论文标题
SARS-COV-2的药物重新利用：高通量分子对接，分子动力学，机器学习和AB-Initio研究
Drug repurposing for SARS-COV-2: A high-throughput molecular docking, molecular dynamics, machine learning, & ab-initio study
论文作者
论文摘要
一个尺寸的125nm分子已引起约4.79亿人的感染（美国为8000万）和全球610万人类的死亡（美国为977,000），并在两年内将全球经济削减了8.5万亿美元。最近历史上唯一的其他事件是通过直接使用（分别是人类或自然）来导致人类生命损失的比较，即“纳米结构”（分别是人为人造的或自然）的结构 - 性能关系，是美国在第二次世界大战期间和1918年流感流动流动性的美国日本城市核炸弹袭击。该分子是SARS-COV-2，它导致一种称为Covid-19的疾病。大流行的高负债成本激励了各种私人，政府和学术实体，以寻找治愈这些和新兴疾病的方法。结果，发现多个候选疫苗以避免感染。但是到目前为止，找到完全有效的治疗候选者还没有成功。在本文中，我们试图根据复杂的多规模内部框架提供多种疗法候选。我们已经使用以下可靠的框架来筛选配体。第1步：高吞吐量对接，步骤II：分子动力学，步骤III：密度功能理论分析。总的来说，我们已经分析了220万个独特的蛋白质结合位点/配体组合。根据最近的实验研究选择蛋白质。 Step-I基于分子对接结合能将该数量降低至10个配体/蛋白质，并基于药物务实分析进一步筛选至2个配体/蛋白质。此外，通过基于分子动态的RMSD分析，在第II条中研究了这两种配体/蛋白质。最终提出了三种配体（ZINC1176619532，ZINC517580540，ZINC952855827）攻击蛋白质的不同结合位点（7BV2），这些结合位点在步骤III中得到了进一步分析。
A molecule of dimension 125nm has caused around 479 Million human infections (80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years. The only other events in recent history that caused comparative human life loss through direct usage (either by (wo)man or nature, respectively) of structure-property relations of 'nano-structures' (either (wo)man-made or nature, respectively) were nuclear bomb attacks of Japanese cities by the USA during World War II and 1918 Flu Pandemic. This molecule is SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for these & emerging diseases. As result, multiple vaccine candidates are discovered to avoid the infection in first place. But so far, there has been no success in finding fully effective therapeutics candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework. We have used the following robust framework to screen the ligands; Step-I: high throughput docking, Step-II: molecular dynamics, Step-III: density functional theory analysis. In total, we have analyzed 2.2 Million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands/proteins were investigated in Step-II with a molecular dynamic based RMSD analysis. It finally suggested three ligands (ZINC1176619532, ZINC517580540, ZINC952855827) attacking different binding sites of the protein(7BV2), which were further analyzed in Step III.
