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

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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.

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Fig. 1

Here, the illustrative diagram style of microstates were adopted from [24], and NMR-determined microscopic pKas for cetirizine were taken from [25]

Fig. 2

Subfigure of cysteine microscopic pKas was reproduced based on [19]

Fig. 3
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Abbreviations

SAMPL:

Statistical Assessment of the Modeling of Proteins and Ligands

pK a :

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

ps K a :

\(-{\log _{10}}\) 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

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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.

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Authors and Affiliations

  1. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Mehtap Işık, Ariën S. Rustenburg & John D. Chodera

  2. Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA

    Mehtap Işık

  3. Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA

    Dorothy Levorse & Timothy Rhodes

  4. Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY, 10065, USA

    Ariën S. Rustenburg

  5. Process and Analytical Research and Development, Merck & Co., Inc., Rahway, NJ, 07065, USA

    Ikenna E. Ndukwe, Xiao Wang, Mikhail Reibarkh & Gary E. Martin

  6. Analytical Research & Development, MRL, Merck & Co., Inc., MRL, 126 East Lincoln Avenue, Rahway, NJ, 07065, USA

    Heather Wang & Alexey A. Makarov

  7. Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA

    David L. Mobley

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  1. Mehtap Işık
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  2. Dorothy Levorse
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  10. David L. Mobley
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  11. Timothy Rhodes
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  12. John D. Chodera
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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.

Corresponding authors

Correspondence to Timothy Rhodes or John D. Chodera.

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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.

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Işık, M., Levorse, D., Rustenburg, A.S. et al. pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments. J Comput Aided Mol Des 32, 1117–1138 (2018). https://doi.org/10.1007/s10822-018-0168-0

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