Development of General Representation Learning Methods for Protein Tertiary Structures
Design Novel Generative Models for generating physically-realistic protein tertiary structures
Applications, Downstream Tasks and Analysis
The key role that the three-dimensional structure of a protein molecule plays in its function and activities in the cell continues to motivate computational research. In particular, we now know that proteins harness their ability to access different structures to regulate their interactions with other molecules. My research leverages the growing momentum in generative AI and contributes increasingly sophisticated deep latent variable models that learn informative representations of protein structures. Rigorous empirical evaluation demonstrates the capabilities of these models in sampling the protein structure space and additionally addressing important protein modeling tasks, linking protein structure and function. I have designed different generative neural network models that learn directly from experimentally-available structures of different protein molecules and generate physically-realistic structures of a target protein, enabling us to expand our in-silico characterization of these ubiquitous molecules beyond the static, single-structure view. My work advances bioinformatics research in molecular biology.