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 coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …

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 …

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 …

Protein design holds immense potential for optimizing naturally occurring proteins, with
broad applications in drug discovery, material design, and sustainability. However …

Effective pandemic preparedness relies on anticipating viral mutations that are able to
evade host immune responses to facilitate vaccine and therapeutic design. However, current …

Generative protein language models are a natural way to design new proteins with desired
functions. However, current models are either difficult to direct to produce a protein from a …

Engineering stabilized proteins is a fundamental challenge in the development of industrial
and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph …

Significant research progress has been made in the field of protein structure and fitness
prediction. Particularly, single-sequence-based structure prediction methods like ESMFold …

Stabilizing proteins is a fundamental challenge in protein engineering and is almost always
a prerequisite for the development of industrial and pharmaceutical biotechnologies. Here …