Optimizing Nonbonded Interactions of the OPLS Force Field for Aqueous Solutions of Carbohydrates: How to Capture Both Thermodynamics and Dynamics
Knowledge on thermodynamic and transport properties of aqueous solutions of carbohydrates is of great interest for process and product design in the food, pharmaceutical, and biotechnological industries. Molecular simulation is a powerful tool to calculate these properties, but current classical force fields cannot provide accurate estimates for all properties of interest. The poor performance of the force fields is mainly observed for concentrated solutions, where solute–solute interactions are overestimated. In this study, we propose a method to refine force fields, such that solute–solute interactions are more accurately described. The OPLS force field combined with the SPC/Fw water model is used as a basis. We scale the nonbonded interaction parameters of sucrose, a disaccharide. The scaling factors are chosen in such a way that experimental thermodynamic and transport properties of aqueous solutions of sucrose are accurately reproduced. Using a scaling factor of 0.8 for Lennard-Jones energy parameters (ϵ) and a scaling factor of 0.95 for partial atomic charges (q), we find excellent agreement between experiments and computed liquid densities, thermodynamic factors, shear viscosities, self-diffusion coefficients, and Fick (mutual) diffusion coefficients. The transferability of these optimum scaling factors to other carbohydrates is verified by computing thermodynamic and transport properties of aqueous solutions of d-glucose, a monosaccharide. The good agreement between computed properties and experiments suggests that the scaled interaction parameters are transferable to other carbohydrates, especially for concentrated solutions.
|Journal||J. Chem. Theory Comput.|
Jamali, S.H, van Westen, T, Moultos, O.A, & Vlugt, T.J.H. (2018). Optimizing Nonbonded Interactions of the OPLS Force Field for Aqueous Solutions of Carbohydrates: How to Capture Both Thermodynamics and Dynamics. J. Chem. Theory Comput., 14(12), 6690–6700. doi:10.1021/acs.jctc.8b00909