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 …

The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and
accurate protein structure prediction, powered by deep learning and Rosetta. With the input …

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 …

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 …

We consider the problem of predicting a protein sequence from its backbone atom
coordinates. Machine learning approaches to this problem to date have been limited by the …

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

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 …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …