Karges Johannes, Stokes Ryjul W, Cohen Seth M
Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, United States.
ACS Med Chem Lett. 2022 Feb 24;13(3):428-435. doi: 10.1021/acsmedchemlett.1c00584. eCollection 2022 Mar 10.
Computational modeling of inhibitors for metalloenzymes in virtual drug development campaigns has proven challenging. To overcome this limitation, a technique for predicting the binding pose of metal-binding pharmacophores (MBPs) is presented. Using a combination of density functional theory (DFT) calculations and docking using a genetic algorithm, inhibitor binding was evaluated in silico and compared with inhibitor-enzyme cocrystal structures. The predicted binding poses were found to be consistent with the cocrystal structures. The computational strategy presented represents a useful tool for predicting metalloenzyme-MBP interactions.
在虚拟药物研发活动中，金属酶抑制剂的计算建模已被证明具有挑战性。为克服这一局限性，本文提出了一种预测金属结合药效团（MBP）结合构象的技术。结合密度泛函理论（DFT）计算和使用遗传算法的对接方法，在计算机上评估了抑制剂结合情况，并与抑制剂-酶共晶体结构进行了比较。发现预测的结合构象与共晶体结构一致。所提出的计算策略是预测金属酶-MBP相互作用的有用工具。
