Deep Latent Variable Models for Learning Representations of Protein Tertiary Structures

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. The research presented in this dissertation 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. The models presented in this dissertation 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. This dissertation work advances bioinformatics research in molecular biology.

Author

• Alam, Fardina Fathmiul

Identifier

hdl:1920/14026

OAI identifier oai:identifier

oai:MARS:1920/14026