Development of CHARMM Polarizable Force Field for Nucleic Acid Bases Based on the Classical Drude Oscillator Model

A polarizable force field for nucleic acid bases based on the classical Drude oscillator model is presented. Parameter optimization was performed to reproduce crystallographic geometries, crystal unit cell parameters, heats of sublimation, vibrational frequencies and assignments, dipole moments, molecular polarizabilities and quantum mechanical base-base and base-water interaction energies. The training and validation data included crystals of unsubstituted and alkyl-substituted adenine, guanine, cytosine, uracil, and thymine bases, hydrated crystals, and hydrogen bonded base pairs. Across all compounds, the RMSD in the calculated heats of sublimation is 4.1%. This equates to an improvement of more than 2.5 kcal/mol in accuracy compared to the nonpolarizable CHARMM27 force field. However, the level of agreement with experimental molecular volume decreased from 1.7% to 2.1% upon moving from the nonpolarizable to the polarizable model. The representation of dipole moments is significantly improved with the Drude polarizable force field. Unlike in additive force fields, there is no requirement for the gas-phase dipole moments to be overestimated, illustrating the ability of the Drude polarizable force field to treat accurately differently dielectric environments and indicating the improvements in the electrostatic model. Validation of the model was performed on the basis of the calculation of the gas-phase binding enthalpies of base pairs obtained via potential of mean force calculations; the additive and polarizable models both performed satisfactorily with average differences of 0.2 and 0.9 kcal/mol, respectively, and rms differences of 1.3 and 1.7 kcal/mol, respectively. Overall, considering the number of significant improvements versus the additive CHARMM force field, the incorporation of explicit polarizability into the force field for nucleic acid bases represents an additional step toward accurate computational modeling of biological systems.