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.

严重的急性呼吸综合症冠状病毒2（SARS-COV-2）引起的冠状病毒疾病（CoVID-19）的最新爆发已成为全球大流行。它已经蔓延到200多个国家，并在全球造成了许多死亡。 SARS-COV-2的主要活性，包括人类细胞侵袭和病毒重复和感染，是通过病毒基因组编码的蛋白质以及病毒与其人宿主之间的蛋白质 - 蛋白质相互作用进行的。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。该项目将扩展最先进的结构生物信息学方法，以生成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，这将有助于理解管理这些冠状病毒的病毒，多样性以及这些冠状病毒的多样性和进化的一般机制和原理，并支持新治疗的新处理，并支持新的cure forcure fimpic forcected cure cure cure cole cole coved covend covend covid9 pend covid19 ped covid-covid-19 ped。多名毕业生和本科生，包括妇女和少数民族，将通过参与该项目的不同目标而接受培训。该项目的结果将与密歇根大学的生物信息学和生物化学博士学位课程以及自然历史博物馆中的生物信息学核心课程融合在一起，目的是增强这项研究对学生和公共教育的外观和广泛影响。对蛋白质结构和功能进行了长期挑战，在结构生物学中具有长期的挑战。解决此问题的经典方法是比较建模，即从已知同源蛋白中推论未知靶蛋白的信息，这些蛋白与靶标有关。这种方法是基于以下假设：相似序列具有相似的结构和功能。尽管它们在许多应用中效果很好，但是比较方法不能应用于有效模拟与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都没有与其他物种的同源模板密切的同源模板。为了解决这些问题，该项目计划扩展PI实验室中开发的多种算法，这些算法主要用于非基于基于非同学的蛋白质结构和功能预测。特别是，这些方法将利用最先进的深卷积中性网络（DCNN）模型生成氨基酸级接触和距离图，以提高蛋白质结构和相互作用网络建模的精度。由于DCNN模型仅在序列数据库上进行训练，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对缺乏同源模板的冠状病毒蛋白进行建模。由于非同疗蛋白结构和功能预测的重要性，成功开发这些方法的结构生物信息学和功能预测的重要性也将有益于结构生物信息学的领域。总而言之，该项目的成功将导致急需的知识基础发展，以提高对与人类冠状病毒相关的基本原理的理解，并支持为Covid-19-19大流行的新疗法发展。该项目生产的数据和方法将在https://zhanglab.ccmb.med.umich.edu/covid-19/上访问。该快速奖励是由生物基础设施部使用冠状病毒援助，救济和经济安全（CARES）法案的资金颁发的。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估NSF的法定任务。

项目成果

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

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.