Accurate predictions of the three-dimensional structures of proteins from their amino acid
sequences have come of age. AlphaFold, a deep learning-based approach to protein …

The structure, function, and evolution of proteins depend on physical and genetic
interactions among amino acids. Recent studies have used new strategies to explore the …

Quantifying the pathogenicity of protein variants in human disease-related genes would
have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of …

While the vast majority of well-structured single protein chains can now be predicted to high
accuracy due to the recent AlphaFold model, the prediction of multi-chain protein complexes …

INTRODUCTION Protein-protein interactions play critical roles in biology, but the structures
of many eukaryotic protein complexes are unknown, and there are likely many interactions …

Repeated emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
variants with increased fitness underscores the value of rapid detection and characterization …

High‐resolution experimental structural determination of protein–protein interactions has led
to valuable mechanistic insights, yet due to the massive number of interactions and …

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2
and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we …

Abstract The HDOCK server (http://hdock. phys. hust. edu. cn/) is a highly integrated suite of
homology search, template-based modeling, structure prediction, macromolecular docking …

Abstract Machine learning has been increasingly used for protein engineering. However,
because the general sequence contexts they capture are not specific to the protein being …