Residue Network Construction and Predictions of Elastic Network Models

TL;DR

This study systematically analyzes how different residue network construction methods affect elastic network model predictions, revealing that long-distance residual interactions ensure robustness of the predicted protein dynamics.

Contribution

It introduces a comprehensive analysis of network construction strategies and their impact on elastic network model predictions, highlighting the role of residual long-distance interactions.

Findings

Long-distance residual interactions are crucial for robustness.

Different network construction methods yield similar slow mode predictions.

Residual interactions have minimal impact on force vectors.

Abstract

The past decade has witnessed the development and success of coarse-grained network models of proteins for predicting many equilibrium properties related to collective modes of motion. Curiously, the results are usually robust towards the different methodologies used for constructing the residue networks from knowledge of the experimental coordinates. We present a systematical study of network construction strategies, and their effect on the predicted properties. The analysis is based on the radial distribution function and the spectral dimensions of a large set of proteins as well as a newly defined quantity, the angular distribution function. By partitioning the interactions into an essential and a residual set, we show that the robustness originates from a large number of long-distance interactions belonging to the latter. These residuals have a vanishingly small effect on the force vectors on each residue. The overall force balance then translates into the Hessian as small shifts in the slow modes of motion and an invariance of the corresponding eigenvectors. Implications for the study of biologically relevant properties of proteins are discussed.