Wu Xinyuan, Pan Jiayi, Fan Rufeng, Zhang Yiwei, Wang Chao, Wang Guoliang, Liu Jiaxiang, Cui Mengqing, Yue Jinfeng, Jin Rui, Duan Zhiqiang, Zheng Mingyue, Mei Lianghe, Zhou Lu, Tan Minjia, Ai Jing, Lu Xiaojie
State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.
J Am Chem Soc. 2025 May 7;147(18):15469-15481. doi: 10.1021/jacs.5c01712. Epub 2025 Apr 28.
Covalent small molecule drugs have emerged as a crucial support in precision therapy due to their high selectivity and robust potency. Covalent DNA-encoded chemical library (CoDEL) technology is an advanced platform for covalent drug discovery. However, the application of CoDELs is constrained by a single-residue focus and limited warhead diversity. Here we report a method to identify residue-selective inhibitors using CoDELs with diverse warheads targeting multiple distinct residues. We systematically evaluated the reactivity of 17 warheads with 9 nucleophilic amino acids of FGFR2 and then constructed CoDELs comprising 24.8 million compounds. These CoDELs enabled the identification of active covalent inhibitors targeting cysteine, lysine, arginine, or glutamic acid. The lysine-targeting inhibitor engaged a novel reactive site. The arginine-targeting inhibitor demonstrated subtype selectivity and overcame drug resistance. The glutamic acid-targeting inhibitor validated the druggability of this unconventional covalent residue site. These findings suggest that our work could potentially expand the target space of covalent drugs and promote precision therapy by harnessing the power of the CoDELs.
由于具有高选择性和强大效力，共价小分子药物已成为精准治疗的关键支撑。共价DNA编码化学文库（CoDEL）技术是共价药物发现的先进平台。然而，CoDELs的应用受到单一残基关注和有限弹头多样性的限制。在此，我们报告一种使用具有针对多个不同残基的多样弹头的CoDELs来鉴定残基选择性抑制剂的方法。我们系统地评估了17种弹头与FGFR2的9种亲核氨基酸的反应性，然后构建了包含2480万个化合物的CoDELs。这些CoDELs能够鉴定靶向半胱氨酸、赖氨酸、精氨酸或谷氨酸的活性共价抑制剂。靶向赖氨酸的抑制剂涉及一个新的反应位点。靶向精氨酸的抑制剂表现出亚型选择性并克服了耐药性。靶向谷氨酸的抑制剂验证了这个非常规共价残基位点的可成药性。这些发现表明，我们的工作可能会扩大共价药物的靶点空间，并通过利用CoDELs的力量促进精准治疗。
