Deep Learning for Protein Complex Prediction and Design
For computational structural biology, this work advances the ability to predict and design protein complexes, which is crucial for understanding cellular function and developing therapeutics.
This thesis develops deep learning methods for modeling and designing protein complex structures, achieving improved prediction accuracy and enabling sequence design for protein complexes.
Accurately modeling and designing protein complex structures is a central problem in computational structural biology, with broad implications for understanding cellular function and developing therapeutics. This thesis investigates two fundamental aspects of this problem using deep learning: domain-specific architectures that capture the hierarchical nature of protein structures, and search algorithms that efficiently navigate the vast sequence spaces of protein complexes to identify interacting homologs for improving complex structure prediction and to design protein sequences.