IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus

IIBR：信息学：RAPID：SARS-CoV-2 病毒的全基因组结构和功能建模

• 批准号: 2030790
• 负责人: Yang Zhang
• 金额: $ 19.98万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2020-06-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030790&HistoricalAwards=false
• 关键词: IIBR; Informatics; RAPID; Genome; wide

The most recent outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic. It has spread over more than 200 countries and caused numerous deaths worldwide. The central activities of SARS-CoV-2, including human cell invasion and viral duplication and infection, are conducted through the proteins coded by the viral genome as well as the protein-protein interactions between the virus and its human hosts. Determination of the structures, functions and interactions of protein molecules associated with coronaviruses can thus provide critically important knowledge to help elucidate and end the pandemic. This project will extend state-of-the-art structural bioinformatics methods to generate genome-wide protein structure and function models for SARS-CoV-2 and other human coronaviruses, which will help in understanding the general mechanisms and principles governing the virulence, diversity and evolution of these coronaviruses and facilitate the development of new treatments to cure infected individuals and terminate the COVID-19 pandemic. Multiple graduate and undergraduate students, including women and minorities, will be trained through participation in different Objectives of the project. The project results will be integrated with the bioinformatics core courses in the Bioinformatics and Biochemistry PhD Programs and the Museum of Natural History at the University of Michigan, with the purpose of enhancing the outreach and broad impacts of this research on both student and public education.Accurately modeling protein structure and function has been a long-term challenge in structural bioinformatics and computational biology. A classical approach to this problem is comparative modeling, i.e., deducing information of unknown target proteins from known homologous proteins that are evolutionarily related to the targets. This approach is built on the assumption that similar sequences have similar structures and functions. Although they work well in many applications, the comparative approaches cannot be applied to effectively model proteins associated with SARS-CoV-2 and other human coronaviruses, because viral genomes are highly mutable, and many of the genes and gene products belonging to these viruses do not have close homologous templates with other species. To address these issues, this project plans to extend multiple algorithms developed in the PI’s lab, which have been designed primarily for non-homology-based protein structure and function prediction. In particular, the methods will utilize cutting-edge deep convolutional neural-network (DCNN) models to generate amino acid-level contact and distance maps in order to improve protein structure and interaction network modeling accuracy. Since the DCNN models are trained only on sequence databases, the performance of the approaches does not rely on the availability of structural and functional templates and can therefore be effectively used to model the coronavirus proteins that lack homologous templates; successfully developing these methods will also benefit the field of structural bioinformatics in general due to the importance of non-homologous protein structure and function prediction. In summary, the success of this project will result in the development of an urgently needed knowledge base to improve the understanding of fundamental principles associated with human coronaviruses and facilitate the development of new treatments for the COVID-19 pandemic. The data and methods produced by the project will be accessible to the community at https://zhanglab.ccmb.med.umich.edu/COVID-19/. This RAPID award is made by the Division of Biological Infrastructure, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.

最近一次爆发的2019年冠状病毒病(新冠肺炎)，由严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)引起，已成为一场全球大流行。它已经蔓延到200多个国家，并在世界范围内造成了无数人死亡。SARS-CoV-2的中心活动，包括人类细胞入侵、病毒复制和感染，是通过病毒基因组编码的蛋白质以及病毒与其宿主之间的蛋白质-蛋白质相互作用来进行的。因此，确定与冠状病毒相关的蛋白质分子的结构、功能和相互作用可以提供至关重要的知识，以帮助阐明和结束大流行。这个项目将扩展最先进的结构生物信息学方法来生成SARS-CoV-2和其他人类冠状病毒的全基因组蛋白质结构和功能模型，这将有助于了解这些冠状病毒毒力、多样性和进化的一般机制和原理，并促进开发新的治疗方法来治愈受感染的个人和终止新冠肺炎大流行。多名研究生和本科生，包括妇女和少数群体，将通过参与该项目的不同目标接受培训。该项目的成果将与生物信息学和生物化学博士项目以及密歇根大学自然历史博物馆的生物信息学核心课程相结合，目的是加强这项研究对学生和公共教育的扩展和广泛影响。准确地模拟蛋白质的结构和功能一直是结构生物信息学和计算生物学的长期挑战。解决这一问题的一个经典方法是比较建模，即从已知的与目标进化相关的同源蛋白中推导出未知目标蛋白的信息。这种方法是建立在相似序列具有相似结构和功能的假设之上的。虽然比较方法在许多应用中效果良好，但不能用于有效地模拟与SARS-CoV-2和其他人类冠状病毒相关的蛋白质，因为病毒基因组高度可变，并且属于这些病毒的许多基因和基因产物与其他物种没有紧密的同源模板。为了解决这些问题，该项目计划扩展PI实验室开发的多种算法，这些算法主要是为基于非同源性的蛋白质结构和功能预测而设计的。特别是，这些方法将利用尖端的深度卷积神经网络(DCNN)模型来生成氨基酸水平的接触图和距离图，以提高蛋白质结构和相互作用网络的建模精度。由于DCNN模型只在序列数据库上训练，因此方法的性能不依赖于结构和功能模板的可用性，因此可以有效地用于模拟缺乏同源模板的冠状病毒蛋白质；由于非同源蛋白质结构和功能预测的重要性，成功开发这些方法也将总体上有益于结构生物信息学领域。总之，该项目的成功将导致建立一个亟需的知识库，以提高对与人类冠状病毒有关的基本原理的理解，并促进开发针对新冠肺炎大流行病的新疗法。该项目产生的数据和方法将在https://zhanglab.ccmb.med.umich.edu/COVID-19/.上向社区开放这一快速奖项由生物基础设施部利用冠状病毒援助、救济和经济安全(CARE)法案的资金做出。这一奖项反映了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。

项目成果

The Human DNA Mismatch Repair Protein MSH3 Contains Nuclear Localization and Export Signals That Enable Nuclear-Cytosolic Shuttling in Response to Inflammation

• DOI: 10.1128/mcb.00029-20
• 发表时间: 2020-07-01
• 期刊: MOLECULAR AND CELLULAR BIOLOGY
• 影响因子: 5.3
• 作者: Tseng-Rogenski, Stephanie S.;Munakata, Koji;Carethers, John M.
• 通讯作者: Carethers, John M.

Identifying the Zoonotic Origin of SARS-CoV-2 by Modeling the Binding Affinity between the Spike Receptor-Binding Domain and Host ACE2

• DOI: 10.1021/acs.jproteome.0c00717
• 发表时间: 2020-12-04
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Huang, Xiaoqiang;Zhang, Chengxin;Zhang, Yang
• 通讯作者: Zhang, Yang