RAPID IIBR Informatics Computational methods for utilizing SARS-Cov2 sequence and structure data in predicting host-pathogen protein-protein interactions

RAPID IIBR 利用 SARS-Cov2 序列和结构数据预测宿主-病原体蛋白质-蛋白质相互作用的信息学计算方法

• 批准号: 2029885
• 负责人: Miranda Lynch
• 金额: $ 19.98万
• 依托单位: Hauptman-Woodward Medical Research Institute Inc
• 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=2029885&HistoricalAwards=false
• 关键词: RAPID; IIBR; Informatics; Computational; methods

The award to Hauptman-Woodward Medical Research Institute supports research into machine learning approaches to understand the interactions of SARS-COV-2 proteins. The researchers will combine information from the viral genome with other data on protein structures to predict protein interactions. This research affords significant societal benefits by providing important information about the virus biology. The research may also contribute to the identification of potential therapeutic compounds. An early stage researcher will participate extensively in the project as part of training activities. Software and data from the studies will be shared in public repositories, published in peer-reviewed journals, and presented at scientific meetings.Researchers supported by this award will develop machine learning based computational tools for prediction of protein-protein interactions (PPI) in the infectious disease setting involving host proteins and viral pathogen proteins. Computational tools that can leverage immediately arising data sources to advance experimental work on the virus can make a major and immediate impact on pandemic response. Support vector machine classifiers and Bayesian inferential methods will be used to develop machine learning models that incorporate both genomic and structural information to better understand and predict protein interactions. The goal in creating computational tools to understand the host-pathogen interface is to contribute basic information on protein interactions that dictate the mechanisms of virus entry into cells and modes of transmission of viral pathogens. Methods developed in this proposal will be valuable in future situations where rapid information development about an emerging pathogen is required.This RAPID award is made by the Division of Biological Infrastructure (DBI) 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.

该奖项授予Hauptman-Woodward医学研究所，以支持机器学习方法的研究，以了解SARS-COV-2蛋白的相互作用。 研究人员将把来自病毒基因组的联合收割机信息与蛋白质结构的其他数据结合起来，预测蛋白质相互作用。 这项研究通过提供有关病毒生物学的重要信息，提供了显着的社会效益。 这项研究也可能有助于鉴定潜在的治疗化合物。 作为培训活动的一部分，一名早期研究人员将广泛参与该项目。 该研究的软件和数据将在公共资源库中共享，发表在同行评议的期刊上，并在科学会议上展示。该奖项支持的研究人员将开发基于机器学习的计算工具，用于预测感染性疾病中涉及宿主蛋白和病毒病原体蛋白的蛋白质-蛋白质相互作用（PPI）。能够利用即时出现的数据源来推进病毒实验工作的计算工具，可以对大流行病的应对产生重大和直接的影响。支持向量机分类器和贝叶斯推理方法将用于开发机器学习模型，这些模型将基因组和结构信息结合起来，以更好地理解和预测蛋白质相互作用。 创建计算工具以了解宿主-病原体界面的目标是提供有关蛋白质相互作用的基本信息，这些蛋白质相互作用决定了病毒进入细胞的机制和病毒病原体的传播模式。该提案中开发的方法在未来需要快速开发新出现病原体信息的情况下将非常有价值。该RAPID奖由生物基础设施部（DBI）使用冠状病毒援助，救济，经济安全（CARES）该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。