When proteins are solvated in electrolyte solutions that contain alkali ions, the ions interact mostly with carboxylates on the protein surface. Correctly accounting for alkali-carboxylate interactions is thus important for realistic simulations of proteins. Acetates are the simplest carboxylates that are amphipathic, and experimental data for alkali acetate solutions are available and can be compared with observables obtained from simulations. We carried out molecular dynamics simulations of alkali acetate solutions using polarizable and non-polarizable forcefields and examined the ion-acetate interactions. In particular, activity coefficients and association constants were studied in a range of concentrations (0.03, 0.1, and 1M). In addition, quantum-mechanics (QM) based energy decomposition analysis was performed in order to estimate the contribution of polarization, electrostatics, dispersion, and QM (non-classical) effects on the cation-acetate and cation-water interactions. Simulations of Li-acetate solutions in general overestimated the binding of Li+ and acetates. In lower concentrations, the activity coefficients of alkali-acetate solutions were too high, which is suggested to be due to the simulation protocol and not the forcefields. Energy decomposition analysis suggested that improvement of the forcefield parameters to enable accurate simulations of Li-acetate solutions can be achieved but may require the use of a polarizable forcefield. Importantly, simulations with some ion parameters could not reproduce the correct ion-oxygen distances, which calls for caution in the choice of ion parameters when protein simulations are performed in electrolyte solutions.
Skip Nav Destination
,
,
Article navigation
21 November 2017
Research Article|
November 16 2017
Computer simulations of alkali-acetate solutions: Accuracy of the forcefields in difference concentrations Available to Purchase
Emma Ahlstrand;
Emma Ahlstrand
1
Department of Chemistry and Biomedical Sciences, Linnæus University
, 391 82 Kalmar, Sweden
2
Linnæus University Centre of Excellence “Biomaterials Chemistry”
, 391 82 Kalmar, Sweden
Search for other works by this author on:
Julio Zukerman Schpector
;
Julio Zukerman Schpector
3
Universidade Federal de São Carlos, Departamento de Química
, CP 676, 13565-905 São Carlos, SP, Brazil
Search for other works by this author on:
Ran Friedman
Ran Friedman
a)
1
Department of Chemistry and Biomedical Sciences, Linnæus University
, 391 82 Kalmar, Sweden
2
Linnæus University Centre of Excellence “Biomaterials Chemistry”
, 391 82 Kalmar, Sweden
Search for other works by this author on:
Emma Ahlstrand
1,2
Julio Zukerman Schpector
3
Ran Friedman
1,2,a)
1
Department of Chemistry and Biomedical Sciences, Linnæus University
, 391 82 Kalmar, Sweden
2
Linnæus University Centre of Excellence “Biomaterials Chemistry”
, 391 82 Kalmar, Sweden
3
Universidade Federal de São Carlos, Departamento de Química
, CP 676, 13565-905 São Carlos, SP, Brazil
a)
Electronic mail: [email protected]
Note: This article was intended as part of the Special Topic “From Quantum Mechanics to Force Fields” in Issue 16 of Volume 147 of J. Chem. Phys.
J. Chem. Phys. 147, 194102 (2017)
Article history
Received:
June 01 2017
Accepted:
October 24 2017
Citation
Emma Ahlstrand, Julio Zukerman Schpector, Ran Friedman; Computer simulations of alkali-acetate solutions: Accuracy of the forcefields in difference concentrations. J. Chem. Phys. 21 November 2017; 147 (19): 194102. https://doi.org/10.1063/1.4985919
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
The Amsterdam Modeling Suite
Evert Jan Baerends, Nestor F. Aguirre, et al.
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
Light–matter interaction at the nano- and molecular scale
Kaifeng Wu, Chufeng Zhang, et al.
Related Content
Quantum machine learning corrects classical forcefields: Stretching DNA base pairs in explicit solvent
J. Chem. Phys. (August 2022)
Can the AMOEBA forcefield be used for high pressure simulations? The extreme case of methane and water
J. Chem. Phys. (August 2024)
Exploring the landscape of Buckingham potentials for silica by machine learning: Soft vs hard interatomic forcefields
J. Chem. Phys. (February 2020)
Ab initio parameterization of a charge optimized many-body forcefield for Si–SiO2: Validation and thermal transport in nanostructures
J. Chem. Phys. (March 2016)
Statistical approaches to forcefield calibration and prediction uncertainty in molecular simulation
J. Chem. Phys. (February 2011)