GCR: Understanding Virus Evolution Through Deep Raman Spectroscopy

GCR：通过深度拉曼光谱了解病毒进化

• 批准号: 1934977
• 负责人: Mauricio Terrones
• 金额: $ 600万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934977&HistoricalAwards=false
• 关键词: GCR; Understanding; Virus; Evolution; Through

This Growing Convergence Research project aims at revolutionizing how circulating epidemic and pandemic influenza virus strains are surveilled and characterized. The project brings together virologists who perform controlled mutations of the virus, nanoscience engineers who create platforms for virus enrichment, materials scientists and optical spectroscopists who develop effective 2D material Raman signal enhancement platforms, and data scientists who analyze data with machine learning methods to achieve virus identification and prediction of potential emergence of new antigenic variants. The goal of the research team is to develop a convergent device platform that can rapidly capture, sense, and identify viruses and predict new antigenic strains against which the human population has limited or no immunity.The project pursues a solution to a grand challenge in the surveillance and characterization of circulating epidemic and pandemic influenza virus strains by addressing deep scientific questions in enhanced Raman spectroscopy for virus detection and evolution prediction. The proposed platform is based on controlled virology experiments that propagate by culture and mutate viruses for specific research tasks, a novel virus enrichment platform for effectively capturing viruses without labels, biosensing of virus surface proteins with enhanced signal through a novel 2D/metal enhanced Raman spectroscopy technique, and rapid and sensitive virus identification and evolution prediction through machine learning analysis of enhanced Raman data.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.

这个不断增长的融合研究项目旨在彻底改变流行性和大流行性流感病毒株的监测和特征。 该项目汇集了执行病毒受控突变的病毒学家，创建病毒富集平台的纳米科学工程师，开发有效的2D材料拉曼信号增强平台的材料科学家和光谱学家，以及用机器学习方法分析数据以实现病毒识别和预测新抗原变体潜在出现的数据科学家。 研究团队的目标是开发一种融合的设备平台，可以快速捕获，感知，该项目旨在通过解决增强型拉曼光谱检测病毒中的深层次科学问题，解决流行性和大流行性流感病毒株监测和表征方面的重大挑战进化预测。所提出的平台是基于受控病毒学实验，通过培养和突变病毒进行特定的研究任务，一种新的病毒富集平台，用于有效地捕获病毒而无需标记，通过新的2D/金属增强拉曼光谱技术对病毒表面蛋白进行生物传感，以及通过增强型拉曼数据的机器学习分析进行快速灵敏的病毒识别和进化预测。该奖项反映了NSF的法定使命，并已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，认为值得支持。

项目成果

Understanding the Excitation Wavelength Dependence and Thermal Stability of the SARS-CoV-2 Receptor-Binding Domain Using Surface-Enhanced Raman Scattering and Machine Learning

• DOI: 10.1021/acsphotonics.2c00456
• 发表时间: 2022-08
• 期刊: ACS Photonics
• 影响因子: 7
• 作者: Kunyan Zhang;Ziyang Wang;He Liu;N. Peréa-López;Jeewan C Ranasinghe;G. Bepete;Allen M. Minns

A rapid and label-free platform for virus capture and identification from clinical samples

• DOI: 10.1073/pnas.1910113117
• 发表时间: 2020-01-14
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Yeh, Yin-Ting;Gulino, Kristen;Terrones, Mauricio
• 通讯作者: Terrones, Mauricio