Dynamic network analysis improves protein 3D structural classification

TL;DR

This paper introduces a novel dynamic protein structure network approach for protein structural classification, leveraging the protein folding process to enhance accuracy over static models.

Contribution

It is the first to model protein 3D structures as dynamic networks, improving classification performance over static network methods.

Findings

Dynamic PSNs outperform static PSNs in classification accuracy.

The approach is validated on 71 datasets with ~44,000 protein domains.

Results show significant improvement over existing methods.

Abstract

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