Evaluating the London Dispersion Coefficients of Protein Force Fields Using the Exchange-Hole Dipole Moment Model

Walters, Evan T.; Mohebifar, Mohamad; Johnson, Erin R.; Rowley, Christopher N.

doi:10.1021/acs.jpcb.8b02814

Cited by 39 publications

(40 citation statements)

References 92 publications

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“…100,101 It may be necessary to consider long-range dispersion interactions explicitly 37,38,55,102 or to include higher-order terms of the London dispersion forces to obtain more accurate simulations. 103 However, to study the effect of crowding without altering the van der Waals parameters, using a model with higher-order dispersion coefficients 103 or a polarizable force field 104,105 might give results closer to experimental values, even though conventional force field improvement is still on-going as well. 106…”

Section: Discussionmentioning

confidence: 99%

Rotational and Translational Diffusion of Proteins as a Function of Concentration

et al. 2019

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Atomistic simulations of three different proteins at different concentrations are performed to obtain insight into protein mobility as a function of protein concentration. We report on simulations of proteins from diluted to the physiological water concentration (about 70% of the mass). First, the viscosity was computed and found to increase by a factor of 7–9 going from pure water to the highest protein concentration, in excellent agreement with in vivo nuclear magnetic resonance results. At a physiological concentration of proteins, the translational diffusion is found to be slowed down to about 30% of the in vitro values. The slow-down of diffusion found here using atomistic models is slightly more than that of a hard sphere model that neglects the electrostatic interactions. Interestingly, rotational diffusion of proteins is slowed down somewhat more (by about 80–95% compared to in vitro values) than translational diffusion, in line with experimental findings and consistent with the increased viscosity. The finding that rotation is retarded more than translation is attributed to solvent-separated clustering. No direct interactions between the proteins are found, and the clustering can likely be attributed to dispersion interactions that are stronger between proteins than between protein and water. Based on these simulations, we can also conclude that the internal dynamics of the proteins in our study are affected only marginally under crowding conditions, and the proteins become somewhat more stable at higher concentrations. Simulations were performed using a force field that was tuned for dealing with crowding conditions by strengthening the protein–water interactions. This force field seems to lead to a reproducible partial unfolding of an α-helix in one of the proteins, an effect that was not observed in the unmodified force field.

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Section: Discussionmentioning

confidence: 99%

Rotational and Translational Diffusion of Proteins as a Function of Concentration

et al. 2019

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“…In particular, the balance between the MM ligandwater, ligand-protein, and the NNP ligand intramolecular dispersion interactions will not necessarily be consistent. 3,4 This issue has been addressed in some QM/MM models by dening new parameters for the QM-MM Lennard-Jones terms. 40,41 Nevertheless, the common practice has been to parameterize the intramolecular terms of ligands to gas phase potential energy surfaces, so the ANI-1ccX should a suitable replacement for these terms.…”

Section: Conformational Free Energiesmentioning

confidence: 99%

“…Development of accurate models of potential energy terms of protein-ligand binding and their optimal parameters is a longstanding objective in computational chemistry. The electrostatic, 1,2 repulsive, and dispersion 3,4 interaction terms have been developed actively; however, accurate representation of intramolecular potential energy of the ligand is particularly challenging and no complete, general solution has been developed. Current force elds approximate intramolecular forces using simple but generally effective terms that were introduced more than 50 years ago, 5 where bond angles and stretches are described with harmonic potentials (i.e., spring-like) and torsional barriers are dened as the sum of a handful of cosine functions.…”

Section: Introductionmentioning

confidence: 99%

Simulating protein–ligand binding with neural network potentials

2020

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“…This is expected since the C6 term in the OPLS forcefield must effectively compensate for higher-order dispersion (C8, C10, ...) terms that are not explicitly included in the OPLS force-field. 155 The high value of C6 on the backbone carbonyl carbon (C (bb)) has been noted previously, 3 and should be revisited in future force fields. Table 16 compares the three kinds of MCLF polarizabilities (i.e., force-field, static, and low freq) to parameters used in the AMOEBA 14,139 polarizable force field.…”

Section: Large Biomoleculementioning

confidence: 82%

Introducing DDEC6 Atomic Population Analysis: Part 5. New Method to Compute Polarizabilities and Dispersion Coefficients

Manz

Chen²,

Cole³

et al. 2018

Preprint

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Polarizabilities and London dispersion forces are important to many chemical processes. Leading terms in these forces are often modeled using polarizabilities and Cn (n=6, 8, 9, 10 …) dispersion coefficients. Force fields for classical atomistic simulations can be constructed using atom-in-material dispersion coefficients and polarizabilities. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and m-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (e.g., polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C6 coefficients. Overall, MCLF has improved accuracy and lower computational cost than the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C8, C9, and C10 dispersion coefficients and Quantum Drude Oscillator parameters. For sufficiently large systems, our method’s required computational time and memory scale linearly with increasing system size. This is a huge improvement over the cubic computational time of direct matrix inversion. As demonstrations, we study an ice crystal containing >250,000 atoms in the unit cell and the HIV reverse transcriptase enzyme complexed with an inhibiter molecule. This method should find widespread applications to parameterize classical force fields and DFT+dispersion methods.

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Evaluating the London Dispersion Coefficients of Protein Force Fields Using the Exchange-Hole Dipole Moment Model

Cited by 39 publications

References 92 publications

Rotational and Translational Diffusion of Proteins as a Function of Concentration

Rotational and Translational Diffusion of Proteins as a Function of Concentration

Simulating protein–ligand binding with neural network potentials

Introducing DDEC6 Atomic Population Analysis: Part 5. New Method to Compute Polarizabilities and Dispersion Coefficients

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