Interpreting Protein Structural Dynamics from NMR Chemical Shifts

In this investigation, semiempirical NMR chemical shift prediction methods are used to evaluate the dynamically averaged values of backbone chemical shifts obtained from unbiased molecular dynamics (MD) simulations of proteins. MD-averaged chemical shift predictions generally improve agreement with experimental values when compared to predictions made from static X-ray structures. Improved chemical shift predictions result from population-weighted sampling of multiple conformational states and from sampling smaller fluctuations within conformational basins. Improved chemical shift predictions also result from discrete changes to conformations observed in X-ray structures, which may result from crystal contacts, and are not always reflective of conformational dynamics in solution. Chemical shifts are sensitive reporters of fluctuations in backbone and side chain torsional angles, and averaged ¹H chemical shifts are particularly sensitive reporters of fluctuations in aromatic ring positions and geometries of hydrogen bonds. In addition, poor predictions of MD-averaged chemical shifts can identify spurious conformations and motions observed in MD simulations that may result from force field deficiencies or insufficient sampling and can also suggest subsets of conformational space that are more consistent with experimental data. These results suggest that the analysis of dynamically averaged NMR chemical shifts from MD simulations can serve as a powerful approach for characterizing protein motions in atomistic detail

Kinetic schemes for substrate binding in one- and two-state proteins.

<p>(A) The kinetic scheme for the interaction of substrate with a two-state handle region, where the open state is the binding-competent state. (B) The kinetic scheme for a single-state handle region, in which the loop is held in a single conformation well-positioned for substrate interactions.</p

Free-energy landscapes for putative major and minor states for ecRNH and ttRNH.

<p>(A) The free-energy diagram constructed under the assumption that both ecRNH and ttRNH share a major state that is incompetent for substrate binding <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003218#pcbi.1003218-Butterwick1" target="_blank">[39]</a>. (B) A revised diagram, inspired by the simulated populations, in which the landscape for ttRNH is mirrored, suggesting that the (closed) minor state of ecRNH is equivalent to the major state of ttRNH, and the (open) major state of ecRNH is equivalent to the minor state of ttRNH.</p

Dynamics of the RNase H handle region as a function of temperature.

<p>(A) Key residues modulating handle region dynamics and their identities in each homolog (soRNH, dark blue; ecRNH, light blue; ctRNH, magenta; ttRNH, red; hsRNH, purple). (B) Representative conformations from the ecRNH trajectory of the open (blue) and closed (brown) states, illustrating the Cartesian distance metric used as a reaction coordinate. (C) Temperature dependence of soRNH (left), ecRNH (middle), and ttRNH (right) handle-region dynamics illustrating the relative populations of the closed and open states at 273K (blue), 300K (black), and 340K (red). Measurements of the distance metric from each crystal structure are shown as green diamonds.</p

Available kinetic measurements for RNase H homologs.

<p>Kinetics data measured under various conditions for soRNH, ecRNH, and ttRNH.</p

Key features of RNase H structure and sequence.

<p>(A) Structural superposition of ecRNH (light blue; PDB ID 2RN2) and ttRNH (red; PDB ID 1RIL). Helices are labeled with green letters and key residues in the handle region and active site (orange arrow) are shown as sticks. (B) Superposition of the ecRNH structure (light blue) with the substrate-bound complex of the hsRNH protein (purple; PDB ID 2QK9), illustrating the position of the handle region interacting with the DNA strand (yellow) of the DNA:RNA hybrid substrate. (C) Sequence alignment of helices B, C, and the handle loop for all five homologs studied.</p

Coupling of handle-region dynamics to residues in the handle region.

<p>(A) Handle-region distance distributions for ecRNH WT (top) and V98A (bottom), illustrating the predominance of the closed state in the mutant. (B) Coupled effects of mutations at positions 95 and 101 in ttRNH (top) and ttRNH dG80 (bottom). Only ttRNH dG80 G95K/R101V shows a population of the open state significantly enriched compared to wild-type ttRNH. (C) Manipulation of relative populations by coupled mutations at positions 95 and 101. For all arginine- or lysine-containing proteins other than soRNH, mutants containing G95 and R101 (brown) populate the closed state more frequently than those containing K95 and V101 (cyan), regardless of the wild-type residues at these positions. The natively N88-containing proteins, ctRNH and hsRNH, both required additional mutations to stabilize the interface between helices B and D, as detailed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003218#pcbi.1003218.s007" target="_blank">Figure S7</a>.</p

Water Dispersion Interactions Strongly Influence Simulated Structural Properties of Disordered Protein States

Many proteins can be partially or completely disordered under physiological conditions. Structural characterization of these disordered states using experimental methods can be challenging, since they are composed of a structurally heterogeneous ensemble of conformations rather than a single dominant conformation. Molecular dynamics (MD) simulations should in principle provide an ideal tool for elucidating the composition and behavior of disordered states at an atomic level of detail. Unfortunately, MD simulations using current physics-based models tend to produce disordered-state ensembles that are structurally too compact relative to experiments. We find that the water models typically used in MD simulations significantly underestimate London dispersion interactions, and speculate that this may be a possible reason for these erroneous results. To test this hypothesis, we create a new water model, TIP4P-D, that approximately corrects for these deficiencies in modeling water dispersion interactions while maintaining compatibility with existing physics-based models. We show that simulations of solvated proteins using this new water model typically result in disordered states that are substantially more expanded and in better agreement with experiment. These results represent a significant step toward extending the range of applicability of MD simulations to include the study of (partially or fully) disordered protein states

Conformational Dynamics of the Partially Disordered Yeast Transcription Factor GCN4

Molecular dynamics (MD) simulations have been employed to study the conformational dynamics of the partially disordered DNA binding basic leucine zipper domain of the yeast transcription factor GCN4. We demonstrate that back-calculated NMR chemical shifts and spin-relaxation data provide complementary probes of the structure and dynamics of disordered protein states and enable comparisons of the accuracy of multiple MD trajectories. In particular, back-calculated chemical shifts provide a sensitive probe of the populations of residual secondary structure elements and helix capping interactions, while spin-relaxation calculations are sensitive to a combination of dynamic and structural factors. Back-calculated chemical shift and spin-relaxation data can be used to evaluate the populations of specific interactions in disordered states and identify regions of the phase space that are inconsistent with experimental measurements. The structural interactions that favor and disfavor helical conformations in the disordered basic region of the GCN4 bZip domain were analyzed in order to assess the implications of the structure and dynamics of the apo form for the DNA binding mechanism. The structural couplings observed in these experimentally validated simulations are consistent with a mechanism where the binding of a preformed helical interface would induce folding in the remainder of the protein, supporting a hybrid conformational selection/induced folding binding mechanism

Characterization of the Conformational Equilibrium between the Two Major Substates of RNase A Using NMR Chemical Shifts

Following the recognition that NMR chemical shifts can be used for protein structure determination, rapid advances have recently been made in methods for extending this strategy for proteins and protein complexes of increasing size and complexity. A remaining major challenge is to develop approaches to exploit the information contained in the chemical shifts about conformational fluctuations in native states of proteins. In this work we show that it is possible to determine an ensemble of conformations representing the free energy surface of RNase A using chemical shifts as replica-averaged restraints in molecular dynamics simulations. Analysis of this surface indicates that chemical shifts can be used to characterize the conformational equilibrium between the two major substates of this protein