Jedwabny Wiktoria, Kłossowski Szymon, Purohit Trupta, Cierpicki Tomasz, Grembecka Jolanta, Dyguda-Kazimierowicz Edyta
Department of Chemistry , Wrocław University of Science and Technology , Wyb. Wyspiańskiego 27 , 50-370 Wrocław , Poland . Email:
Department of Pathology , University of Michigan , 1150 W. Medical Center Dr, MSRBI, Rm 4510D , Ann Arbor , MI 48109 , USA . Email:
Medchemcomm. 2017 Dec 1;8(12):2216-2227. doi: 10.1039/c7md00170c. Epub 2017 Sep 12.
Development and binding affinity predictions of inhibitors targeting protein-protein interactions (PPI) still represent a major challenge in drug discovery efforts. This work reports application of a predictive non-empirical model of inhibitory activity for PPI inhibitors, exemplified here for small molecules targeting the menin-mixed lineage leukemia (MLL) interaction. Systematic analysis of menin-inhibitor complexes was performed, revealing the physical nature of these interactions. Notably, the non-empirical protein-ligand interaction energy comprising electrostatic multipole and approximate dispersion terms ((10)El,MTP + ) produced a remarkable correlation with experimentally measured inhibitory activities and enabled accurate activity prediction for new menin-MLL inhibitors. Importantly, this relatively simple and computationally affordable non-empirical interaction energy model outperformed binding affinity predictions derived from commonly used empirical scoring functions. This study demonstrates high relevance of the non-empirical model we developed for binding affinity prediction of inhibitors targeting protein-protein interactions that are difficult to predict using empirical scoring functions.
针对蛋白质-蛋白质相互作用(PPI)的抑制剂的开发和结合亲和力预测,仍然是药物研发工作中的一项重大挑战。本研究报告了一种用于PPI抑制剂抑制活性的预测性非经验模型的应用,此处以靶向Menin-混合谱系白血病(MLL)相互作用的小分子为例。对Menin-抑制剂复合物进行了系统分析,揭示了这些相互作用的物理本质。值得注意的是,包含静电多极和近似色散项的非经验蛋白质-配体相互作用能((10)El,MTP +)与实验测量的抑制活性具有显著相关性,并能够对新型Menin-MLL抑制剂的活性进行准确预测。重要的是,这种相对简单且计算成本较低的非经验相互作用能模型优于常用经验评分函数得出的结合亲和力预测。本研究表明,我们开发的非经验模型对于难以用经验评分函数预测的靶向蛋白质-蛋白质相互作用的抑制剂的结合亲和力预测具有高度相关性。