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

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.

严重急性呼吸道综合征冠状病毒2（SARS-CoV-2）的生命周期涉及感染和复制所需的许多病毒蛋白质和酶。针对这些酶的抑制剂可作为针对2019冠状病毒病（COVID-19）的潜在治疗干预措施。有了这个奖项，化学部的生命过程化学项目正在支持东北大学的玛丽·乔·昂德雷琴和彭妮·J·博宁博士的研究，应用计算方法来确定SARS-CoV-2蛋白中的位点，这些位点将是结合抑制剂的良好靶点。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结构中的口袋和裂缝，这些口袋和裂缝可能成为开发抗病毒剂的新目标。通过计算搜索天然和合成化合物的大数据集，以寻找适合这些替代位点的分子，并且将通过实验测试任何适合的化合物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物化学分析方面的培训。该项目使用Ondrechen博士团队开发的独特的偏序优化Likestive（POOL）机器学习（ML）方法来预测SARS-CoV-2蛋白中多种类型的结合位点，包括催化位点、变构位点和其他相互作用位点。本项目的目标是应用POOL-ML方法，以蛋白质的三维结构为输入，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产生构象系综对接。将来自大型分子数据库的化合物计算对接到预测位点以鉴定潜在的强结合配体。选择的SARS-CoV-2蛋白，包括主要的蛋白酶和2-O-核糖RNA甲基转移酶的候选配体，实验测试在体外的结合亲和力和最佳预测的抑制剂的催化活性的影响，通过直接生化测定确定。研究了蛋白质数据库（Protein Data Bank，PDB）中所有SARS-CoV-2蛋白质的结构。该研究的化合物库包括：a）从ZINC和Enamine数据库中选择的2600多种化合物，这些化合物已经在生产中; B）该团队最近获得的食品中发现的20，000多种化合物的库;这些化合物可能具有一些特殊优势，包括在公共领域的现成可用性和低成本;以及c）2020年3月开放获取的CAS（美国化学学会）数据库，包含50，000种已知或潜在抗病毒活性的化合物。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Identification and characterization of alternative sites and molecular probes for SARS-CoV-2 target proteins.

• DOI: 10.3389/fchem.2022.1017394
• 发表时间: 2022
• 期刊: Frontiers in chemistry
• 影响因子: 5.5

Reintegrating Biology Through the Nexus of Energy, Information, and Matter

通过能量、信息和物质的联系重新整合生物学

• DOI: 10.1093/icb/icab174
• 发表时间: 2021
• 期刊: Integrative and Comparative Biology
• 影响因子: 2.6
• 作者: Hoke, Kim L;Zimmer, Sara L;Roddy, Adam B;Ondrechen, Mary Jo;Williamson, Craig E;Buan, Nicole R
• 通讯作者: Buan, Nicole R