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Journal of Computer-Aided Molecular Design

, Volume 32, Issue 10, pp 1117–1138 | Cite as

pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments

  • Mehtap Işık
  • Dorothy Levorse
  • Ariën S. Rustenburg
  • Ikenna E. Ndukwe
  • Heather Wang
  • Xiao Wang
  • Mikhail Reibarkh
  • Gary E. Martin
  • Alexey A. Makarov
  • David L. Mobley
  • Timothy Rhodes
  • John D. Chodera
Article

Abstract

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transfer to another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pKa) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pKa prediction component to assess the accuracy with which contemporary pKa prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pKa values currently exist, predicting the pKas of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors—an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid–base titrations, we used UV absorbance-based pKa measurements to construct a high-quality experimental reference dataset of macroscopic pKas for the evaluation of computational pKa prediction methodologies that was utilized in the SAMPL6 pKa challenge. For several compounds in which the microscopic protonation states associated with macroscopic pKas were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pKa prediction methodologies on kinase inhibitor-like compounds.

Keywords

Acid dissociation constants Spectrophotometric pKa measurement Blind prediction challenge SAMPL Macroscopic pKa Microscopic pKa Macroscopic protonation state Microscopic protonation state 

Abbreviations

SAMPL

Statistical Assessment of the Modeling of Proteins and Ligands

pKa

\(-{\log _{10}}\) acid dissociation equilibrium constant

psKa

\(-{\log _{10}}\) apparent acid dissociation equilibrium constant in the presence of cosolvent

DMSO

Dimethyl sulfoxide

ISA

Ionic-strength adjusted

SEM

Standard error of the mean

TFA

Target factor analysis

LC–MS

Liquid chromatography–mass spectrometry

NMR

Nuclear magnetic resonance spectroscopy

HMBC

Heteronuclear multiple-bond correlation

TFA-d

Deutero-trifluoroacetic acid

Notes

Acknowledgements

MI, ASR, and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges support from NIH grant P30 CA008748. MI, JDC, ASR, and DLM gratefully acknowledge support from NIH grant R01GM124270 supporting SAMPL blind challenges. MI acknowledges support from a Doris J. Hutchinson Fellowship. DLM appreciates financial support from the National Institutes of Health (1R01GM108889-01), the National Science Foundation (CHE 1352608). IEN acknowledges support from the MRL Postdoctoral Research Program. The authors are extremely grateful for the assistance and support from the MRL Preformulations and NMR Structure Elucidation groups for materials, expertise, and instrument time, without which this SAMPL challenge would not have been possible. MI and DL are grateful to Pion/Sirius Analytical for their technical support in the planning and execution of this study. We are especially thankful to Karl Box (Sirius Analytical) for the guidance on optimization and interpretation of pKa measurements with the Sirius T3, as well as feedback on the manuscript. We thank Brad Sherborne (MRL; ORCID: 0000-0002-0037-3427) for his valuable insights at the conception of the pKa challenge and connecting us with TR and DL who were able to provide resources for experimental measurements. We acknowledge Paul Czodrowski (Merck KGaA; ORCID: 0000-0002-7390-8795) who provided feedback on multiple stages of this work: challenge construction, purchasable compound selection, and manuscript. We acknowledge contributions from Caitlin Bannan who provided feedback on experimental data collection and structure of pKa challenge from a computational chemist’s perspective. We are also grateful to Marilyn Gunner (CCNY) for her feedback on this manuscript. We thank anonymous reviewers for their input and constructive comments that improved this manuscript. MI, ASR, and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author contributions

Conceptualization, MI, JDC, TR, ASR, DLM; Methodology, MI, DL, IEN; Software, MI, ASR; Formal Analysis, MI; Investigation, MI, DL, IEN, HW, XW, MR; Resources, TR, DL; Data Curation, MI; Writing-Original Draft, MI, JDC, IEN; Writing - Review and Editing, MI, DL, ASR, IEN, HW, XW, MR, GEM, DLM, TR, JDC; Visualization, MI, IEN; Supervision, JDC, TR, DLM, GEM, AAM; Project Administration, MI; Funding Acquisition, JDC, DLM, TR, MI.

Compliance with ethical standards

Conflict of interest

JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study. JDC and DLM are current members of the Scientific Advisory Board of OpenEye Scientific Software. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, the Molecular Sciences Software Institute, the Starr Cancer Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete list of funding can be found at http://choderalab.org/funding.

Supplementary material

10822_2018_168_MOESM1_ESM.pdf (3.6 mb)
Supplementary material 1 (PDF 3731 KB)
10822_2018_168_MOESM2_ESM.zip (68.4 mb)
Supplementary material 2 (ZIP 70025 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mehtap Işık
    • 1
    • 2
  • Dorothy Levorse
    • 3
  • Ariën S. Rustenburg
    • 1
    • 4
  • Ikenna E. Ndukwe
    • 5
  • Heather Wang
    • 6
  • Xiao Wang
    • 5
  • Mikhail Reibarkh
    • 5
  • Gary E. Martin
    • 5
  • Alexey A. Makarov
    • 6
  • David L. Mobley
    • 7
  • Timothy Rhodes
    • 3
  • John D. Chodera
    • 1
  1. 1.Computational and Systems Biology Program, Sloan Kettering InstituteMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical SciencesCornell UniversityNew YorkUSA
  3. 3.Pharmaceutical SciencesMRL, Merck & Co., Inc.RahwayUSA
  4. 4.Graduate Program in Physiology, Biophysics, and Systems BiologyWeill Cornell Medical CollegeNew YorkUSA
  5. 5.Process and Analytical Research and DevelopmentMerck & Co., Inc.RahwayUSA
  6. 6.Analytical Research & DevelopmentMRL, Merck & Co., Inc.RahwayUSA
  7. 7.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of California, IrvineIrvineUSA

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