Sztain Terra, Amaro Rommie, McCammon J Andrew
Department of Chemistry and Biochemistry.
Department of Pharmacology, University of California, San Diego, La Jolla, California 92093, United States.
bioRxiv. 2020 Jul 24:2020.07.23.218784. doi: 10.1101/2020.07.23.218784.
The SARS-CoV-2 pandemic has rapidly spread across the globe, posing an urgent health concern. Many quests to computationally identify treatments against the virus rely on small molecule docking to experimentally determined structures of viral proteins. One limit to these approaches is that protein dynamics are often unaccounted for, leading to overlooking transient, druggable conformational states. Using Gaussian accelerated molecular dynamics to enhance sampling of conformational space, we identified cryptic pockets within the SARS-CoV-2 main protease, including some within regions far from the active site and assed their druggability. These pockets can aid in virtual screening efforts to identify a protease inhibitor for the treatment of COVID-19.
严重急性呼吸综合征冠状病毒2(SARS-CoV-2)大流行已在全球迅速蔓延,引发了紧迫的健康问题。许多通过计算来确定针对该病毒治疗方法的探索都依赖于小分子对接实验确定的病毒蛋白结构。这些方法的一个局限性在于,蛋白质动力学常常未被考虑在内,导致忽略了短暂的、可成药的构象状态。我们使用高斯加速分子动力学来增强构象空间的采样,在SARS-CoV-2主要蛋白酶中识别出了隐秘口袋,包括一些远离活性位点区域内的口袋,并评估了它们的可成药性。这些口袋有助于虚拟筛选工作,以识别用于治疗2019冠状病毒病(COVID-19)的蛋白酶抑制剂。