This COVID-19 EArly-concept Grant for Exploratory Research (EAGER) project will investigate how SARS-CoV-2, virus causing the disease, attaches to cells, and how it may detach when subject to mechanical force. The virus attaches to cells through its spike protein, and it is believed that this protein has flexible regions called hinges that allow it to change its shape to regulate its attachment. This project will establish a method involving molecular simulations and data science methods to reliably identify these hinges. These analyses will aim to explain how mechanical forces and chemical factors influence hinge motions that influence the binding of the virus to cells. Understanding how the spike protein changes its shape during viral attachment, and how it may detach, e.g. during coughing and sneezing is critical for accurately describing how the virus enters cells, and how we can prevent infection. These findings will advance scientific understanding about this novel coronavirus and provide fundamental insights needed to find a cure for the disease.

This research program will aim to implement a novel computational strategy to understand how hinge motions of the spike protein govern viral attachment. This method will combine molecular simulations with network reconstruction and community detection methods that are new to the study of proteins. It will aim to accurately identify soft and stiff regions of the protein, pinpoint hinge motions, and examine how mechanical forces impact receptor binding domain accessibility and binding strength. This will provide critically needed physical insights into how SARS-CoV-2 binds and infects cells, how it may dislodge when subject to force, and which compliant regions antibodies could target to prevent attachment. Statistically rigorous techniques for inferring network properties will help to correctly identify the regions that regulate viral attachment. As part of outreach efforts, physics-based animations and videos on the spike protein dynamics and viral attachment will be created and disseminated. Statistical methods, codes and software developed to infer networks and hinge motions from simulations will be shared with the research community.

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.

Project Start
Project End
Budget Start
2020-06-15
Budget End
2021-05-31
Support Year
Fiscal Year
2020
Total Cost
$188,034
Indirect Cost
Name
Northwestern University at Chicago
Department
Type
DUNS #
City
Chicago
State
IL
Country
United States
Zip Code
60611