Elucidating Elastic Network Model Robustness by Parametrization with Molecular Dynamics

Abstract

Recently, there has been a surge in elastic network model (ENM) parametrizations using molecular dynamics (MD) simulations. These simple, coarse-grained models represent proteins as beads connected by harmonic springs. The motions of this system are then elucidated by normal mode analysis. The goal of these recent parametrizations is to use MD to optimize predicted motions. In this study, we optimize many ENM functional forms using a uniform dataset containing only long MD simulations. Our results show that, across all models tested, residues neighboring in sequence adopt spring constants that are orders of magnitude stiffer than more distal contacts. In addition, the statistical significance of ENM performance varied with model resolution. We also show that fitting long trajectories does not improve ENM performance due to a problem inherent in all network models tested: they underestimate the relative importance of the most concerted motions. Finally, we characterize ENMs resilience to parametrization by tessellating the parameter space. Taken together our data reveals that choice of spring function and parameters are not vital to performance of a network model and that simple parameters can by derived “by hand” when no data is available for fitting, thus illustrating the robustness of the models.