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Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge

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Abstract

In this work, quantum mechanical methods were used to predict the microscopic and macroscopic pKa values for a set of 24 molecules as a part of the SAMPL6 blind challenge. The SMD solvation model was employed with M06-2X and different basis sets to evaluate three pKa calculation schemes (direct, vertical, and adiabatic). The adiabatic scheme is the most accurate approach (RMSE = 1.40 pKa units) and has high correlation (R2 = 0.93), with respect to experiment. This approach can be improved by applying a linear correction to yield an RMSE of 0.73 pKa units. Additionally, we consider including explicit solvent representation and multiple lower-energy conformations to improve the predictions for outliers. Adding three water molecules explicitly can reduce the error by 2–4 pKa units, with respect to experiment, whereas including multiple local minima conformations does not necessarily improve the pKa prediction.

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Acknowledgements

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). The authors would also like to thank Frank C. Pickard, IV and Samarjeet Prasad for the very helpful discussion.

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  1. Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, 12 South Drive, Building 12A Room 3053, Bethesda, MD, 20814, USA

    Qiao Zeng, Michael R. Jones & Bernard R. Brooks

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  1. Qiao Zeng
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Correspondence to Qiao Zeng.

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Zeng, Q., Jones, M.R. & Brooks, B.R. Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge. J Comput Aided Mol Des 32, 1179–1189 (2018). https://doi.org/10.1007/s10822-018-0150-x

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