Post date: Aug 21, 2015 5:48:27 PM
In press on JCTC http://pubs.acs.org/doi/abs/10.1021/acs.jctc.5b00512
A (dis)similarity index to compare correlated motions in molecular simulations
Matteo Tiberti, Gaetano Invernizzi, Elena Papaleo
Molecular dynamics (MD) simulations are widely used to complement or guide experimental studies in the characterization of protein dynamics, thanks to both the improvement in force-field accuracy and in the software and hardware to sample the conformational landscape of proteins. Among the different applications of MD simulations, the study of correlated motions is largely employed for different purposes and several metrics have been developed to describe correlated motions in MD ensemble, such as methods based on Pearson Correlation or Mutual Information. Cross-correlation analysis of MD trajectories is indeed appealing not only to identify residues characterized by coupled fluctuations in protein structures but also since it can be used to extrapolate motions along directions in which major conformational changes should occur, for example on longer timescales than the one that are actually simulated. Nevertheless, most of the MD studies employ average correlation maps and mostly in a qualitative way, even when different systems or different replicates of the same system are compared. The broad application of correlation metrics in the analysis of MD simulations and especially for comparative purposes requires a step forward toward more quantitative and accurate comparisons. We thus here employed a simple but effective index, which is based on a normalize Frobenius norm of the differences between protein correlation maps, to compare correlation motions. We applied this index for a quantitative comparison of correlated motions from MD simulations of seven proteins of different size and fold. We also employed the index to assess the robustness of correlation description when multi-replicate MD simulations of a same system are used and we compared it to metrics for comparison of structural ensembles such as Root Mean Square Inner Product and the Bhattacharyya coefficient.