Building Water Models: A Different Approach

Izadi, Shahrokh; Anandakrishnan, Ramu; Onufriev, Alexey V.

doi:10.1021/jz501780a

Cited by 889 publications

(1,069 citation statements)

References 65 publications

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“…The first 100 ns of each trajectory were disregarded in these calculations. The robustness of the spermine distributions to the choice of water model was confirmed using the OPC water model (34).…”

Section: All-atom MD Simulationsmentioning

confidence: 82%

Spermine Condenses DNA, but Not RNA Duplexes

Katz

Tolokh

Pabit

et al. 2017

Biophysical Journal

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Interactions between the polyamine spermine and nucleic acids drive important cellular processes. Spermine condenses DNA and some RNAs, such as poly(rA):poly(rU). A large fraction of the spermine present in cells is bound to RNA but apparently does not condense it. Here, we study the effect of spermine binding to short duplex RNA and DNA, and compare our findings with predictions of molecular-dynamics simulations. When small numbers of spermine are introduced, RNA with a designed sequence containing a mixture of 14 GC pairs and 11 AU pairs resists condensation relative to DNA of an equivalent sequence or to 25 bp poly(rA):poly(rU) RNA. A comparison of wide-angle x-ray scattering profiles with simulation results suggests that spermine is sequestered deep within the major groove of mixed-sequence RNA. This prevents condensation by limiting opportunities to bridge to other molecules and stabilizes the RNA by locking it into a particular conformation. In contrast, for DNA, simulations suggest that spermine binds externally to the duplex, offering opportunities for intermolecular interaction. The goal of this study is to explain how RNA can remain soluble and available for interaction with other molecules in the cell despite the presence of spermine at concentrations high enough to precipitate DNA.

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Section: All-atom MD Simulationsmentioning

confidence: 82%

Spermine Condenses DNA, but Not RNA Duplexes

Katz

Tolokh

Pabit

et al. 2017

Biophysical Journal

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“…MD1 combined the OPC water model, 92 RESP partial charges via the R.E.D. server, 93 and the new GAFF2 force field for bonded and Lennard-Jones parameters.…”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

HYDROPHOBE Challenge: A Joint Experimental and Computational Study on the Host–Guest Binding of Hydrocarbons to Cucurbiturils, Allowing Explicit Evaluation of Guest Hydration Free-Energy Contributions

et al. 2017

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The host-guest complexation of hydrocarbons (22 guest molecules) with cucurbit[7]uril (CB7) was investigated in aqueous solution. Association constants were determined by using the indicator displacement strategy, which allows binding constant determinations also for poorly water-soluble (hydrophobic) guests. The binding constants (103–109 M−1) increased with the size of the hydrocarbon, pointing to the hydrophobic effect and dispersion interactions as driving forces. Besides potential applications for the sensing and separation of hydrocarbons, the measured affinities provide unique benchmark data for the binding of neutral guest molecules. Consequently, a computational blind challenge, the HYDROPHOBE challenge, was conducted in order to allow a comparison with state-of-the-art computational methods for predicting host-guest affinity constants. In total, 5 computational data sets were submitted, which allowed the comparison of experimental binding constants with those predicted by coupled-cluster theory (DLPNO-CCSD(T)), dispersion-corrected density functional theory (DFT), and explicit solvent molecular dynamics (MD) simulations parameterized with two different force field combinations from the AMBER simulation package. All submissions were capable of predicting the general binding trend, with a slightly better correlation for the MD compared to the quantum-chemical (QM) data sets (R2MD = 0.80 vs R2QM = 0.66, average values for the submitted data sets). On the other hand, QM calculations showed better predictions for the absolute values of the binding affinities as reflected by the mean signed errors (4.3 kcal mol−1 for MD vs 1.8 kcal mol−1 for QM). When searching for sources of uncertainty in predicting the host-guest affinities, the experimentally known hydration energies of the investigated hydrocarbons could be employed, which provided a distinct advantage of the HYDROPHOBE challenge. The comparison with the employed solvation models (explicit solvent for MD and COSMO-RS for QM) confirmed a good correlation for both methods, but revealed a rather constant offset of the COSMO data, by ca. +2 kcal mol−1, which was traced back to a required reference-state correction in the QM submissions (2.38 kcal mol−1). Introduction of the reference-state correction improved the predictive power of the QM methods, particularly for small hydrocarbons up to C5. The correlations of both QM and MD submissions also exposed specific outliers, which could be due to peculiarities of the investigated guests, for example, different degrees of conformational changes upon complexation, such as helical structures of the longer n-alkyl chains within the cavity. The latter was confirmed by 2D NMR experiments and both the MD as well as QM calculations.

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“…For example, the collision frequency of 10.0 ps À1 , or even 50.0 ps À1 , only crudely approximates that of water. And TIP3P itself is~2.4 times faster than real water as measured by its self-diffusion rate (96).…”

Section: Mixed Casementioning

confidence: 96%

Speed of Conformational Change: Comparing Explicit and Implicit Solvent Molecular Dynamics Simulations

Anandakrishnan

Drozdetski

Walker

et al. 2015

Biophysical Journal

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172

192

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Adequate sampling of conformation space remains challenging in atomistic simulations, especially if the solvent is treated explicitly. Implicit-solvent simulations can speed up conformational sampling significantly. We compare the speed of conformational sampling between two commonly used methods of each class: the explicit-solvent particle mesh Ewald (PME) with TIP3P water model and a popular generalized Born (GB) implicit-solvent model, as implemented in the AMBER package. We systematically investigate small (dihedral angle flips in a protein), large (nucleosome tail collapse and DNA unwrapping), and mixed (folding of a miniprotein) conformational changes, with nominal simulation times ranging from nanoseconds to microseconds depending on system size. The speedups in conformational sampling for GB relative to PME simulations, are highly system- and problem-dependent. Where the simulation temperatures for PME and GB are the same, the corresponding speedups are approximately onefold (small conformational changes), between ∼1- and ∼100-fold (large changes), and approximately sevenfold (mixed case). The effects of temperature on speedup and free-energy landscapes, which may differ substantially between the solvent models, are discussed in detail for the case of miniprotein folding. In addition to speeding up conformational sampling, due to algorithmic differences, the implicit solvent model can be computationally faster for small systems or slower for large systems, depending on the number of solute and solvent atoms. For the conformational changes considered here, the combined speedups are approximately twofold, ∼1- to 60-fold, and ∼50-fold, respectively, in the low solvent viscosity regime afforded by the implicit solvent. For all the systems studied, 1) conformational sampling speedup increases as Langevin collision frequency (effective viscosity) decreases; and 2) conformational sampling speedup is mainly due to reduction in solvent viscosity rather than possible differences in free-energy landscapes between the solvent models.

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Building Water Models: A Different Approach

Cited by 889 publications

References 65 publications

Spermine Condenses DNA, but Not RNA Duplexes

Spermine Condenses DNA, but Not RNA Duplexes

HYDROPHOBE Challenge: A Joint Experimental and Computational Study on the Host–Guest Binding of Hydrocarbons to Cucurbiturils, Allowing Explicit Evaluation of Guest Hydration Free-Energy Contributions

Speed of Conformational Change: Comparing Explicit and Implicit Solvent Molecular Dynamics Simulations

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