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

RAPID：D3SC：针对 SARS-CoV-2 蛋白新位点的化学探针和抑制剂的鉴定，用于干预 COVID-19

• 批准号: 2030180
• 负责人: Mary Jo Ondrechen
• 金额: $ 16.58万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030180&HistoricalAwards=false
• 关键词: RAPID; D3SC; Identification; Chemical; Probes; RAPID; D3SC; Identification; Chemical; Probes

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.

严重急性呼吸综合征2（SARS-COV-2）的生命周期涉及多种病毒蛋白和复制所需的病毒蛋白和酶。靶向这些酶的抑制剂是针对2019年冠状病毒病（COVID-19）的潜在治疗干预措施。有了这个奖项，化学部门的化学过程计划支持DRS的研究。来自东北大学的Mary Jo Ondrechen和Penny J. Beuning采用计算方法来识别SARS-COV-2蛋白中的位点，这将是结合抑制剂的良好靶标。该项目使用在东北大学开发的人工智能方法来识别病毒蛋白结构中的口袋和缝隙，这些方法可能是抗病毒剂开发的新目标。大量的天然和合成化合物数据集在计算中搜索适合这些替代位点的分子，并且任何适合的化合物都将在实验测试中，以抑制这些病毒酶功能的能力。该项目为研究生和博士后伙伴提供了计算化学和生物化学分析的培训。本项目使用Ondrechen博士小组开发的独特部分阶最佳可能性（POM）机器学习（ML）方法来预测多种类型的SARS-COV-2蛋白质中的约束位点，包括催化性催化的位点，包括催化的位点，构型，其他相互作用。该项目的目标是应用池-ML方法以使用三维蛋白质结构作为输入来识别病毒病原体SARS-COV-2蛋白上的结合位点。分子动力学模拟用于生成集合对接的构象。来自大分子数据库的化合物在计算上停靠到预测位点，以识别潜在的强结合配体。对选定的SARS-COV-2蛋白的候选配体，包括主蛋白和2ʹ-O-ribose RNA RNA甲基转移酶，在体外对结合亲和力进行了实验测试，以及最佳预测抑制剂对由直接生物化学分析确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蛋白结构。该研究的化合物库包括：a）来自已经制造的锌和搪瓷数据库中的2600多种化合物； b）该团队最近获得的食品中发现了20,000多种化合物的图书馆；这些可能具有一些特殊的优势，包括在公共领域的现成供应和低成本； c）2020年3月的开放访问CAS（美国化学学会）数据库，该数据库具有50,000种具有已知或潜在的反病毒活动的化合物。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被视为通过评估来获得珍贵的支持。