RAPID: Using Data Science and Biophysical Models to Address the COVID-19 Pandemic

RAPID：利用数据科学和生物物理模型应对 COVID-19 大流行

• 批准号: 2030491
• 负责人: Javier Arsuaga
• 金额: $ 20万
• 依托单位: University of California-Davis
• 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: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030491&HistoricalAwards=false
• 关键词: RAPID; Using; Data; Science; Biophysical

COVID-19, the disease caused by the SARS-CoV-2 coronavirus, is at the center of one of the most dangerous pandemics the world has ever known. As it spreads through the human population the virus mutates producing proteins that can lead to higher infection rates (infectivity), and an increased ability to cause severe disease (virulence). This project will predict the most likely mutations of the virus by combining methods from machine learning, mathematics and biophysics. Specifically, the proteins resulting from viral mutations will be experimentally synthesized, and their infectivity and virulence will be tested by the project team through a collaboration with researchers in industry. This project benefits from unprecedented access to genomic data compiled on SARS-CoV-2, combined with a rich set of novel tools developed through interdisciplinary advances in data science, mathematics, and biophysics. The results of this project will build a pipeline capable of assisting the development of vaccines and drugs against COVID-19 while simultaneously advancing the fields of machine learning and mathematical virology. The project team is led by mathematicians, molecular biologists and biotechnology experts working in an interdisciplinary and collaborative setting. Students and postdoctoral researchers will be trained and will participate in publicly disseminating the findings and results of the project. The SARS-CoV-2 coronavirus is believed to have originated as a bat virus and to have evolved through a combination of sequence mutations, recombination, and natural selection to be infectious in human hosts. Some of the most relevant sequence variations occurred in the S gene encoding the Spike (S) protein. As SARS-CoV-2 spreads through the human population, mutations of the S gene can potentially increase viral infectivity and virulence. Within the framework of an evolutionary algorithm, the PIs will combine graph theory, topological data analysis, and computational biophysics to characterize the most likely mutations of the S protein. This powerful interdisciplinary approach will draw upon existing experimental data from SARS-CoV-2. The PIs will collaborate with an industrial partner to experimentally design the peptides corresponding to those predicted sequences, and use binding affinity assays and cryo-electron microscopy to test binding of the peptides to the human receptor (ACE2). The resulting pipeline will help us better understand the evolutionary landscape of viral proteins and will assist researchers in the development of anti-viral drugs and vaccines. Future extensions of this work will increase our understanding of how viruses are transmitted across species and propagate in humans. The project will provide multi-disciplinary student and postdoctoral training. The PIs will broadly disseminate their results, as well as the data they collect and software they design.With this award, the Mathematical Biology Program in the Division of Mathematical Sciences and the Chemistry of Life Processes Program in the Division of Chemistry are supporting Drs. Arsuaga, Rodriguez, and Vazquez from University of California-Davis to study genomic variations of the SARS-CoV-2 viral spike (S) protein and predict the expansion range of transmission in human populations.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.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.

COVID-19 是由 SARS-CoV-2 冠状病毒引起的疾病，是世界上已知的最危险的流行病之一的核心。当病毒在人群中传播时，病毒会发生突变，产生蛋白质，从而导致更高的感染率（传染性），并增强引起严重疾病（毒力）的能力。该项目将结合机器学习、数学和生物物理学的方法来预测病毒最可能的突变。 具体来说，病毒突变产生的蛋白质将通过实验合成，项目团队将与业界研究人员合作，测试其感染性和毒力。该项目得益于对 SARS-CoV-2 基因组数据的前所未有的访问，以及通过数据科学、数学和生物物理学的跨学科进步开发的一套丰富的新颖工具。该项目的成果将建立一条能够协助开发针对 COVID-19 的疫苗和药物的管道，同时推进机器学习和数学病毒学领域的发展。该项目团队由数学家、分子生物学家和生物技术专家领导，在跨学科和协作环境中工作。学生和博士后研究人员将接受培训，并将参与公开传播该项目的研究结果和成果。 SARS-CoV-2 冠状病毒被认为起源于蝙蝠病毒，并通过序列突变、重组和自然选择的结合而进化，从而在人类宿主中具有感染性。一些最相关的序列变异发生在编码 Spike (S) 蛋白的 S 基因中。随着 SARS-CoV-2 在人群中传播，S 基因的突变可能会增加病毒的感染性和毒力。在进化算法的框架内，PI 将结合图论、拓扑数据分析和计算生物物理学来表征 S 蛋白最可能的突变。这种强大的跨学科方法将利用 SARS-CoV-2 的现有实验数据。 PI 将与工业合作伙伴合作，通过实验设计与这些预测序列相对应的肽，并使用结合亲和力测定和冷冻电子显微镜来测试肽与人类受体 (ACE2) 的结合。由此产生的管道将帮助我们更好地了解病毒蛋白的进化景观，并将协助研究人员开发抗病毒药物和疫苗。这项工作的未来扩展将加深我们对病毒如何跨物种传播和在人类中传播的理解。该项目将提供多学科的学生和博士后培训。 PI 将广泛传播他们的结果，以及他们收集的数据和他们设计的软件。通过该奖项，数学科学系的数学生物学项目和化学系的生命过程化学项目将支持 Drs.加州大学戴维斯分校的 Arsuaga、Rodriguez 和 Vazquez 负责研究 SARS-CoV-2 病毒刺突 (S) 蛋白的基因组变异，并预测在人群中传播范围的扩大。这笔赠款是使用分配给 MPS 的冠状病毒援助、救济和经济安全 (CARES) 法案补充资金提供的资金授予​​的。该奖项反映了 NSF 的法定资金 使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。