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

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and
structure data have radically transformed computational protein design. New methods …

Predicting the effects of mutations in proteins is critical to many applications, from
understanding genetic disease to designing novel proteins to address our most pressing …

Predicting the thermodynamic stability of proteins is a common and widely used step in
protein engineering, and when elucidating the molecular mechanisms behind evolution and …

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 …

Amino acid mutations that lower a protein's thermodynamic stability are implicated in
numerous diseases, and engineered proteins with enhanced stability can be important in …

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 …

Proteins play important roles in biology, biotechnology and pharmacology, and missense
variants are a common cause of disease. Discovering functionally important sites in proteins …

Methods from artificial intelligence (AI) trained on large datasets of sequences and
structures can now" write" proteins with new shapes and molecular functions de novo …

Protein engineering often targets binding pockets or active sites which are enriched in
epistasis—nonadditive interactions between amino acid substitutions—and where the …