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.
获取有关配体在生物相关条件下与G蛋白偶联受体(GPCRs)相互作用的药理学见解对于药物发现和基础研究至关重要。基于NanoLuc的生物发光共振能量转移(NanoBRET)监测荧光示踪剂与未修改的测试化合物之间的竞争结合已经成为一种强大而敏感的方法,用于量化与特定GPCRs遗传融合到NanoLuc荧光酶或发光HiBiT肽的配体相互作用。然而,开发荧光示踪剂通常具有挑战性,并且仍然是该方法的主要瓶颈。减轻为每个受体开发特定示踪剂的负担的一种方法是使用多功能示踪剂,这是由BRET的固有特异性所实现的。在这里,我们设计了一种集成示踪剂发现工作流程,将机器学习引导的体外筛选具有对GPCRs显示多功能结合的支架与合成策略的混合相结合,以快速生成多个示踪剂候选物。随后,这些候选物在HiBiT标记的GPCRs库中进行了NanoBRET配体结合筛选的结合评估。利用这种工作流程,我们生成了几种可以有效与多个GPCRs相互作用的多功能荧光示踪剂,展示了这种方法的效率。我们相信这种工作流程有潜力加速发现用于GPCRs和其他靶标类别的NanoBRET荧光示踪剂。