PIPP Phase I: Develop and Evaluate Computational Frameworks to Predict and Prevent Future Coronavirus Pandemics

PIPP 第一阶段：开发和评估计算框架以预测和预防未来的冠状病毒大流行

• 批准号: 2200138
• 负责人: Hong Qin
• 金额: $ 100万
• 依托单位: University of Tennessee Chattanooga
• 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=2200138&HistoricalAwards=false
• 关键词: PIPP; Phase; Develop; Evaluate; Computational

Once a novel coronavirus or a new variant is detected, how likely would the novel coronavirus or new variant transmit from person to person, and how sick could patients become? What kind of new coronaviruses could cause future pandemics? Knowing the answers to these questions can help nations make proper strategic decisions. The dilemma is how to predict the behavior and pathogenic severity of new viruses as early as possible. A team of researchers thinks they have found ways to answer these questions by developing new artificial intelligence software tools to predict the virus’s behaviors based on its genome sequence. This team of researchers recognizes the potential bias in machine learning applications and the need to increase diversity in the future artificial intelligence workforce. Leveraging their expertise in genomics, data science, artificial intelligence, genetics, infectious disease, chemical engineering, public health, and communication, this team of researchers will organize training workshops and activities providing culturally responsive teaching of artificial intelligence, data science training to teachers, and context-relevant coding experiences to high school students. The team will promote public trust in science and discernment of misinformation through community outreach. This research team will prototype a deep learning model based on biological knowledge and hypotheses that can predict viral pathogenic fitness from genomic sequences to test the potential rules for viral pathogenicity. The team will explore several methods to correct the sampling bias in viral genomic surveillance in order to accurately estimate the fitness of a viral strain. The team will investigate the mutation and recombination profiles in all available bat coronavirus genomes from the Southeastern Asia and build a prototype geospatial model to predict the recombination probability for all available bat coronaviruses. Leveraging their expertise in genetics and macromolecular structure modeling, the team will test a few candidate genes in SARS-CoV-2 for potential pathogenic rules. Based on the outcomes of these pilot projects, the team will be able to estimate the pathogenic fitness of an emerging SARS-CoV-2 variant or another novel coronavirus. 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.

一旦检测到新型冠状病毒或新变种，新型冠状病毒或新变种在人与人之间传播的可能性有多大，以及患者的病情会如何？了解这些问题的答案可以导致未来的大流行吗？帮助各国做出正确的战略决策的困境是如何尽早预测新病毒的行为和致病严重性。一组研究人员认为，他们已经找到了通过开发新的人工智能软件工具来预测病毒的方法来回答这些问题。该团队的研究人员根据其基因组序列进行行为。该团队认识到机器学习应用中的潜在偏见以及增加未来人工智能劳动力多样性的必要性，他们利用其在基因组学、数据科学、人工智能、遗传学、传染病、化学工程、公共卫生和通信方面的专业知识。研究人员将组织培训研讨会和活动，为教师提供文化响应式的人工智能教学、数据科学培训，并为高中生提供与情境相关的编码体验。该团队将通过社区外展活动促进公众对科学的信任和对错误信息的辨别。团队将基于深度学习模型构建原型该团队将探索多种方法来纠正病毒基因组监测中的采样偏差，以准确估计病毒株的适应性。该团队将研究东南亚所有可用蝙蝠冠状病毒基因组的突变和重组概况，并建立一个原型地理空间模型来预测所有可用蝙蝠冠状病毒的重组概率，该团队将利用其在遗传学和大分子结构建模方面的专业知识。根据这些试点项目的结果，测试 SARS-CoV-2 中的一些候选基因，以了解潜在的致病规则，该团队将能够估计新出现的 SARS-CoV-2 变体或另一种新型冠状病毒的致病适应性。该奖项由跨部门的流行病预防第一阶段预测智能（PIPP）项目支持，该项目由生物科学（BIO）、计算机信息科学与工程（CISE）、工程（ENG）和社会科学理事会联合资助，行为方面该奖项反映了 NSF 的法定使命，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。

项目成果

A yeast-based system to study SARS-CoV-2 Mpro structure and to identify nirmatrelvir resistant mutations

基于酵母的系统，用于研究 SARS-CoV-2 Mpro 结构并识别 nirmatrelvir 耐药突变

• DOI: 10.1371/journal.ppat.1011592
• 发表时间: 2023-08
• 期刊: PLOS Pathogens
• 作者: Jin Ou;E. M. Lew;owski;owski;Yanmei Hu;Austin A. Lipinski;Ryan T. Morgan;L. Jacobs;Xiujun Zhang;M. Bikowitz;P. Langlais;Hao Tan;Jun Wang;Yu Chen;John S. Choy
• 通讯作者: John S. Choy

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

用于心脏病预测的多目标人工蜂群优化混合深度置信网络和 XGBoost 算法

• DOI: 10.3389/fdgth.2023.1279644
• 发表时间: 2023-11
• 期刊: Frontiers in Digital Health
• 作者: Kalita, Kanak;Ganesh, Narayanan;Jayalakshmi, Sambandam;Chohan, Jasgurpreet Singh;Mallik, Saurav;Qin, Hong
• 通讯作者: Qin, Hong

Structure, dynamics and free energy studies on the effect of point mutations on SARS-CoV-2 spike protein binding with ACE2 receptor

点突变对 SARS-CoV-2 刺突蛋白与 ACE2 受体结合影响的结构、动力学和自由能研究

• DOI: 10.1371/journal.pone.0289432
• 发表时间: 2023
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Rucker, George;Qin, Hong;Zhang, Liqun
• 通讯作者: Zhang, Liqun