Impact of Dispersion Coefficient on Simulations of Proteins and Organic Liquids

Bashardanesh, Zahedeh; Spoel, David van der

doi:10.1021/acs.jpcb.8b05770

Cited by 17 publications

(42 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…85,86 LJ-PME was used because it has been shown that the omission of long-range LJ interactions leads to incorrect surface tensions of liquids 87−89 and biological membranes 86 and, in addition, has an effect on protein aggregation at high protein concentrations in simulations. 90 Constraints were used on all chemical bonds to hydrogen atoms, applying the LINCS algorithm, 91 allowing a 1 fs integration time step. Temperature coupling in production simulations was applied using the v-rescale algorithm 92 with a time constant of 0.5 ps.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

et al. 2019

Self Cite

View full text Add to dashboard Cite

The partitioning of compounds between aqueous and other phases is important for predicting toxicity. Although thousands of octanol–water partition coefficients have been measured, these represent only a small fraction of the anthropogenic compounds present in the environment. The octanol phase is often taken to be a mimic of the inner parts of phospholipid membranes. However, the core of such membranes is typically more hydrophobic than octanol, and other partition coefficients with other compounds may give complementary information. Although a number of (cheap) empirical methods exist to compute octanol–water (log k OW ) and hexadecane–water (log k HW ) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log k OW and log k HW for 133 compounds from seven different pollutant categories as well as a control group using the solvation model based on electronic density (SMD) protocol based on Hartree–Fock (HF) or density functional theory (DFT) and the COSMO-RS method. For comparison, XlogP3 (log k OW ) values were retrieved from the PubChem database, and KowWin log k OW values were determined as well. For 24 of these compounds, log k OW was computed using potential of mean force (PMF) calculations based on classical molecular dynamics simulations. A comparison of the accuracy of the methods shows that COSMO-RS, KowWin, and XlogP3 all have a root-mean-square deviation (rmsd) from the experimental data of ≈0.4 log units, whereas the SMD protocol has an rmsd of 1.0 log units using HF and 0.9 using DFT. PMF calculations yield the poorest accuracy (rmsd = 1.1 log units). Thirty-six out of 133 calculations are for compounds without known log k OW , and for these, we provide what we consider a robust prediction, in the sense that there are few outliers, by averaging over the methods. The results supplied may be instrumental when developing new methods in computational ecotoxicity. The log k HW values are found to be strongly correlated to log k OW for most compounds.

show abstract

Section: Methodsmentioning

confidence: 99%

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…42 Nevertheless, we adopted this combination of models for the work here, after we evaluated it against reducing dispersion coefficient within proteins and organic liquids. 38 A further route was taken by von Bülow et al, who used a recent Amber99 variant (Amber99SB*-ILDN-Q 43−46 ) in combination with the dispersion-corrected TIP4P water model. 34 As a side note, we would like to stress that evaluating force field predictive power using protein simulations is likely less efficient than using organic liquids.…”

Section: Introductionmentioning

confidence: 99%

Rotational and Translational Diffusion of Proteins as a Function of Concentration

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…32,68,69 Apart from protein-water interactions, the London-dispersion coefficients in the force fields have been investigated as well but their effect on protein-protein interactions seems to be minor. [69][70][71] A comparison…”

Section: Discussionmentioning

confidence: 99%

Making Soup: Preparing and Validating Molecular Simulations of the Bacterial Cytoplasm

Bortot¹,

Bashardanesh²,

Spoel

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Biomolecular crowding affects the biophysical and biochemical behavior of macro- molecules when compared to the dilute environment present in experiments made with isolated proteins. Computational modeling and simulation are useful tools to study how crowding affects the structural dynamics and biological properties of macromolecules. As computational power increased, modeling and simulating large scale all-atom explicit solvent models of the prokaryote cytoplasm become possible. In this work, we build an atomistic model of the cytoplasm of Escherichia coli composed of 1.5 million atoms and submit it to a total of 3 μs of molecular dynamics simulations. The properties of biomolecules under crowding conditions are compared to those from simulations of the individual compounds under dilute conditions. The simulation model is found to be consistent with experimental data about the diffusion coefficient and stability of macromolecules under crowded conditions. In order to stimulate further work we provide a Python script and a set of files that enables other researchers to build their own E. coli cytoplasm models to address questions related to crowding.<br>

show abstract

Impact of Dispersion Coefficient on Simulations of Proteins and Organic Liquids

Cited by 17 publications

References 76 publications

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

Rotational and Translational Diffusion of Proteins as a Function of Concentration

Making Soup: Preparing and Validating Molecular Simulations of the Bacterial Cytoplasm

Contact Info

Product

Resources

About