficiency of this approach. We believe that this workflow has the potential to accelerate discovery of NanoBRET fluorescent tracers for GPCRs and other target classes.

Highlights

  • G protein-coupled receptors (GPCRs) are among the most widely studied pharmacological targets [1,2]

  • The high specificity afforded by NanoBRET allows utilization of promiscuous fluorescent tracers for selective measurements at specific HiBiT-GPCRs, reducing the need to develop a specific tracer for each target

  • Given the abundance of available information on GPCR-ligand interactions in public databases, we sought to design an in silico screening strategy to identify compounds exhibiting a propensity for promiscuous binding interactions

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Summary

Introduction

G protein-coupled receptors (GPCRs) are among the most widely studied pharmacological targets [1,2]. The resulting panel of fluorescent tracer candidates was evaluated in a NanoBRET ligand-engagement screen across a library of 184 HiBiT-tagged GPCRs. The screen revealed a good overall correlation with model predictions and provided valuable feedback on tracers’ binding profile, suitable conjugation site, and potential to deliver robust ligand-engagement assays. The screen revealed a good overall correlation with model predictions and provided valuable feedback on tracers’ binding profile, suitable conjugation site, and potential to deliver robust ligand-engagement assays Far, this strategy has resulted in several fluorescent tracers that can effectively engage multiple receptors from one or more GPCR families. We believe this workflow has the potential to accelerate tracer discovery and to broaden the target classes amenable for NanoBRET ligand-engagement analyses

Results and Discussion
Data Acquisition and Preparation for Machine Learning
Analysis of the Machine Learning Dataset Using UMAP
Development of a Machine Learning Model Classifying GPCR-Ligand Interactions
Evaluation of Model Predictions for Individual GPCR Families
Extending Model Predictions to Identify Promiscuous GPCR Ligands
Synthesis of Fluorescent Tracers from Selected Scaffolds
NanoBRET
Purinergic
Machine
2.10. Overlap
2.10. Summary
Data and Pre-Processing for Machine Learning
Data Representation using Molecular Fingerprints
UMAP Clustering of Molecules in the Training Dataset
Training the Machine Learning Model
Machine Learning Model Implementation and Comparison with NanoBRET Data
Robotic NanoBRET Screening
Methods
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