David B. Stoll

This study modifies de novo α-helical barrel biocatalysts using deep learning and computational methods to improve solubility and catalytic performance while maintaining structural stability.

This paper introduces Riff-Diff, a machine learning and atomistic modeling method for designing enzymes with high catalytic activity and stereoselectivity, demonstrating its effectiveness for two chemical reactions.

The paper designs and characterizes a thermostable α-helical barrel protein with tunable active-site positioning, showing improved catalytic efficiency.