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

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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.

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Acknowledgments

The authors would like to thank Tim Miller, Richard Venable and John Legato for technical assistance with the parallelization of the QM/MM calculations. The support by Yihan Shao was invaluable during the setup of the Q-Chem scripts and we also would like to thank Florentina Tofoleanu, Tim Miller and Juyong Lee for carefully reading and commenting on the manuscript as well as Stefan Boresch and Lee Woodcock for fruitful discussions on the optimal performance of NBB. This work was supported by the intramural research program of the National Heart, Lung and Blood Institute of the National Institutes of Health and utilized the high-performance computational capabilities of the LoBoS and Biowulf Linux clusters at the National Institutes of Health. (http://www.lobos.nih.gov and http://biowulf.nih.gov).

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  1. Laboratory of Computational Biology, National Institutes of Health, National Heart, Lung and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA

    Gerhard König, Frank C. Pickard IV, Ye Mei & Bernard R. Brooks

  2. State Key Laboratory of Precision Spectroscopy, Institute of Theoretical and Computational Science, East China Normal University, Shanghai, 200062, China

    Ye Mei

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  1. Gerhard König
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  2. Frank C. Pickard IV
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Correspondence to Gerhard König.

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König, G., Pickard, F.C., Mei, Y. et al. Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4. J Comput Aided Mol Des 28, 245–257 (2014). https://doi.org/10.1007/s10822-014-9708-4

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