Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

Directed evolution of stereo‐, regio‐, and chemoselective enzymes constitutes a unique way
to generate biocatalysts for synthetically interesting transformations in organic chemistry and …

To reduce experimental effort associated with directed protein evolution and to explore the
sequence space encoded by mutating multiple positions simultaneously, we incorporate …

Enzymes are nature's catalysts that are designed to accelerate specific reactions up to 106
times with high selectivity. 1 The enormous potential of enzymes as catalysts for organic …

Catalysis has a central role in organic synthetic methodology, especially in stereoselective
reactions. In many reactions, enantioselectivity is made possible through the use of chiral …

Cytokines have pivotal roles in immunity, making them attractive as therapeutics for a variety
of immune-related disorders. However, the widespread clinical use of cytokines has been …

Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …

Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

The design of structurally diverse enzymes is constrained by long-range interactions that are
necessary for accurate folding. We introduce an atomistic and machine learning strategy for …

The field of protein design has made remarkable progress over the past decade. Historically,
the low reliability of purely structure-based design methods limited their application, but …