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J Comput Aided Mol Des. 2018 Oct;32(10):1117-1138. doi: 10.1007/s10822-018-0168-0. Epub 2018 Nov 7.

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

Author information

1
Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
2
Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA.
3
Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA.
4
Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY, 10065, USA.
5
Process and Analytical Research and Development, Merck & Co., Inc., Rahway, NJ, 07065, USA.
6
Analytical Research & Development, MRL, Merck & Co., Inc., MRL, 126 East Lincoln Avenue, Rahway, NJ, 07065, USA.
7
Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA.
8
Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA. timothy_rhodes@merck.com.
9
Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. john.chodera@choderalab.org.

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; Blind prediction challenge; Macroscopic pK a; Macroscopic protonation state; Microscopic pK a; Microscopic protonation state; SAMPL; Spectrophotometric pK a measurement

PMID:
30406372
DOI:
10.1007/s10822-018-0168-0

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