Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …

Directed evolution aims to expedite the natural evolution process of biological molecules
and systems in a test tube through iterative rounds of gene diversifications and library …

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

The binding and catalytic functions of proteins are generally mediated by a small number of
functional residues held in place by the overall protein structure. Here, we describe deep …

Signal peptides (SPs) are short amino acid sequences that control protein secretion and
translocation in all living organisms. SPs can be predicted from sequence data, but existing …

Natural evolution must explore a vast landscape of possible sequences for desirable yet
rare mutations, suggesting that learning from natural evolutionary strategies could guide …

Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function …

Advances in DNA sequencing and machine learning are providing insights into protein
sequences and structures on an enormous scale. However, the energetics driving folding …

Enzymatic deconstruction of poly (ethylene terephthalate)(PET) is under intense
investigation, given the ability of hydrolase enzymes to depolymerize PET to its constituent …

Learning effective protein representations is critical in a variety of tasks in biology such as
predicting protein function or structure. Existing approaches usually pretrain protein …