In this investigation, semi-empirical 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 mad 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.
The A1-42 peptide that is overproduced in Alzheimer's disease (AD) from a large precursor protein has a normal amino acid sequence but, when liberated, misfolds at neutral pH to form ''protofibrils'' and fibrils that are rich in -sheets. We find that these protofibrils or fibrils are toxic to certain neuronal cells that carry Ca-permeant ␣-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors. Disrupting the structure of the A1-42 fibrils and protofibrils might lead to the discovery of molecules that would be very useful in the treatment of AD. A high-throughput screen of a library of >3,000 small molecules with known ''biological activity'' was set up to find compounds that efficiently decrease the -sheet content of aggregating A1-42. Lead compounds were characterized by using thioflavin T (ThT) as a -sheet assay. The most effective of six compounds found was 4,5-dianilinophthalimide (DAPH) under the following conditions: DAPH at low micromolar concentrations abolishes or greatly reduces previously existing fully formed A1-42 fibrils, producing instead amorphous materials without fibrils but apparently containing some protofibrils and smaller forms. Coincubation of the A1-42 peptide with DAPH produces either amorphous materials or empty fields. Coincubation of DAPH and A1-42 greatly reduces the -sheet content, as measured with ThT fluorescence, and produces a novel fluorescent complex with ThT. When the A1-42 peptide was coincubated with DAPH at very low micromolar concentrations, the neuronal toxicity mentioned above (Ca 2؉ influx) was eliminated. Clearly, DAPH is a promising candidate for AD therapy.Ca ions ͉ amyloid peptide ͉ aggregation ͉ toxicity ͉ aggregation
Events of scientific interest in molecular dynamics (MD) simulations, including conformational changes, folding transitions, and translocations of ligands and reaction products, often correspond to high-level structural rearrangements that alter contacts between molecules or among different parts of a molecule. Due to advances in computer architecture and software, MD trajectories representing such structure-changing events have become easier to generate, but the length of these trajectories poses a challenge to scientific interpretation and analysis. In this paper, we present automated methods for the detection of potentially important structure-changing events in long MD trajectories. In contrast with traditional tools for the analysis of such trajectories, our methods provide a detailed report of broken and formed contacts that aids in the identification of specific time-dependent side-chain interactions. Our approach employs a coarse-grained representation of amino acid side chains, a contact metric based on higher order generalizations of Delaunay tetrahedralization, techniques for detecting significant shifts in the resulting contact time series, and a new kernel-based measure of contact alteration activity. The analysis methods we describe are incorporated in a newly developed package, called TimeScapes, which is freely available and compatible with trajectories generated by a variety of popular MD programs. Tests based on actual microsecond time scale simulations demonstrate that the package can be used to efficiently detect and characterize important conformational changes in realistic protein systems.
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.
Abstract:Since the behavior of biomolecules can be sensitive to temperature, the ability to accurately calculate and control the temperature in molecular dynamics (MD) simulations is important. Standard analysis of equilibrium MD simulationsseven constant-energy simulations with negligible long-term energy driftsoften yields different calculated temperatures for different motions, however, in apparent violation of the statistical mechanical principle of equipartition of energy. Although such analysis provides a valuable warning that other simulation artifacts may exist, it leaves the actual value of the temperature uncertain. We observe that Tolman's generalized equipartition theorem should hold for long stable simulations performed using velocity-Verlet or other symplectic integrators, because the simulated trajectory is thought to sample almost exactly from a continuous trajectory generated by a shadow Hamiltonian. From this we conclude that all motions should share a single simulation temperature, and we provide a new temperature estimator that we test numerically in simulations of a diatomic fluid and of a solvated protein. Apparent temperature variations between different motions observed using standard estimators do indeed disappear when using the new estimator. We use our estimator to better understand how thermostats and barostats can exacerbate integration errors. In particular, we find that with large (albeit widely used) time steps, the common practice of using two thermostats to remedy so-called hot solvent-cold solute problems can have the counterintuitive effect of causing temperature imbalances. Our results, moreover, highlight the utility of multiple-time step integrators for accurate and efficient simulation.
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