Journal of Computer-Aided Molecular Design

, Volume 28, Issue 3, pp 245–257 | Cite as

Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4

  • Gerhard König
  • Frank C. PickardIV
  • Ye Mei
  • Bernard R. Brooks
Article

Abstract

The correct representation of solute-water interactions is essential for the accurate simulation of most biological phenomena. Several highly accurate quantum methods are available to deal with solvation by using both implicit and explicit solvents. So far, however, most evaluations of those methods were based on a single conformation, which neglects solute entropy. Here, we present the first test of a novel approach to determine hydration free energies that uses molecular mechanics (MM) to sample phase space and quantum mechanics (QM) to evaluate the potential energies. Free energies are determined by using re-weighting with the Non-Boltzmann Bennett (NBB) method. In this context, the method is referred to as QM-NBB. Based on snapshots from MM sampling and accounting for their correct Boltzmann weight, it is possible to obtain hydration free energies that incorporate the effect of solute entropy. We evaluate the performance of several QM implicit solvent models, as well as explicit solvent QM/MM for the blind subset of the SAMPL4 hydration free energy challenge. While classical free energy simulations with molecular dynamics give root mean square deviations (RMSD) of 2.8 and 2.3 kcal/mol, the hybrid approach yields an improved RMSD of 1.6 kcal/mol. By selecting an appropriate functional and basis set, the RMSD can be reduced to 1 kcal/mol for calculations based on a single conformation. Results for a selected set of challenging molecules imply that this RMSD can be further reduced by using NBB to reweight MM trajectories with the SMD implicit solvent model.

Keywords

Hydration free energy calculations Non-Boltzmann Bennett Implicit solvent Explicit solvent QM/MM 

Supplementary material

10822_2014_9708_MOESM1_ESM.pdf (40 kb)
PDF (40 KB)

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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2014

Authors and Affiliations

  • Gerhard König
    • 1
  • Frank C. PickardIV
    • 1
  • Ye Mei
    • 1
    • 2
  • Bernard R. Brooks
    • 1
  1. 1.Laboratory of Computational BiologyNational Institutes of Health, National Heart, Lung and Blood InstituteRockvilleUSA
  2. 2.State Key Laboratory of Precision Spectroscopy, Institute of Theoretical and Computational ScienceEast China Normal UniversityShanghaiChina

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