Comparisons of Protein Dynamics from Experimental Structure Ensembles, Molecular Dynamics Ensembles, and Coarse-Grained Elastic Network Models

Abstract

Predicting protein motions is important for bridging the gap between protein structure and function. With growing numbers of structures of the same or closely related proteins becoming available, it is now possible to understand more about the intrinsic dynamics of a protein with principal component analysis (PCA) of the motions apparent within ensembles of experimental structures. In this paper, we compare the motions extracted from experimental ensembles of 50 different proteins with the modes of motion predicted by several types of coarse-grained elastic network models (ENMs) which additionally take into account more details of either the protein geometry or the amino acid specificity. We further compare the structural variations in the experimental ensembles with the motions sampled in molecular dynamics (MD) simulations for a smaller subset of 17 proteins with available trajectories. We find that the correlations between the motions extracted from MD trajectories and experimental structure ensembles are slightly different than those for the ENMs, possibly reflecting potential sampling biases. We find that there are small gains in the predictive power of the ENMs in reproducing motions present in either experimental or MD ensembles by accounting for the protein geometry rather than the amino acid specificity of the interactions.

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier

“…In spite of this simplicity, it soon became evident that ENMs can not only predict residue fluctuations, but are also capable of guessing with striking precision the directions of large-scale transitions between e.g., X-ray open and closed pairs (Tama and Sanejouand, 2001). Later work has shown that ENMs reproduce as well the flexibility from experimental X-ray and NMR ensembles, or long MD simulations (Rueda et al, 2007; Orellana et al, 2010; Mahajan and Sanejouand, 2015; Sankar et al, 2018) and importantly, track the pathways for conformational change (Orellana et al, 2016; see NM projections, Figure 1C, left). Therefore, ENMs have been at the core of CG-strategies to find transition paths; however, being limited to an equilibrium basin, pathway generation requires iterative deformation along selected NMs, or implementation into some simulation scheme.…”

Advances in coarse-grained modeling of macromolecular complexes

“…For instance, parameter sets for network models have been introduced that delineate different inter-residue coupling forces to reproduce experimental Debye-Waller factors [46]. On the basis of statistical distributions of residue contacts observed in training sets of experimental structures, it is possible to formulate hybrid network/native-contact models that capture pairwise energies [47], vibrational entropies [42,43], relative entropies [48,49], and maintained contacts [50]. In the case of intrinsically disordered proteins, in which native states are largely unknown, large datasets of radius of gyrations were used to parameterize effective CG potentials.…”

Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites

“…30,31 Previous studies showed that these simple models can efficiently capture the functional dynamics of proteins 32,33 and that the global dynamics derived with Elastic Network Model (ENM) shows strong overlap with the motions from atomistic molecular dynamic simulations. 34,35 Some of these dynamical features include mean-square fluctuations of residues and the resilience of residues to external perturbations given by dynamic flexibility index (DFI). 36 For prediction of allosteric residues, we specifically consider the shortest dynamically correlated path between a given residue and the active site residues and the effect of perturbing the active site residues on a given residue.…”

“…In our models, we include features that describe the dynamic behavior of residues in a protein molecule by using elastic network models . Previous studies showed that these simple models can efficiently capture the functional dynamics of proteins and that the global dynamics derived with Elastic Network Model (ENM) shows strong overlap with the motions from atomistic molecular dynamic simulations . Some of these dynamical features include mean‐square fluctuations of residues and the resilience of residues to external perturbations given by dynamic flexibility index (DFI) .…”