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

Protein semisynthesis—defined herein as the assembly of a protein from a combination of
synthetic and recombinant fragments—is a burgeoning field of chemical biology that has …

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 …

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 …

Deep learning generative approaches provide an opportunity to broadly explore protein
structure space beyond the sequences and structures of natural proteins. Here, we use deep …

Natural proteins are highly optimized for function but are often difficult to produce at a scale
suitable for biotechnological applications due to poor expression in heterologous systems …

Protein denoising diffusion probabilistic models are used for the de novo generation of
protein backbones but are limited in their ability to guide generation of proteins with …

Allosteric modulation of protein function, wherein the binding of an effector to a protein
triggers conformational changes at distant functional sites, plays a central part in the control …

We describe an approach for designing high-affinity small molecule–binding proteins poised
for downstream sensing. We use deep learning–generated pseudocycles with repeating …

Asymmetric multiprotein complexes that undergo subunit exchange play central roles in
biology but present a challenge for design because the components must not only contain …