Abstract AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind
that can predict three-dimensional (3D) structures of proteins from amino acid sequences …

Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can
cause disease. With recent breakthroughs in structure prediction and a deluge of genomic …

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 …

Predicting the structure of interacting protein chains is a fundamental step towards
understanding protein function. Unfortunately, no computational method can produce …

Cellular functions are governed by molecular machines that assemble through protein-
protein interactions. Their atomic details are critical to studying their molecular mechanisms …

We combine advances in neural language modeling and structurally motivated design to
develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts …

Proteins are essential molecular building blocks of life, responsible for most biological
functions as a result of their specific molecular interactions. However, predicting their …

Residue-residue distance information is useful for predicting tertiary structures of protein
monomers or quaternary structures of protein complexes. Many deep learning methods …

Identification of bacterial protein–protein interactions and predicting the structures of these
complexes could aid in the understanding of pathogenicity mechanisms and developing …

Accurately capturing domain-domain interactions is key to understanding protein function
and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on …