Characterizing protein motions from structure

Abstract

To clarify the extent structure plays in determining protein dynamics, a comparative study is made using three models that characterize native state dynamics of single domain proteins starting from known structures taken from four distinct SCOP classifications. A geometrical simulation using the Framework Rigidity Optimized Dynamics Algorithm (FRODA) based on rigid cluster decomposition is compared to the commonly employed elastic network model (specifically the Anisotropic Network Model ANM) and molecular dynamics (MD) simulation. The essential dynamics are quantified by a mode subspace constructed from ANM and a principal component analysis (PCA) on FRODA and MD trajectories. Aggregate conformational ensembles are constructed to provide a basis for quantitative comparisons between FRODA runs using different parameter settings to critically assess how the predictions of essential dynamics depend on a priori arbitrary user-defined distance constraint rules. We established a range of physicality for these parameters. Surprisingly, FRODA maintains greater intra-consistent results than obtained from MD trajectories, comparable to ANM. Additionally, a mode subspace is constructed from PCA on an exemplar set of myoglobin structures from the Protein Data Bank. Significant overlap across the three model subspaces and the experimentally derived subspace is found. While FRODA provides the most robust sampling and characterization of the native basin, all three models give similar dynamical information of a native state, further demonstrating that structure is the key determinant of dynamics.

Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins

“…For this purpose, details about the setup of the various simulations are ignored, except when it pertains to methodology. Additional details can be found in [16]. The MD trajectory consists of 2,000 frames after equilibration.…”

“…In the ENM, one typically considers nearby alpha carbon atoms to interact harmonically, where the connectivity is determined from a single structure to extract an elastic network. Typically, the large-scale motions quantified by a small set of lowest frequency modes of vibration are in good agreement with the same corresponding number of PCA modes when direct comparisons of subspaces are made [16–18]. One advantage of performing PCA to obtain the ED of a protein is that information from any selected set of atoms can be used to obtain the PCA modes associated with that subspace.…”

Constrained geometric dynamics of the Fenna–Matthews–Olson complex: the role of correlated motion in reducing uncertainty in excitation energy transfer

“…Here, to generate rigid cluster decompositions, the hydrogen bond cut-off energy (E cut ) was set to −4.6 kcal/mol. The principal component subspaces spanned by FRODA simulations have been shown to be very robust with respect to the chosen value of E cut (David and Jacobs, 2011).…”

Impact of intracellular domain flexibility upon properties of activated human 5‐HT3 receptors

“…To generate rigid cluster decompositions, the hydrogen bond energy cut‐off (Ecut) was set to −1.0 kcal·mol −1 and the ‘H3’ hydrophobic tether scheme was employed. The principal component subspaces spanned by FRODA simulations have been shown to be very robust with respect to chosen value of Ecut (David and Jacobs, ); we have repeated our simulations with an Ecut value of −0.6 kcal·mol −1 , observing no difference in qualitative behaviour (Supporting Information http://onlinelibrary.wiley.com/doi/10.1111/bph.12536/suppinfo).…”