RAPID - COVID-19 target epitopes and human genetic factors of virulence

该RAPID奖项将为SARS-CoV-2研究开发新的计算工具，通过体现进化、物理学和机器学习基本概念的算法，为COVID-19带来大量关于变异和（通常被忽视的）分歧的进化数据。该项目将开发，基准测试和传播工具，完全在计算机上对每个CoV 2蛋白进行深度突变扫描。对冠状病毒家族的序列、结构和功能的计算分析将发现CoV 2蛋白质组中的功能表位，并权衡新出现的CoV 2毒株中新突变的功能影响。其结果将是全面绘制CoV 2中所有可操作的靶点，这些靶点随后可能用作泛冠状病毒疫苗的抗原或重新用途药物的对接位点。对人类宿主遗传因素的机器学习搜索可以区分无症状至轻度COVID-19感染和严重至致命感染，这将有助于识别和传播人类宿主死亡率生物标志物，从而针对个体遗传风险定制个性化的预防措施和治疗方式。 该项目采用进化行动理论，该理论采用综合数学物理方法来研究序列变异，蛋白质结构功能和进化分歧之间的耦合。这种方法包括计算形成适应度景观的进化力量，并评估突变在穿越这些景观时针对这些力量所消耗的能量。虽然大多数自然突变产生的能量很低，对功能或进化的影响很小或没有影响，但少数突变携带显著的能量，并可靠地改变功能和适应性。包括SARS-CoV-2在内的初步数据表明，这种方法将产生精确和可调的蛋白质功能表位图，以及突变对功能影响的准确评分。作为一个训练特征，它将为机器学习提供每个突变的功能影响的非常准确的测量，以发现哪些基因在一组复杂表型的携带者与对照组中受到影响。 由于EA是基于进化和蛋白质结构-功能的基本原理，它是完全通用的，我们提出的计算技术可以用于研究任何病毒，细菌或真核系统及其相互作用。这将导致对基因型-表型关系的广泛的新见解，对所有生命王国的研究，教育和生物技术发展同样有用。该奖项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。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。

This RAPID award will develop new computational tools for SARS-CoV-2 research that bring to bear on COVID-19 a massive amount of evolutionary data on variations and (often neglected) divergences through algorithms that embody basic concepts of evolution, physics, and machine learning. The project will develop, benchmark and disseminate tools that perform deep mutational scanning of every CoV2 protein entirely in silico. A computational analysis of sequence, structure and function across the Coronavirus family will yield the discovery of functional epitopes across the CoV2 proteome and on weighing the functional impact of new mutations in emerging CoV2 strains. The outcome will be to comprehensively map all actionable targets in CoV2 that may then serve, for example, as antigens for pan-Coronavirus vaccines or as docking sites for repurposed drugs. A machine learning search for human host genetic factors that may distinguish between asymptomatic to mild COVID-19 infections from severe to lethal infections will help identify and disseminate human host biomarkers of mortality that personalize preventive measures and treatment modalities, tailored to individual genetic risks. The project applies an Evolutionary Action theory that takes an integrative mathematical physics approach to the couplings between sequence variations, protein structure-function and evolutionary divergences. This approach involves computing the evolutionary forces that shaped fitness landscapes, and assessing the energy expended by mutations against these forces as they traverse these landscapes. While most natural mutations exert low energy and little or no impact on function or evolution, a few mutations carry significant energy and reliably change function and fitness. Preliminary data, including on SARS-CoV-2, show that this approach will yield precise and tunable maps of protein functional epitopes and accurate scores of the impact of mutations on function. As a training feature, it will provide a singularly accurate measure of the functional impact of each mutation for machine learning to find which genes are diferentially affected in a group of carriers of a complex phenotype vs a control group. Because EA is based on fundamental principles of evolution and protein structure-function, it is entirely general and the computational techniques we propose can be deployed to study any virus, bacterium, or eukaryotic system and their interactions. This will lead to broad new insights into the genotype-phenotype relationship, equally useful to research, education, and biotechnology development across all kingdoms of life. 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.