Using Topology to Estimate Structural Similarities of Proteins

TL;DR

This paper investigates how the topology of hydrogen bonds in proteins can be used to estimate structural similarities, specifically GDT_TS scores, through models and regression techniques, showing promising predictive accuracy.

Contribution

It introduces a novel approach linking hydrogen bond topology to protein structural similarity estimation, with empirical validation.

Findings

Average ΔGDT of 6.45 with 54.5% predictions within 2 ΔGDT for the first method.

Average ΔGDT of 4.41 with 72.7% predictions within 2 ΔGDT for the regression approach.

Demonstrates the potential of topology-based models for protein structure comparison.

Abstract

An effective model for protein structures is important for the study of protein geometry, which, to a large extent, determine the functions of proteins. There are a number of approaches for modelling; one might focus on the conformation of the backbone or H-bonds, and the model may be based on the geometry or the topology of the structure in focus. We focus on the topology of H-bonds in proteins, and explore the link between the topology and the geometry of protein structures. More specifically, we take inspiration from CASP Evaluation of Model Accuracy and investigate the extent to which structural similarities, via GDT_TS, can be estimated from the topology of H-bonds. We report on two experiments; one where we attempt to mimic the computation of GDT_TS based solely on the topology of H-bonds, and the other where we perform linear regression where the independent variables are various scores computed from the topology of H-bonds. We achieved an average $\Delta\text{GDT}$ of 6.45 with 54.5% of predictions inside 2 $\Delta\mathrm{GDT}$ for the first method, and an average $\Delta\mathrm{GDT}$ of 4.41 with 72.7% of predictions inside 2 $\Delta\mathrm{GDT}$ for the second method.