Bioinformatics Algorithms for Protein Interactions and Applications

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Proteins drive most cellular processes by interacting with other proteins, to form protein complexes, or with other molecules such as nucleic acids and small ligands. Experimental techniques for protein interaction identification remain expensive, labor intensive, time consuming and suffer from high levels of false-positive predictions. Accurately predicting these interactions in silico is a fundamental problem in bioinformatics. My proposed research program concerns developing algorithms and tools for highly accurate, interpretable, and applicable protein interaction prediction. This work will impact the biologists' ability to determine, understand, and use protein interactions. The models I propose to build will be made widely available and easily accessible via web servers. Users with proper training will be able to freely download and run the software for high throughput prediction. High confidence in-silico predictions can dramatically reduce the wet lab work of biologists, as they can focus their efforts on those interactions that are most likely to be successful. Understanding the interactions will help to annotate protein functions, construct PPI networks, and study disease mechanisms. The applications presented can be used by others to develop novel therapeutics, e.g., against viruses like HIV, SARS-CoV-2, through drug screening, repurposing, and side effect identification.