Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease

Molecular Dynamics simulations are often used in drug design. However, such simulations do not account for the physiological environment of the receptor; hence overlook its impact on biomolecular interactions. To address this lacuna, we identified three objectives to pursue – develop models of physiological environment, study a drug-receptor complex in such environments, and identify methods to analyze these complicated simulations. Two novel physiological models were developed and studied. The first, called ‘m10’, comprises of 10 of the most abundant cytoplasmic metabolites at physiological concentrations. The second, called ‘phy’, supplements m10 with an additional crowder protein to elicit macromolecular crowding effect. The main protease (M<i>^pro</i>) of SARS-CoV-2, being essential for viral replication, is an attractive drug target for COVID-19. Hence, we chose M<i>^pro</i> docked with multiple drugs as our model drug-receptor system. With a plethora of compounds, physiological systems can be exceedingly large and complex. A novel Spark-based software (SparkTraj) was developed to rapidly analyze non-specific contacts and water interactions. Our study shows that crowding enhances the difference in the dynamics of apo- vs drug-bound complexes. Metabolites, at times as a cluster, were seen interacting with the protease, drugs, and binding sites in drug-free receptor. Except one that <i>crawled</i> to an adjacent pocket in phy, the drugs remained in their respective pockets in all simulations. Given these observations, we hope that the models and approach presented here would help the optimization, evaluation, and selection of potential drugs. Generic biomolecular dynamics could also benefit from such models and tools. Communicated by Ramaswamy H. Sarma

分子动力学（Molecular Dynamics）模拟常被应用于药物研发领域。然而，此类模拟并未考虑受体的生理环境，因此忽略了生理环境对生物分子相互作用的影响。为填补这一研究空白，我们确立了三项研究目标：构建生理环境模型、在该类环境中研究药物-受体复合物，以及开发用于分析这类复杂模拟结果的方法。
我们开发并研究了两种全新的生理环境模型。第一种命名为‘m10’，由10种在生理浓度下含量最丰富的细胞质代谢物组成。第二种命名为‘phy’，在m10的基础上额外添加了一种拥挤蛋白，以模拟大分子拥挤（macromolecular crowding）效应。
新型冠状病毒（SARS-CoV-2）的主要蛋白酶（main protease, Mpro）是病毒复制所必需的关键靶点，也是抗COVID-19药物研发的理想靶标。因此，我们选用了与多种药物对接后的Mpro作为本次研究的药物-受体模型系统。
由于涉及的化合物种类繁多，生理系统往往规模庞大且结构复杂。我们开发了一款基于Spark的新型软件（SparkTraj），可快速分析非特异性接触与水相互作用。
本研究表明，大分子拥挤会扩大无配体复合物与药物结合复合物之间的动力学差异。部分代谢物有时会以簇状形式与蛋白酶、药物以及无配体受体的结合位点发生相互作用。除了在phy模型中一个药物‘爬’入了相邻口袋之外，所有模拟中的药物均保留在其初始结合口袋中。
基于上述研究结果，我们期望本文所提出的生理模型与分析方法能够助力潜在候选药物的优化、评估与筛选。通用生物分子动力学研究也可从这类模型与工具中获益。
由Ramaswamy H. Sarma转交