Kiera H. Sumida

This study designs novel serine hydrolases with high catalytic efficiency using computational methods, achieving structures close to design models and distinct from natural enzymes.

The paper uses ProteinMPNN to stabilize Fe(II)/KG enzymes, enabling more effective directed evolution of new catalytic functions.

This paper introduces ProteinMPNN, a deep learning model that improves protein expression, stability, and function by incorporating evolutionary and structural information.