Large-Scale G Protein-Coupled Olfactory Receptor–Ligand Pairing

G protein-coupled receptors (GPCRs) conserve common structural folds and activation mechanisms, yet their ligand spectra and functions are highly diverse. This work investigated how the amino-acid sequences of olfactory receptors (ORs)the largest GPCR familyencode diversified responses to various ligands. We established a proteochemometric (PCM) model based on OR sequence similarities and ligand physicochemical features to predict OR responses to odorants using supervised machine learning. The PCM model was constructed with the aid of site-directed mutagenesis, in vitro functional assays, and molecular simulations. We found that the ligand selectivity of the ORs is mostly encoded in the residues up to 8 Å around the orthosteric pocket. Subsequent predictions using Random Forest (RF) showed a hit rate of up to 58%, as assessed by in vitro functional assays of 111 ORs and 7 odorants of distinct scaffolds. Sixty-four new OR–odorant pairs were discovered, and 25 ORs were deorphanized here. The best model demonstrated a 56% deorphanization rate. The PCM-RF approach will accelerate OR–odorant mapping and OR deorphanization.G蛋白偶联受体(G protein-coupled receptors, GPCRs)保守共有结构折叠与激活机制，但其配体谱与功能却呈现高度多样性。本研究针对嗅觉受体(olfactory receptors, ORs)——作为最大的GPCR家族——的氨基酸序列如何编码对不同配体的多样化应答展开探究。我们基于嗅觉受体序列相似性与配体理化特征构建蛋白化学计量学(proteochemometric, PCM)模型，借助监督式机器学习方法预测受体对气味分子的应答。该PCM模型的构建依托定点诱变、体外功能实验与分子模拟手段完成。研究发现，嗅觉受体的配体选择性主要由正构口袋周围8埃（Å）以内的残基编码。后续采用随机森林(Random Forest, RF)开展的预测实验命中率最高可达58%，该结果通过针对111种嗅觉受体与7种不同骨架类型气味分子的体外功能实验得以验证。本研究共发现64组全新的受体-气味分子配对，并成功孤儿化25种嗅觉受体。最优模型的孤儿化成功率达56%。本研究所提出的PCM-RF方法将加速受体-气味分子映射与嗅觉受体孤儿化进程。