Many proteins exert their function by switching among different structures. Knowing the
conformational ensembles affiliated with these states is critical to elucidate key mechanistic …

Abstract Information in proteins flows from sequence to structure to function, with each step
causally driven by the preceding one. Protein design is founded on inverting this process …

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 …

Many peptide hormones form an α-helix on binding their receptors,,–, and sensitive methods
for their detection could contribute to better clinical management of disease. De novo protein …

Coronavirus replication is associated with the remodeling of cellular membranes, resulting
in the formation of double-membrane vesicles (DMVs). A DMV-spanning pore was identified …

Protein language models have shown remarkable success in learning biological information
from protein sequences. However, most existing models are limited by either autoencoding …

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 …

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein
structure prediction. However, large protein complexes are still challenging to predict due to …

T cells are essential immune cells responsible for identifying and eliminating pathogens.
Through interactions between their T-cell antigen receptors (TCRs) and antigens presented …