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Journal of Computer-Aided Molecular Design

, Volume 30, Issue 11, pp 1087–1100 | Cite as

Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections

  • Frank C. PickardIV
  • Gerhard König
  • Florentina Tofoleanu
  • Juyong Lee
  • Andrew C. Simmonett
  • Yihan Shao
  • Jay W. Ponder
  • Bernard R. Brooks
Article

Abstract

The computation of distribution coefficients between polar and apolar phases requires both an accurate characterization of transfer free energies between phases and proper accounting of ionization and protomerization. We present a protocol for accurately predicting partition coefficients between two immiscible phases, and then apply it to 53 drug-like molecules in the SAMPL5 blind prediction challenge. Our results combine implicit solvent QM calculations with classical MD simulations using the non-Boltzmann Bennett free energy estimator. The OLYP/DZP/SMD method yields predictions that have a small deviation from experiment (RMSD = 2.3 \(\log\) D units), relative to other participants in the challenge. Our free energy corrections based on QM protomer and \({\text{p}}K_{\text{a}}\) calculations increase the correlation between predicted and experimental distribution coefficients, for all methods used. Unfortunately, these corrections are overly hydrophilic, and fail to account for additional effects such as aggregation, water dragging and the presence of polar impurities in the apolar phase. We show that, although expensive, QM-NBB free energy calculations offer an accurate and robust method that is superior to standard MM and QM techniques alone.

Keywords

Free energy Partition coefficients Distribution coefficients Non-Boltzmann Bennett Implicit solvent SAMPL5 pKa Protomer Tautomer 

Notes

Acknowledgments

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)

Supplementary material

10822_2016_9955_MOESM1_ESM.pdf (714 kb)
Supplementary material 1 (pdf 714 KB)

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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2016

Authors and Affiliations

  1. 1.Laboratory of Computational BiologyNational Institutes of Health – National Heart, Lung and Blood InstituteRockvilleUSA
  2. 2.Max Planck Institut für KohlenforschungMülheim an der RuhrGermany
  3. 3.Department of Chemistry and BiochemistryUniversity of OklahomaNormanUSA
  4. 4.Department of ChemistryWashington UniversitySt. LouisUSA

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