A new approach for extracting information from protein dynamics

TL;DR

This paper introduces an inverse network approach to analyze protein dynamics, providing more robust and interpretable insights into allosteric pathways and dynamical differences across proteins, surpassing traditional correlation-based methods.

Contribution

The study presents a novel inverse approach for constructing protein dynamics networks that avoids structural alignment issues and enhances interpretability and robustness.

Findings

Identifies physically interpretable networks in FimH protein

Detects dynamical differences in structurally similar proteins

Provides biophysical insights into protein allosteric pathways

Abstract

Increased ability to predict protein structures is moving research focus towards understanding protein dynamics. A promising approach is to represent protein dynamics through networks and take advantage of well-developed methods from network science. Most studies build protein dynamics networks from correlation measures, an approach that only works under very specific conditions, instead of the more robust inverse approach. Thus, we apply the inverse approach to the dynamics of protein dihedral angles, a system of internal coordinates, to avoid structural alignment. Using the well-characterized adhesion protein, FimH, we show that our method identifies networks that are physically interpretable, robust, and relevant to the allosteric pathway sites. We further use our approach to detect dynamical differences, despite structural similarity, for Siglec-8 in the immune system, and the SARS-CoV-2 spike protein. Our study demonstrates that using the inverse approach to extract a network from protein dynamics yields important biophysical insights.