Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …

Protein–protein interactions (PPIs) carry out the cellular processes of all living organisms.
Experimental methods for PPI detection suffer from high cost and false-positive rate, hence …

Abstract Protein-Protein Interactions (PPIs) are fundamental means of functions and
signalings in biological systems. The massive growth in demand and cost associated with …

Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …

Peptide-protein interactions are involved in various fundamental cellular functions and their
identification is crucial for designing efficacious peptide therapeutics. Recently, a number of …

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

Spatial structures of proteins are closely related to protein functions. Integrating protein
structures improves the performance of protein–protein interaction (PPI) prediction …

Self-supervised protein language models have proved their effectiveness in learning the
proteins representations. With the increasing computational power, current protein language …

The scientific community is rapidly generating protein sequence information, but only a
fraction of these proteins can be experimentally characterized. While promising deep …

Proteomics has been revolutionized by large protein language models (PLMs), which learn
unsupervised representations from large corpora of sequences. These models are typically …