Department of Chemistry, Memorial University of Newfoundland, St. John's, NL, A1B 3X9, Canada.
Phys Chem Chem Phys. 2022 Oct 5;24(38):23391-23401. doi: 10.1039/d2cp03080b.
The main protease (M) of the SARS-CoV-2 virus is an attractive therapeutic target for developing antivirals to combat COVID-19. M is essential for the replication cycle of the SARS-CoV-2 virus, so inhibiting M blocks a vital piece of the cell replication machinery of the virus. A promising strategy to disrupt the viral replication cycle is to design inhibitors that bind to the active site cysteine (Cys145) of the M. Cysteine targeted covalent inhibitors are gaining traction in drug discovery owing to the benefits of improved potency and extended drug-target engagement. An interesting aspect of these inhibitors is that they can be chemically tuned to form a covalent, but reversible bond, with their targets of interest. Several small-molecule cysteine-targeting covalent inhibitors of the M have been discovered-some of which are currently undergoing evaluation in early phase human clinical trials. Understanding the binding energetics of these inhibitors could provide new insights to facilitate the design of potential drug candidates against COVID-19. Motivated by this, we employed rigorous absolute binding free energy calculations and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations to estimate the energetics of binding of some promising reversible covalent inhibitors of the M. We find that the inclusion of enhanced sampling techniques such as replica-exchange algorithm in binding free energy calculations can improve the convergence of predicted non-covalent binding free energy estimates of inhibitors binding to the M target. In addition, our results indicate that binding free energy calculations coupled with multiscale simulations can be a useful approach to employ in ranking covalent inhibitors to their targets. This approach may be valuable in prioritizing and refining covalent inhibitor compounds for lead discovery efforts against COVID-19 and other coronavirus infections.
新型冠状病毒主蛋白酶(M)是开发抗 COVID-19 抗病毒药物的有吸引力的治疗靶标。M 对于新型冠状病毒的复制周期至关重要,因此抑制 M 会阻断病毒细胞复制机制的重要部分。破坏病毒复制周期的一个有前途的策略是设计与 M 的活性位点半胱氨酸(Cys145)结合的抑制剂。由于提高效力和延长药物靶点结合的益处,靶向半胱氨酸的共价抑制剂在药物发现中越来越受到关注。这些抑制剂的一个有趣方面是,它们可以通过化学方法进行调整,与感兴趣的靶标形成共价但可逆的键。已经发现了几种针对 M 的小分子半胱氨酸靶向共价抑制剂,其中一些目前正在进行早期人体临床试验评估。了解这些抑制剂的结合能有助于为设计针对 COVID-19 的潜在药物候选物提供新的见解。受此启发,我们采用了严格的绝对结合自由能计算和混合量子力学/分子力学(QM/MM)计算来估算一些有前途的 M 可逆共价抑制剂的结合能。我们发现,在结合自由能计算中包含增强采样技术(如复制交换算法)可以提高抑制剂与 M 靶标结合的非共价结合自由能估计的预测收敛性。此外,我们的结果表明,结合自由能计算与多尺度模拟相结合可以成为对共价抑制剂与其靶标进行排序的有用方法。这种方法可能在针对 COVID-19 和其他冠状病毒感染的先导发现工作中对共价抑制剂化合物进行优先级排序和优化方面具有重要价值。