The recent emergence of deep learning to characterize complex patterns of protein big data
reveals its potential to address the classic challenges in the field of protein data mining …

This paper aims to provide a comprehensive review of the trends and challenges of deep
neural networks for protein secondary structure prediction (PSSP). In recent years, deep …

Though many countries have already launched COVID-19 mass vaccination programs to
control the disease outbreak quickly, numerous countries around worldwide are grappling …

Motivation Phosphorylation is one of the most studied post-translational modifications, which
plays a pivotal role in various cellular processes. Recently, deep learning methods have …

Abstract Motivation Protein–protein interactions (PPIs) are central to most biological
processes. However, reliable identification of PPI sites using conventional experimental …

Motivation Predictions of protein backbone torsion angles (ϕ and ψ) and secondary
structure from sequence are crucial subproblems in protein structure prediction. With the …

Predicting compound–protein affinity is beneficial for accelerating drug discovery. Doing so
without the often-unavailable structure data is gaining interest. However, recent progress in …

Interactions between DNA and transcription factors (TFs) play an essential role in
understanding transcriptional regulation mechanisms and gene expression. Due to the large …

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the
past decades, many successful methods have been proposed to address this issue …

Protein secondary structures have been identified as the links in the physical processes of
primary sequences, typically random coils, folding into functional tertiary structures that …