Novel Computational Protein Design Algorithms with Sparse Residue Interaction Graphs, Ensembles, and Mathematical Guarantees, and their Application to Antibody Design
Computational structure-based protein design seeks to harness the incredible biological power of proteins by designing proteins with new structures and even new function. In this dissertation, we present new algorithms to more efficiently search over two models of protein design: design with sparse residue interaction graphs, and design with conformational ensembles. These algorithms build upon existing provable algorithms: they retain all mathematical guarantees of preceding provable methods while providing both efficiency gains and novel theoretical results. Using provable algorithms and the OSPREY protein design software suite we develop and apply protocols to redesign broadly neutralizing antibodies for improved potency and breadth vs. HIV. We retrospectively validate experimentally observed escape mutations to HIV gp120 that reduce binding affinity for the broadly neutralizing antibody CAP256-VRC26.25, and identify mutations predicted to improve both potency and breadth of CAP256-VRC26.25 against HIV.