The 2019 novel coronavirus, identified as the cause for the pneumonia pathology reported in Wuhan, spread quickly and became a global pandemic. The project will employ experimental methods to develop sensors for the detection of SARSCoV-2 from environmental samples and develop predictive models for virus attachment to cells by applying computational machine learning methods. The outcome of this project will contribute to the development of proactive measures to identify viruses with pandemic potential before they are able to transmit and spread broadly among humans. The graduate students involved in this research will gain experience in protein biochemistry, fluorescence microscopy, and computational simulations and experience utilizing those skills to problems of societal importance.
This NSF Rapid response Research (RAPID) project will support a project that is aimed to characterize receptor interactions mediated by the Spike protein (S) of SARS-CoV-2. Development of fluorescence-based assays to characterize SARSCoV-2 attachment to Angiotensin converting enzyme (ACE2)-functionalized surfaces with controlled density and mobility, identifying peptide mimics of the ACE2 ectodomain for the development of sensors to detect SARSCoV-2 from environmental samples, and develop and validate predictive models of CoV attachment from primary sequence using machine learning constitute the specific goals of this project.
This RAPID award is made by the Molecular Biophysics Program in the Division of Molecular and Cellular Biosciences, 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.
