C(α) torsion angles as a flexible criterion to extract secrets from a molecular dynamics simulation.

Abstract

Given the increasing complexity of simulated molecular systems, and the fact that simulation times have now reached milliseconds to seconds, immense amounts of data (in the gigabyte to terabyte range) are produced in current molecular dynamics simulations. Manual analysis of these data is a very time-consuming task, and important events that lead from one intermediate structure to another can become occluded in the noise resulting from random thermal fluctuations. To overcome these problems and facilitate a semi-automated data analysis, we introduce in this work a measure based on C(α) torsion angles: torsion angles formed by four consecutive C(α) atoms. This measure describes changes in the backbones of large systems on a residual length scale (i.e., a small number of residues at a time). Cluster analysis of individual C(α) torsion angles and its fuzzification led to continuous time patches representing (meta)stable conformations and to the identification of events acting as transitions between these conformations. The importance of a change in torsion angle to structural integrity is assessed by comparing this change to the average fluctuations in the same torsion angle over the complete simulation. Using this novel measure in combination with other measures such as the root mean square deviation (RMSD) and time series of distance measures, we performed an in-depth analysis of a simulation of the open form of DNA polymerase I. The times at which major conformational changes occur and the most important parts of the molecule and their interrelations were pinpointed in this analysis. The simultaneous determination of the time points and localizations of major events is a significant advantage of the new bottom-up approach presented here, as compared to many other (top-down) approaches in which only the similarity of the complete structure is analyzed.