The prediction of three-dimensional (3D) protein structure from amino acid sequences has
stood as a significant challenge in computational and structural bioinformatics for decades …

Intrinsically disorder regions (IDRs) are abundant in nature, particularly among Eukaryotes.
While they facilitate a wide spectrum of cellular functions including signaling, molecular …

We present DescribePROT, the database of predicted amino acid-level descriptors of
structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 …

Computational predictions of the intrinsic disorder and its functions are instrumental to
facilitate annotation for the millions of unannotated proteins. However, access to these …

The DescribePROT database of amino acid-level descriptors of protein structures and
functions was substantially expanded since its release in 2020. This expansion includes …

Quality assessment of predicted model structures of proteins is as important as the protein
tertiary structure prediction. A highly efficient quality assessment of predicted model …

Motivation: Sampling structural models and ranking them are the two major challenges of
protein structure prediction. Traditional protein structure prediction methods generally use …

Deep learning methods that achieved great success in predicting intrachain residue-residue
contacts have been applied to predict interchain contacts between proteins. However, these …

Sequence-based prediction of residue–residue contact in proteins becomes increasingly
more important for improving protein structure prediction in the big data era. In this study, we …

ABSTRACT Introduction Molecular recognition features (MoRFs) are regions in protein
sequences that undergo induced folding upon binding partner molecules. MoRFs are …