Agogo-Mawuli Percy S, Sadiya Isra, Abramyan Tigran M, Bosch Dustin E, Emmitte Kyle A, Colón-Pérez Luis M, Kosloff Mickey, Siderovski David P
Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, Texas, USA.
Faculty of Natural Sciences, Department of Human Biology, University of Haifa, Haifa, Israel.
J Biol Chem. 2025 Aug 21;301(10):110611. doi: 10.1016/j.jbc.2025.110611.
Regulator of G protein Signaling-14 (RGS14), an intracellular inactivator of G protein-coupled receptor (GPCR) signaling, is considered an undruggable protein, given its shallow and relatively featureless protein-protein interaction interface combined with a distal allosteric site prone to nonspecific inhibition by thiol-reactive compounds. Here, we identify and validate a tractable chemotype that selectively and non-covalently inhibits RGS14 GTPase-accelerating protein (GAP) activity. Combining structure-guided virtual screening, ligand docking across multiple receptor conformers, and enrichment validation, we progressed from a first-generation active, Z90276197, to over 40 second-generation analogs with improved potency. These inhibitors are predicted to engage the solvent-exposed "canyon" in the RGS14 RGS-box that interacts with the Gα switch I region. Binding pose predictions underscored the importance of non-polar interactions and shape complementarity over polar interactions in engaging this Gα-binding canyon and revealed an "ambidextrous" pattern of R1-and R2-group orientations. GAP inhibition was confirmed in fluorescence-based and gold-standard radioactive GTP hydrolysis assays. Two second-generation analogs, Z55660043 and Z55627844, inhibited RGS14 GAP activity in both assays and without measurable cytotoxicity. Deep learning-based scoring of predicted docking poses further supported observed affinity gains from R3-group additions. One analog demonstrated favorable in vivo pharmacokinetics and CNS penetration. Collectively, our findings establish tractable, non-covalent, small molecule inhibition of a G protein regulatory interface and illustrate how machine learning-enhanced docking can guide ligand optimization for shallow protein surfaces. This work opens the door to future development of RGS14 inhibitors as potential therapeutics for central nervous system and metabolic disorders.
G蛋白信号转导调节因子14（RGS14）是G蛋白偶联受体（GPCR）信号通路的一种细胞内失活剂，由于其蛋白质-蛋白质相互作用界面较浅且相对缺乏特征，再加上远端变构位点容易受到硫醇反应性化合物的非特异性抑制，因此被认为是一种难以成药的蛋白质。在此，我们鉴定并验证了一种易于处理的化学类型，它能选择性且非共价地抑制RGS14的GTP酶加速蛋白（GAP）活性。结合结构导向的虚拟筛选、跨多个受体构象的配体对接以及富集验证，我们从第一代活性化合物Z90276197发展出了40多种活性更强的第二代类似物。预计这些抑制剂会与RGS14 RGS结构域中与Gα开关I区域相互作用的溶剂暴露“峡谷”结合。结合姿势预测强调了在与这个Gα结合峡谷结合时，非极性相互作用和形状互补性比极性相互作用更重要，并揭示了R1和R2基团取向的“双灵巧”模式。在基于荧光和金标准放射性GTP水解试验中证实了对GAP的抑制作用。两种第二代类似物Z55660043和Z55627844在两种试验中均抑制了RGS14的GAP活性，且无明显细胞毒性。基于深度学习的预测对接姿势评分进一步支持了从添加R3基团观察到的亲和力增加。一种类似物在体内表现出良好的药代动力学和中枢神经系统渗透性。总的来说，我们的研究结果确立了对G蛋白调节界面的易处理、非共价、小分子抑制作用，并说明了机器学习增强的对接如何指导浅蛋白表面的配体优化。这项工作为未来开发RGS14抑制剂作为中枢神经系统和代谢紊乱的潜在治疗药物打开了大门。
