Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks

Abstract

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.

Keywords: dynamic residue interaction networks; graph theory; molecular dynamics simulations; network analysis; protein structure networks.

FIGURE 1

Networks produced on wild-type KDM6A trajectory data using three different methods. Tool-specific betweenness values were mapped onto the corresponding protein representation using a color scale ranging from blue (low) to red (high). (A) NAPS Ensemble network with C_α distances as edges: C_α-representation of the KDM6A protein with, in the box, an example of a network downloadable from the web-server. (B) “Spacefill” representation of the KDM6A protein with contacts computed by MD-TASK using C_β distances. (C) Left: dynamic residue interaction network obtained using RING, with residue-number color scheme. Right: Cartoon representation of all-atom KDM6A protein.