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

最近由严重急性呼吸系统综合征冠状病毒2 （SARS-CoV-2）引起的2019年冠状病毒病（COVID-19）爆发已成为全球大流行。它已经蔓延到200多个国家，并在世界范围内造成大量死亡。SARS-CoV-2的核心活动，包括入侵人类细胞、病毒复制和感染，都是通过病毒基因组编码的蛋白质以及病毒与人类宿主之间的蛋白质-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，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，这将有助于了解这些冠状病毒的毒力、多样性和进化的一般机制和原则，并促进开发新的治疗方法，以治愈感染者并终止COVID-19大流行。包括妇女和少数民族在内的多名研究生和本科生将通过参与该项目的不同目标而得到培训。该项目的研究成果将与密歇根大学生物信息学和生物化学博士课程以及自然历史博物馆的生物信息学核心课程相结合，以加强这项研究对学生和公众教育的推广和广泛影响。准确建模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，即从已知的与目标蛋白进化相关的同源蛋白中推断未知目标蛋白的信息。这种方法建立在相似序列具有相似结构和功能的假设之上。尽管比较方法在许多应用中都很有效，但它们不能有效地用于模拟与SARS-CoV-2和其他人类冠状病毒相关的蛋白质，因为病毒基因组是高度可变的，而且属于这些病毒的许多基因和基因产物与其他物种没有密切的同源模板。为了解决这些问题，该项目计划扩展PI实验室开发的多种算法，这些算法主要用于基于非同源的蛋白质结构和功能预测。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和距离图，以提高蛋白质结构和相互作用网络建模的准确性。由于DCNN模型仅在序列数据库上进行训练，因此该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；由于非同源蛋白结构和功能预测的重要性，这些方法的成功发展也将有利于结构生物信息学领域的发展。总而言之，该项目的成功将导致建立一个急需的知识库，以提高对与人类冠状病毒相关的基本原理的理解，并促进开发针对COVID-19大流行的新疗法。该项目产生的数据和方法将在https://zhanglab.ccmb.med.umich.edu/COVID-19/上向社区提供。该RAPID奖由生物基础设施部使用《冠状病毒援助、救济和经济安全法案》的资金颁发。该奖项反映了美国国家科学基金会的法定使命，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。

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.