Protein Blocks (PBs) represent a widely used structural alphabet that enables the approximation and analysis of local protein conformations through 16 prototype fragments defined by dihedral angles. Initially developed to overcome the limitations of classical secondary structure definitions, PBs provide a powerful tool for understanding protein structure, dynamics, and function. Their applications span structural annotation, protein fold superimposition and recognition, sequence-based prediction and molecular dynamics analysis. Notably, PBs facilitate the distinction between rigid, flexible, and disordered regions via an entropy-based index (Neq), offering insights into protein flexibility and intrinsic disorder. Their integration with deep learning has dramatically improved predictive performance, and their utility has been demonstrated in diverse contexts such as integrin polymorphisms, VHH variability and AlphaFold structure analysis. As a robust and adaptable framework, PBs remain central in modern structural bioinformatics.
Offmann et al. (Fri,) studied this question.