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

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 …

Deep-learning language models have shown promise in various biotechnological
applications, including protein design and engineering. Here we describe ProGen, a …

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 …

Attention-based models trained on protein sequences have demonstrated incredible
success at classification and generation tasks relevant for artificial-intelligence-driven …

The ability to design efficient enzymes from scratch would have a profound effect on
chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past …

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

Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

Abstract Machine learning-based models of protein fitness typically learn from either
unlabeled, evolutionarily related sequences or variant sequences with experimentally …

Learning the design patterns of proteins from sequences across evolution may have
promise toward generative protein design. However it is unknown whether language …