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使用基于结构的设计和计算方法识别新型强大的NSD2-PWWP1干

Luca Carlino¹, Peter C Astles¹, Bryony Ackroyd²

¹Oncology R&D, AstraZeneca, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K.

Journal of medicinal chemistry

|May 15, 2024

概括

研究人员确定了针对NSD2-PWWP1域的强效抑制剂,NSD2-PWWP1域是癌症中至关重要的蛋白质. 这一发现为研究核受体结合SET域2 (NSD2) 失调和开发向癌症治疗提供了新的工具.

科学领域:

生物化学生物化学
分子生物学分子生物学
计算化学计算化学

背景情况:

核受体结合核受体SET域2 (NSD2) 的基因组甲基转移酶失调与各种癌症有关.
NSD2是一种多域蛋白质,具有基因组的写入和阅读功能.
开发强效和选择性的NSD2抑制剂以向其酶活性是具有挑战性的.

研究的目的:

探索针对NSD2-PWWP1域的小分子,作为一种替代的抑制策略.
使用计算化学识别NSD2-PWWP1域的高亲和度结合剂.
为研究人类癌症中的NSD2功能提供新的化学工具.

主要方法:

采用先进的计算化学技术,包括与机器学习 (FEP/ML) 模型相结合的自由能量扰动.
利用虚拟选 (VS) 活动来识别潜在的NSD2-PWWP1结合剂.
合成和表征已识别的化合物,以结合亲和力.

主要成果:

确定了NSD2-PWWP1域的新型高亲和度结合剂.
发现化合物34,这是迄今为止报告的最强大的NSD2-PWWP1结合剂 (pIC50 = 8.2).
验证了计算方法在识别选择性酶抑制剂中的有效性.

结论:

这些已识别的化合物作为研究PWWP1域在NSD2抑制中的作用的有价值的工具.
这项工作表明了FEP/ML和VS在药物发现中对NSD2.2等具有挑战性的目标的潜力.
这些发现为开发针对NSD2驱动的恶性瘤的新型治疗策略铺平了道路.

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