Dataset Information

Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive.

ABSTRACT: Atomistic molecular simulations are a powerful way to make quantitative predictions, but the accuracy of these predictions depends entirely on the quality of the force field employed. Although experimental measurements of fundamental physical properties offer a straightforward approach for evaluating force field quality, the bulk of this information has been tied up in formats that are not machine-readable. Compiling benchmark data sets of physical properties from non-machine-readable sources requires substantial human effort and is prone to the accumulation of human errors, hindering the development of reproducible benchmarks of force-field accuracy. Here, we examine the feasibility of benchmarking atomistic force fields against the NIST ThermoML data archive of physicochemical measurements, which aggregates thousands of experimental measurements in a portable, machine-readable, self-annotating IUPAC-standard format. As a proof of concept, we present a detailed benchmark of the generalized Amber small-molecule force field (GAFF) using the AM1-BCC charge model against experimental measurements (specifically, bulk liquid densities and static dielectric constants at ambient pressure) automatically extracted from the archive and discuss the extent of data available for use in larger scale (or continuously performed) benchmarks. The results of even this limited initial benchmark highlight a general problem with fixed-charge force fields in the representation low-dielectric environments, such as those seen in binding cavities or biological membranes.

SUBMITTER: Beauchamp KA

PROVIDER: S-EPMC4667959 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive.

Beauchamp Kyle A KA Behr Julie M JM Rustenburg Ariën S AS Bayly Christopher I CI Kroenlein Kenneth K Chodera John D JD

The journal of physical chemistry. B 20150929 40

Atomistic molecular simulations are a powerful way to make quantitative predictions, but the accuracy of these predictions depends entirely on the quality of the force field employed. Although experimental measurements of fundamental physical properties offer a straightforward approach for evaluating force field quality, the bulk of this information has been tied up in formats that are not machine-readable. Compiling benchmark data sets of physical properties from non-machine-readable sources re ...[more]

PMID: 26339862

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Project description:The high concentration of macromolecules in the crowded cellular interior influences different thermodynamic and kinetic properties of proteins, including their structural stabilities, intermolecular binding affinities and enzymatic rates. Moreover, various structural biology methods, such as NMR or different spectroscopies, typically involve samples with relatively high protein concentration. Due to large sampling requirements, however, the accuracy of classical molecular dynamics (MD) simulations in capturing protein behavior at high concentration still remains largely untested. Here, we use explicit-solvent MD simulations and a total of 6.4 µs of simulated time to study wild-type (folded) and oxidatively damaged (unfolded) forms of villin headpiece at 6 mM and 9.2 mM protein concentration. We first perform an exhaustive set of simulations with multiple protein molecules in the simulation box using GROMOS 45a3 and 54a7 force fields together with different types of electrostatics treatment and solution ionic strengths. Surprisingly, the two villin headpiece variants exhibit similar aggregation behavior, despite the fact that their estimated aggregation propensities markedly differ. Importantly, regardless of the simulation protocol applied, wild-type villin headpiece consistently aggregates even under conditions at which it is experimentally known to be soluble. We demonstrate that aggregation is accompanied by a large decrease in the total potential energy, with not only hydrophobic, but also polar residues and backbone contributing substantially. The same effect is directly observed for two other major atomistic force fields (AMBER99SB-ILDN and CHARMM22-CMAP) as well as indirectly shown for additional two (AMBER94, OPLS-AAL), and is possibly due to a general overestimation of the potential energy of protein-protein interactions at the expense of water-water and water-protein interactions. Overall, our results suggest that current MD force fields may distort the picture of protein behavior in biologically relevant crowded environments.

Dataset Information

Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive.

Publications

Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive.

Similar Datasets

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