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 …

Machine learning–based design has gained traction in the sciences, most notably in the
design of small molecules, materials, and proteins, with societal applications ranging from …

The effective design of combinatorial libraries to balance fitness and diversity facilitates the
engineering of useful enzyme functions, particularly those that are poorly characterized or …

Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application.
However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior …

Sequence-function data provides valuable information about the protein functional
landscape but is rarely obtained during directed evolution campaigns. Here, we present …

Natural products and their derivatives are widely used across various industries, particularly
pharmaceuticals. Modern engineered biosynthesis provides an alternative way of producing …

High-throughput (HT) methodologies are extensively applied in synthetic biology for the
rapid enrichment and selection of desired properties from a wide range of genetic diversity …

Consistent introduction of novel enzymes is required for develo** efficient biocatalysts for
challenging biotransformations. Absorbing catalytic modes from organocatalysis may be …

Computational biology involves applying computer science and informatics techniques in
biology to understand complex biological data. It allows us to collect, connect, and analyze …

Various machine learning-assisted directed evolution (MLDE) strategies have been shown
to identify high-fitness protein variants more efficiently than typical wet-lab directed evolution …