The award to Hauptman-Woodward Medical Research Institute supports research into machine learning approaches to understand the interactions of SARS-COV-2 proteins. The researchers will combine information from the viral genome with other data on protein structures to predict protein interactions. This research affords significant societal benefits by providing important information about the virus biology. The research may also contribute to the identification of potential therapeutic compounds. An early stage researcher will participate extensively in the project as part of training activities. Software and data from the studies will be shared in public repositories, published in peer-reviewed journals, and presented at scientific meetings.
Researchers supported by this award will develop machine learning based computational tools for prediction of protein-protein interactions (PPI) in the infectious disease setting involving host proteins and viral pathogen proteins. Computational tools that can leverage immediately arising data sources to advance experimental work on the virus can make a major and immediate impact on pandemic response. Support vector machine classifiers and Bayesian inferential methods will be used to develop machine learning models that incorporate both genomic and structural information to better understand and predict protein interactions. The goal in creating computational tools to understand the host-pathogen interface is to contribute basic information on protein interactions that dictate the mechanisms of virus entry into cells and modes of transmission of viral pathogens. Methods developed in this proposal will be valuable in future situations where rapid information development about an emerging pathogen is required.
This RAPID award is made by the Division of Biological Infrastructure (DBI) 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.
