title: Multiscale Free Energy Simulations: An Efficient Method for Connecting Classical MD Simulations to QM or QM/MM Free Energies Using Non-Boltzmann Bennett Reweighting Schemes.
abstract: THE RELIABILITY OF FREE ENERGY SIMULATIONS (FES) IS LIMITED BY TWO FACTORS: (a) the need for correct sampling and (b) the accuracy of the computational method employed. Classical methods (e.g., force fields) are typically used for FES and present a myriad of challenges, with parametrization being a principle one. On the other hand, parameter-free quantum mechanical (QM) methods tend to be too computationally expensive for adequate sampling. One widely used approach is a combination of methods, where the free energy difference between the two end states is computed by, e.g., molecular mechanics (MM), and the end states are corrected by more accurate methods, such as QM or hybrid QM/MM techniques. Here we report two new approaches that significantly improve the aforementioned scheme; with a focus on how to compute corrections between, e.g., the MM and the more accurate QM calculations. First, a molecular dynamics trajectory that properly samples relevant conformational degrees of freedom is generated. Next, potential energies of each trajectory frame are generated with a QM or QM/MM Hamiltonian. Free energy differences are then calculated based on the QM or QM/MM energies using either a non-Boltzmann Bennett approach (QM-NBB) or non-Boltzmann free energy perturbation (NB-FEP). Both approaches are applied to calculate relative and absolute solvation free energies in explicit and implicit solvent environments. Solvation free energy differences (relative and absolute) between ethane and methanol in explicit solvent are used as the initial test case for QM-NBB. Next, implicit solvent methods are employed in conjunction with both QM-NBB and NB-FEP to compute absolute solvation free energies for 21 compounds. These compounds range from small molecules such as ethane and methanol to fairly large, flexible solutes, such as triacetyl glycerol. Several technical aspects were investigated. Ultimately some best practices are suggested for improving methods that seek to connect MM to QM (or QM/MM) levels of theory in FES.
authors.affiliation: Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health , Bethesda, Maryland 20892, United States.
authors.forename: Gerhard
authors.initials: G
authors.lastname: König
authors.affiliation: Department of Chemistry, University of South Florida , 4202 E. Fowler Avenue, CHE205, Tampa, Florida 33620-5250, United States.
authors.forename: Phillip S
authors.initials: PS
authors.lastname: Hudson
authors.affiliation: Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna , Währingerstraße 17, A-1090 Vienna, Austria.
authors.forename: Stefan
authors.initials: S
authors.lastname: Boresch
authors.affiliation: Department of Chemistry, University of South Florida , 4202 E. Fowler Avenue, CHE205, Tampa, Florida 33620-5250, United States.
authors.forename: H Lee
authors.initials: HL
authors.lastname: Woodcock