A CHEMICAL GROUP GRAPH REPRESENTATION FOR EFFICIENT HIGH-THROUGHPUT ANALYSIS OF ATOMISTIC PROTEIN SIMULATIONS

Abstract

Graphs are rapidly becoming a powerful and ubiquitous tool for the analysis of protein structure and for event detection in dynamical protein systems. Despite their rise in popularity, however, the graph representations employed to date have shared certain features and parameters that have not been thoroughly investigated. Here, we examine and compare variations on the construction of graph nodes and graph edges. We propose a graph representation based on chemical groups of similar atoms within a protein rather than residues or secondary structure and find that even very simple analyses using this representation form a powerful event detection system with significant advantages over residue-based graph representations. We additionally compare graph edges based on probability of contact to graph edges based on contact strength and analyses of the entire graph structure to an alternative and more computationally tractable node-based analysis. We develop the simplest useful technique for analyzing protein simulations based on these comparisons and use it to shed light on the speed with which static protein structures adjust to a solvated environment at room temperature in simulation.