  Protein structural classification (PSC) is a supervised problem of assigning
proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the
proteins' sequence or 3D structural features. We recently proposed PSC
approaches that model protein 3D structures as protein structure networks
(PSNs) and analyze PSN-based protein features, which performed better than or
comparable to state-of-the-art sequence or other 3D structure-based approaches
in the task of PSC. However, existing PSN-based PSC approaches model the whole
3D structure of a protein as a static PSN. Because folding of a protein is a
dynamic process, where some parts of a protein fold before others, modeling the
3D structure of a protein as a dynamic PSN might further help improve the
existing PSC performance. Here, we propose for the first time a way to model 3D
structures of proteins as dynamic PSNs, with the hypothesis that this will
improve upon the current state-of-the-art PSC approaches that are based on
static PSNs (and thus upon the existing state-of-the-art sequence and other 3D
structural approaches). Indeed, we confirm this on 71 datasets spanning ~44,000
protein domains from CATH and SCOPe
