Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. Here, we offered an innovative solution to identify Mcl1 inhibitors with molecular dynamics-refined pharmacophore and machine learning methods. Considering the safety and druggability of FDA-approved drugs, virtual screening of the database was performed to discover Mcl1 inhibitors, and the hit was subsequently validated via TR-FRET, cytotoxicity, and flow cytometry assays. To reveal the binding characteristics shared by the hit and a typical Mcl1 selective inhibitor, we employed quantum mechanics and molecular mechanics (QM/MM) calculations, umbrella sampling, and metadynamics in this work. The combined studies suggested that fluvastatin had promising cell inhibitory potency and was suitable for further investigation. We believe that this research will shed light on the discovery of novel Mcl1 inhibitors that can be used as a supplemental treatment against leukemia and provide a possible method to improve the accuracy of drug repurposing with limited computational resources while balancing the costs of experimentation well.

中文翻译：

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应用基于分子动力学的药效团和机器学习方法通​​过药物再利用和力学研究来识别新型 Mcl1 抑制剂


髓样细胞白血病 1 (Mcl1) 是一种调节细胞凋亡的关键蛋白，被认为是抗肿瘤药物的一个有前景的靶点。传统的药效团筛选方法在构象采样和数据挖掘方面存在局限性。在这里，我们提供了一种创新的解决方案，通过分子动力学改进的药效团和机器学习方法来识别 Mcl1 抑制剂。考虑到 FDA 批准药物的安全性和成药性，对数据库进行了虚拟筛选以发现 Mcl1 抑制剂，随后通过 TR-FRET、细胞毒性和流式细胞术检测验证了该抑制剂。为了揭示命中与典型 Mcl1 选择性抑制剂共有的结合特征，我们在这项工作中采用了量子力学和分子力学 (QM/MM) 计算、伞式采样和元动力学。综合研究表明氟伐他汀具有良好的细胞抑制效力，适合进一步研究。我们相信，这项研究将揭示新型 Mcl1 抑制剂的发现，这些抑制剂可用作白血病的补充治疗，并提供一种可能的方法，以有限的计算资源提高药物重新利用的准确性，同时很好地平衡实验成本。