Most proteins in cells are composed of multiple folding units (or domains) to perform
complex functions in a cooperative manner. Relative to the rapid progress in single-domain …

Three-dimensional protein structure is directly correlated with its function and its
determination is critical to understanding biological processes and addressing human …

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 …

Protein–RNA and protein–DNA complexes play critical roles in biology. Despite
considerable recent advances in protein structure prediction, the prediction of the structures …

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 …

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

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

Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and …

RNA 3D structure prediction is a long-standing challenge. Inspired by the recent
breakthrough in protein structure prediction, we developed trRosettaRNA, an automated …

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 …