Protein-based therapeutics have led to new paradigms in disease treatment. Projected to be
half of the top ten selling drugs in 2023, proteins have emerged as rivaling and, in some …

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

There has been considerable recent progress in designing new proteins using deep-
learning methods,,,,,,,–. Despite this progress, a general deep-learning framework for protein …

Three billion years of evolution has produced a tremendous diversity of protein molecules,
but the full potential of proteins is likely to be much greater. Accessing this potential has …

De novo enzyme design has sought to introduce active sites and substrate-binding pockets
that are predicted to catalyse a reaction of interest into geometrically compatible native …

Although deep learning has revolutionized protein structure prediction, almost all
experimentally characterized de novo protein designs have been generated using …

Deep-learning methods have revolutionized protein structure prediction and design but are
presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which …

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 …

Physical interactions between proteins are essential for most biological processes
governing life. However, the molecular determinants of such interactions have been …

Construction of a scaffold structure that supports a desired motif, conferring protein function,
shows promise for the design of vaccines and enzymes. But a general solution to this motif …