Accurately predicting peptide secondary structures remains a challenging task due to the
lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep …

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many
domains of society and have recently reached the field of drug discovery. Given the …

Peptides can bind challenging disease targets with high affinity and specificity, offering
enormous opportunities for addressing unmet medical needs. However, peptides' unique …

Lysine crotonylation (Kcr) is a posttranslational modification widely detected in histone and
nonhistone proteins. It plays a vital role in human disease progression and various cellular …

Drug–target interactions (DTIs) are considered a crucial component of drug design and drug
discovery. To date, many computational methods were developed for drug–target …

Fast and accurate structure prediction is essential to the study of peptide function, molecular
targets, and interactions and has been the subject of considerable efforts in the past decade …

Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein
functions and properties. Significant progress has been made in this field in recent years …

The secondary structures (SSs) and supersecondary structures (SSSs) underlie the three-
dimensional structure of proteins. Prediction of the SSs and SSSs from protein sequences …

Peptide-mediated protein-protein interactions (PPIs) play an important role in various
biological processes. The development of peptide-based drugs to modulate PPIs has …

In this review article, we explore the transformative impact of deep learning (DL) on
structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by …