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Journal of Computer-Aided Molecular Design

, Volume 32, Issue 10, pp 1203–1216 | Cite as

SAMPL6: calculation of macroscopic pKa values from ab initio quantum mechanical free energies

  • Edithe Selwa
  • Ian M. Kenney
  • Oliver BecksteinEmail author
  • Bogdan I. IorgaEmail author
Article

Abstract

Macroscopic pKa values were calculated for all compounds in the SAMPL6 blind prediction challenge, based on quantum chemical calculations with a continuum solvation model and a linear correction derived from a small training set. Microscopic pKa values were derived from the gas-phase free energy difference between protonated and deprotonated forms together with the Conductor-like Polarizable Continuum Solvation Model and the experimental solvation free energy of the proton. pH-dependent microstate free energies were obtained from the microscopic pKas with a maximum likelihood estimator and appropriately summed to yield macroscopic pKa values or microstate populations as function of pH. We assessed the accuracy of three approaches to calculate the microscopic pKas: direct use of the quantum mechanical free energy differences and correction of the direct values for short-comings in the QM solvation model with two different linear models that we independently derived from a small training set of 38 compounds with known pKa. The predictions that were corrected with the linear models had much better accuracy [root-mean-square error (RMSE) 2.04 and 1.95 pKa units] than the direct calculation (RMSE 3.74). Statistical measures indicate that some systematic errors remain, likely due to differences in the SAMPL6 data set and the small training set with respect to their interactions with water. Overall, the current approach provides a viable physics-based route to estimate macroscopic pKa values for novel compounds with reasonable accuracy.

Keywords

pKa pH Quantum chemistry SAMPL challenge 

Notes

Acknowledgements

Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R01GM118772 (to OB). BII was supported in part by Grants ANR-10-LABX-33 (LabEx LERMIT) and ANR-14-JAMR-0002-03 (JPIAMR) from the French National Research Agency (ANR), and by a Grant DIM MAL-INF from the Région Ile-de-France.

Supplementary material

10822_2018_138_MOESM1_ESM.zip (4.6 mb)
(ZIP 4.682 MB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Université Paris-Saclay, Labex LERMITGif-sur-YvetteFrance
  2. 2.Department of PhysicsArizona State UniversityTempeUSA
  3. 3.Center for Biological PhysicsArizona State UniversityTempeUSA

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