RAPID: D3SC: Identification of Chemical Probes and Inhibitors Targeting Novel Sites on SARS-CoV-2 Proteins for COVID-19 Intervention

严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的生命周期涉及许多传染性和复制所需的病毒蛋白质和酶。针对这些酶的抑制剂可作为2019年预防冠状病毒病的潜在治疗干预措施(新冠肺炎)。通过这一奖项，化学系的生命过程化学项目支持东北大学的Mary Jo Ondrechen博士和Penny J.Beuning博士应用计算方法来识别SARS-CoV-2蛋白质中可能成为结合抑制剂的良好靶点的研究。该项目使用东北大学开发的人工智能方法来识别病毒蛋白质结构中的口袋和缝隙，这些可能成为抗病毒药物开发的新目标。通过计算搜索天然和合成化合物的大型数据集，以寻找适合这些替代位点的分子，并对任何适合的化合物进行实验测试，以确定它们是否具有抑制这些病毒酶功能的能力。该项目为研究生和博士后提供计算化学和生化分析方面的培训。该项目使用Ondrechen博士团队开发的独特的偏序最优似然(POOL)机器学习(ML)方法来预测SARS-CoV-2蛋白质中的多种类型的结合部位，包括催化部位、变构部位和其他相互作用部位。本项目的目标是应用POOL-ML方法，以SARS-CoV-2蛋白的三维结构为输入，识别病毒病原体SARS-CoV-2蛋白的结合位点。分子动力学模拟被用来生成系综对接的构象。来自大型分子数据库的化合物通过计算对接到预测的位置，以识别潜在的强结合配体。体外实验测试了SARS-CoV-2蛋白的候选配体，包括主要蛋白酶和2个ʹ-O-核糖核糖甲基转移酶的结合亲和力，以及最佳预测的抑制剂对直接生化测定的催化活性的影响。研究了蛋白质数据库(PDB)中的所有SARS-CoV-2蛋白质结构。这项研究的化合物文库包括：a)从锌和烯胺数据库中挑选出已在生产的2600多种化合物；b)该团队最近获得的食品中发现的20,000多种化合物的文库；这些化合物可能具有一些特殊的优势，包括现成的公共领域可得和低成本；以及c)2020年3月开放获取的CAS(美国化学协会)数据库，其中包含50,000种已知或潜在的抗病毒活性化合物。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The life cycle of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves a number of viral proteins and enzymes required for infectivity and replication. Inhibitors that target these enzymes serve as potential therapeutic interventions against coronavirus disease 2019 (COVID-19). With this award, the Chemistry of Life Processes program in the Chemistry Division is supporting the research of Drs. Mary Jo Ondrechen and Penny J. Beuning from Northeastern University to apply computational methods to identify sites in SARS-CoV-2 proteins that would be good targets for binding inhibitors. The project uses artificial intelligence methods developed at Northeastern University to identify pockets and crevices in the structures of viral proteins that may serve as new targets for the development of antiviral agents. Large datasets of natural and synthetic compounds are computationally searched for molecules that fit into these alternative sites, and any compounds that fit will be experimentally tested for their ability to inhibit the functions of these viral enzymes. The project provides training in computational chemistry and biochemical analysis to graduate students and postdoctoral associates.This project uses the unique Partial Order Optimum Likelihood (POOL) machine learning (ML) method developed by Dr. Ondrechen’s group to predict multiple types of binding sites in SARS-CoV-2 proteins, including catalytic sites, allosteric sites, and other interaction sites. The goals of this project are to apply the POOL-ML method to identify the binding sites on viral pathogen SARS-CoV-2 proteins using the three-dimensional protein structures as input. Molecular dynamics simulations are used to generate conformations for ensemble docking. Compounds from the large molecular databases are computationally docked into the predicted sites to identify potentially strong binding ligands. Candidate ligands to selected SARS-CoV-2 proteins, including the main protease and 2ʹ-O-ribose RNA methyltransferase, are experimentally tested in vitro for binding affinity and the effect of the best predicted inhibitors on catalytic activities determined by direct biochemical assays. All the SARS-CoV-2 protein structures in the Protein Data Bank (PDB) are studied. Compound libraries for the study include: a) selected 2600+ compounds from the ZINC and Enamine databases that are already being manufactured; b) a library of 20,000+ compounds found in foods that the team recently gained access to; these potentially hold some special advantages, including ready availability in the public domain and low cost; and c) the March 2020 open access CAS (American Chemical Society) database of 50,000 compounds with known or potential anti-viral activity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.