26 research outputs found
Developing a molecular dynamics force field for both folded and disordered protein states
Molecular dynamics (MD) simulation is a valuable tool for characterizing the structural dynamics of folded proteins and should be similarly applicable to disordered proteins and proteins with both folded and disordered regions. It has been unclear, however, whether any physical model (force field) used in MD simulations accurately describes both folded and disordered proteins. Here, we select a benchmark set of 21 systems, including folded and disordered proteins, simulate these systems with six state-of-the-art force fields, and compare the results to over 9,000 available experimental data points. We find that none of the tested force fields simultaneously provided accurate descriptions of folded proteins, of the dimensions of disordered proteins, and of the secondary structure propensities of disordered proteins. Guided by simulation results on a subset of our benchmark, however, we modified parameters of one force field, achieving excellent agreement with experiment for disordered proteins, while maintaining state-of-the-art accuracy for folded proteins. The resulting force field, a99SB-disp, should thus greatly expand the range of biological systems amenable to MD simulation. A similar approach could be taken to improve other force fields
Thermal Adaptation of Conformational Dynamics in Ribonuclease H
The relationship between inherent internal conformational processes and enzymatic activity or thermodynamic stability of proteins has proven difficult to characterize. The study of homologous proteins with differing thermostabilities offers an especially useful approach for understanding the functional aspects of conformational dynamics. In particular, ribonuclease HI (RNase H), an 18 kD globular protein that hydrolyzes the RNA strand of RNA:DNA hybrid substrates, has been extensively studied by NMR spectroscopy to characterize the differences in dynamics between homologs from the mesophilic organism E. coli and the thermophilic organism T. thermophilus. Herein, molecular dynamics simulations are reported for five homologous RNase H proteins of varying thermostabilities and enzymatic activities from organisms of markedly different preferred growth temperatures. For the E. coli and T. thermophilus proteins, strong agreement is obtained between simulated and experimental values for NMR order parameters and for dynamically averaged chemical shifts, suggesting that these simulations can be a productive platform for predicting the effects of individual amino acid residues on dynamic behavior. Analyses of the simulations reveal that a single residue differentiates between two different and otherwise conserved dynamic processes in a region of the protein known to form part of the substrate-binding interface. Additional key residues within these two categories are identified through the temperature-dependence of these conformational processes
A practical guide to the simultaneous determination of protein structure and dynamics using metainference
Accurate protein structural ensembles can be determined with metainference, a
Bayesian inference method that integrates experimental information with prior
knowledge of the system and deals with all sources of uncertainty and errors as
well as with system heterogeneity. Furthermore, metainference can be
implemented using the metadynamics approach, which enables the computational
study of complex biological systems requiring extensive conformational
sampling. In this chapter, we provide a step-by-step guide to perform and
analyse metadynamic metainference simulations using the ISDB module of the
open-source PLUMED library, as well as a series of practical tips to avoid
common mistakes. Specifically, we will guide the reader in the process of
learning how to model the structural ensemble of a small disordered peptide by
combining state-of-the-art molecular mechanics force fields with nuclear
magnetic resonance data, including chemical shifts, scalar couplings and
residual dipolar couplings.Comment: 49 pages, 9 figure
Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding
Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into “structurally similar” clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task, as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and α-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered substates within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information, provide interpretable visualizations of the conformational heterogeneity within each ensemble, and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs
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 <sup>1</sup>H 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
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