PIPP Phase I: Community Informed Computational Prevention of Pandemics

PIPP 第一阶段：社区知情计算预防流行病

• 批准号: 2200045
• 负责人: Th Murali
• 金额: $ 100万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200045&HistoricalAwards=false
• 关键词: PIPP; Phase; Community; Informed; Computational

The COVID-19 pandemic has revealed multiple gaps in our understanding of how viruses emerge to infect humans. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grant seeks to create a cohesive interdisciplinary research team to investigate three distinct yet related scientific challenges with the goal of predicting and preventing future pandemics. In the first theme, the project will develop computational and experimental methods that analyze the genomic sequences of viruses that currently infect animals to determine how they may evolve in order to cross species barriers and infect humans in the future. The second theme of the project will be to investigate and develop predictive models to find drugs that can be used to combat a virus identified in the first theme as having the potential to infect humans. A distinguishing characteristic of the approach will be to identify new uses for or to chemically modify drugs that have already been approved for other human diseases. Finally, in the third theme, the investigators will engage with the community at large to develop an understanding of societal and ethical concerns of the research.The COVID-19 pandemic has brought to the spotlight the urgent need to be prepared both on scientific and societal fronts to detect and curb novel zoonotic viral infections. The project seeks to control and prevent future viral pandemics through ethically-grounded and community-informed research that will (a) predict in silico cross-species virus infection and host adaptation and (b) perform computational drug redesign and repurposing. The deep learning methods to be developed will detect positive selection events in virus sequences that permit a zoonotic shift from an animal host to humans. These methods will be translatable to any viral pathogen whose genome has been sequenced. The project will experimentally validate predictions using single or integrated human organoids, which will enable a comprehensive understanding on how a pathogen could infect and harm a broad range of organs and tissues. Other explainable machine learning systems to be developed will predict interactions between viral and human proteins, repurpose drugs that target key host proteins required by a virus, and redesign high affinity antiviral drugs. The investigators will use innovative efforts to integrate community engagement into the scientific goals of the project. The community-informed framework addresses the challenges faced during pandemics related to public perception of preventive strategies, societal concerns, inequities in healthcare. It will result in a multifaceted strategy to prepare for and address the next pandemic. These efforts can be translated to best practices in future potential crises on how to effectively integrate disciplines and utilize feedback from diverse sources to successfully implement preventative measures. Team-building and peer-mentoring activities will enable participants to think outside the realms of traditional scientific boundaries and instill the importance of interdisciplinary research, thereby providing a pipeline to a workforce in the future that will value and thrive in a team science environment.This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).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大流行揭示了我们对病毒如何出现并感染人类的理解存在诸多空白。这一流行病预防预测情报（PIPP）第一阶段：发展资助旨在建立一个有凝聚力的跨学科研究团队，研究三个不同但相关的科学挑战，目标是预测和预防未来的流行病。在第一个主题中，该项目将开发计算和实验方法，分析目前感染动物的病毒的基因组序列，以确定它们如何进化，以便在未来跨越物种障碍并感染人类。该项目的第二个主题将是调查和开发预测模型，以找到可用于对抗在第一个主题中确定的有可能感染人类的病毒的药物。该方法的一个显著特点将是确定已经批准用于治疗其他人类疾病的药物的新用途或对其进行化学修饰。最后，在第三个主题中，研究人员将与社区广泛接触，以了解研究的社会和伦理问题。COVID-19大流行使人们认识到，迫切需要在科学和社会方面做好准备，以发现和遏制新型人畜共患病毒感染。该项目旨在通过以伦理为基础和社区知情的研究来控制和预防未来的病毒大流行，这些研究将(a)在计算机上预测跨物种病毒感染和宿主适应，(b)进行计算药物重新设计和重新利用。待开发的深度学习方法将检测病毒序列中的积极选择事件，这些事件允许人畜共患病从动物宿主转移到人类。这些方法将适用于任何基因组已测序的病毒病原体。该项目将通过实验验证使用单个或集成的人体类器官的预测，这将使人们能够全面了解病原体如何感染和伤害广泛的器官和组织。其他有待开发的可解释的机器学习系统将预测病毒和人类蛋白质之间的相互作用，重新设计针对病毒所需的关键宿主蛋白质的药物，以及重新设计高亲和力抗病毒药物。研究人员将利用创新的努力将社区参与纳入该项目的科学目标。社区知情框架解决了大流行病期间面临的挑战，涉及公众对预防战略的看法、社会关切、保健方面的不平等。这将产生一项多方面的战略，以防备和应对下一次大流行。这些努力可以转化为未来潜在危机中如何有效整合学科和利用来自不同来源的反馈来成功实施预防措施的最佳实践。团队建设和同伴指导活动将使参与者能够在传统科学界限之外的领域进行思考，并灌输跨学科研究的重要性，从而为未来的劳动力提供一条管道，这些劳动力将在团队科学环境中得到重视和发展。该奖项由流行病预防的跨部门预测情报第一阶段（PIPP）计划提供支持，该计划由生物科学（BIO）、计算机信息科学与工程（CISE）、工程（ENG）和社会、行为和经济科学（SBE）委员会共同资助。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。