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 (ML), particularly deep learning (DL), has made rapid and substantial
progress in synthetic biology in recent years. Biotechnological applications of biosystems …

Stabilizing proteins is a foundational step in protein engineering. However, the evolutionary
pressure of all extant proteins makes identifying the scarce number of mutations that will …

Protein engineering is essential for altering the substrate scope, catalytic activity and
selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such …

Achieving cost-competitive bio-based processes requires development of stable and
selective biocatalysts. Their realization through in vitro enzyme characterization and …

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for
value-added compound production. To optimize metabolism and reach optimal productivity …

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made
significant progress and provided benefits in the fields of chemistry and material science …

Synthesis of corticosteroids, particularly hydrocortisone, is challenging owing to the complex
network requiring pairing of cytochrome P450s with cytochrome P450 reductase (CPR) for …

Biocatalysis is entering a promising era as a data-driven science. High-throughput
experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw …

Protein engineering through directed evolution and (semi) rational approaches is routinely
applied to optimize protein properties for a broad range of applications in industry and …