There are tens of thousands of proteins that bind with other or identical proteins. These protein interactions play a critical role in a range of cellular and physiological functions including gene regulation and immune system response. As such, aberrant protein association has been implicated in pathological conditions including metabolic disorders, chronic inflammation, and cancer. Thus, developing molecules that can control protein interactions would have a broad impact on numerous scientific disciplines including immunology, pathology, and cellular/molecular biology. Furthermore, the identification of chemical agents that reduce unwanted protein interactions would lead to novel therapeutics and diagnostic tools having important clinical implications. Apart from the scientific and clinical impact, such studies would also advance biotechnology, including the manufacturing of specialty proteins and the ability to probe commercially important biological reaction mechanisms. Thus, there is a clear need to develop chemical agents that can control protein association. The overall objective of this project is to employ machine learning tools and molecular-scale simulation, coupled with experimental validation of the predicted molecular performance, to rationally design molecules that can control protein interactions.

This research program focuses on the interferon regulator factors (IRF) family of proteins, where protein association is critical to the function of these proteins. IRFs play an important role in numerous physiological and pathological processes, such as in the signaling pathways operative during the immune response to pathogens. The IRF family has nine known members, providing a wide range of protein associations to study. Preliminary research has revealed that small organic molecules, like phenyl methimazole (termed C10), are effective at blocking the association of members of the IRF family. In this research program, high-throughput searches around the chemical space of C10, using a combination of genetic algorithms, machine learning, and molecular dynamics simulation, will be used to find molecules optimized for binding with IRF members. Studies will be conducted to determine the specificity of the binders, i.e., are the binders specific to one particular IRF member or do they behave as pan-IRF binders. Once optimized candidate binders are computationally identified, they will be procured or synthesized, and molecular/cellular assays will be used to quantify their ability to inhibit IRF association. The computational strategies will allow exploration of a large chemical space to narrow the search to a small number of potential binder molecules and the experiments will serve as validation for this approach. Because molecular-level details of how small molecules bind with IRF proteins will be revealed, the fundamental knowledge generated in this research program will be applicable in the search for inhibitors of association of other protein families. The investigators will be involved in a number of outreach activities, including organizing district-level science fairs and participation in university-level programs such as the Program to Aid Career Exploration to advance the Appalachian region of Ohio through education.

This project is co-funded by Chemistry of Life Processes in the Division of Chemistry (Mathematical and Physical Sciences Directorate) and by the Process Systems, Reaction Engineering, and Molecular Thermodynamics Program of the Division of Chemical, Bioengineering, Environmental, and Transport Systems (Directorate for Engineering).

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.

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Ohio University
United States
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