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Validating Molecular Dynamics Simulations against Experimental Observables in Light of Underlying Conformational Ensembles

Cite this: J. Phys. Chem. B 2018, 122, 26, 6673–6689
Publication Date (Web):June 4, 2018
https://doi.org/10.1021/acs.jpcb.8b02144
Copyright © 2018 American Chemical Society

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    Abstract

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    Far from the static, idealized conformations deposited into structural databases, proteins are highly dynamic molecules that undergo conformational changes on temporal and spatial scales that may span several orders of magnitude. These conformational changes, often intimately connected to the functional roles that proteins play, may be obscured by traditional biophysical techniques. Over the past 40 years, molecular dynamics (MD) simulations have complemented these techniques by providing the “hidden” atomistic details that underlie protein dynamics. However, there are limitations of the degree to which molecular simulations accurately and quantitatively describe protein motions. Here we show that although four molecular dynamics simulation packages (AMBER, GROMACS, NAMD, and ilmm) reproduced a variety of experimental observables for two different proteins (engrailed homeodomain and RNase H) equally well overall at room temperature, there were subtle differences in the underlying conformational distributions and the extent of conformational sampling obtained. This leads to ambiguity about which results are correct, as experiment cannot always provide the necessary detailed information to distinguish between the underlying conformational ensembles. However, the results with different packages diverged more when considering larger amplitude motion, for example, the thermal unfolding process and conformational states sampled, with some packages failing to allow the protein to unfold at high temperature or providing results at odds with experiment. While most differences between MD simulations performed with different packages are attributed to the force fields themselves, there are many other factors that influence the outcome, including the water model, algorithms that constrain motion, how atomic interactions are handled, and the simulation ensemble employed. Here four different MD packages were tested each using best practices as established by the developers, utilizing three different protein force fields and three different water models. Differences between the simulated protein behavior using two different packages but the same force field, as well as two different packages with different force fields but the same water models and approaches to restraining motion, show how other factors can influence the behavior, and it is incorrect to place all the blame for deviations and errors on force fields or to expect improvements in force fields alone to solve such problems.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.8b02144.

    • Hydrogen bond occupancy data, NOE satisfaction data, rotamer populations, rotamer lifetimes, comparison of parameter sets used with the Karplus relation, tabulation of χ2 results by data type, S-value analysis, Cα RMSD details, Cα RMSF details, quantification of the effect of ensemble subsampling on chemical shift agreement, comparison of MD-derived and experimental chemical shifts, detailed analyses of chemical shift agreements and coupling constant agreements, details regarding agreement between MD-derived and experimental order parameters, comparison between the experimental and MD-derived amide S2 order parameters, plots of the distribution of errors between MD and experiment for data associated with the χ2 calculations, and an example of the conformational clustering method used to identify transition state ensembles from high-temperature unfolding simulations (PDF)

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