Literature DB >> 32052350

Standard state free energies, not pKas, are ideal for describing small molecule protonation and tautomeric states.

M R Gunner1, Taichi Murakami2, Ariën S Rustenburg3,4,5, Mehtap Işık3,6, John D Chodera3.   

Abstract

The pKa is the standard measure used to describe the aqueous proton affinity of a compound, indicating the proton concentration (pH) at which two protonation states (e.g. A- and AH) have equal free energy. However, compounds can have additional protonation states (e.g. AH2+), and may assume multiple tautomeric forms, with the protons in different positions (microstates). Macroscopic pKas give the pH where the molecule changes its total number of protons, while microscopic pKas identify the tautomeric states involved. As tautomers have the same number of protons, the free energy difference between them and their relative probability is pH independent so there is no pKa connecting them. The question arises: What is the best way to describe protonation equilibria of a complex molecule in any pH range? Knowing the number of protons and the relative free energy of all microstates at a single pH, ∆G°, provides all the information needed to determine the free energy, and thus the probability of each microstate at each pH. Microstate probabilities as a function of pH generate titration curves that highlight the low energy, observable microstates, which can then be compared with experiment. A network description connecting microstates as nodes makes it straightforward to test thermodynamic consistency of microstate free energies. The utility of this analysis is illustrated by a description of one molecule from the SAMPL6 Blind pKa Prediction Challenge. Analysis of microstate ∆G°s also makes a more compact way to archive and compare the pH dependent behavior of compounds with multiple protonatable sites.

Entities:  

Keywords:  Multiprotic; Protonation state; SAMPL6; Tautomer; pH titration; pKa

Year:  2020        PMID: 32052350     DOI: 10.1007/s10822-020-00280-7

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  23 in total

Review 1.  The pKa Cooperative: a collaborative effort to advance structure-based calculations of pKa values and electrostatic effects in proteins.

Authors:  Jens E Nielsen; M R Gunner; Bertrand E García-Moreno
Journal:  Proteins       Date:  2011-10-15

2.  Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

Authors:  Robert Fraczkiewicz; Mario Lobell; Andreas H Göller; Ursula Krenz; Rolf Schoenneis; Robert D Clark; Alexander Hillisch
Journal:  J Chem Inf Model       Date:  2014-12-16       Impact factor: 4.956

3.  pKa calculations for tautomerizable and conformationally flexible molecules: partition function vs. state transition approach.

Authors:  Nicolas Tielker; Lukas Eberlein; Christian Chodun; Stefan Güssregen; Stefan M Kast
Journal:  J Mol Model       Date:  2019-04-30       Impact factor: 1.810

4.  The SAMPL6 challenge on predicting aqueous pKa values from EC-RISM theory.

Authors:  Nicolas Tielker; Lukas Eberlein; Stefan Güssregen; Stefan M Kast
Journal:  J Comput Aided Mol Des       Date:  2018-08-02       Impact factor: 3.686

5.  SAMPL6 challenge results from [Formula: see text] predictions based on a general Gaussian process model.

Authors:  Caitlin C Bannan; David L Mobley; A Geoffrey Skillman
Journal:  J Comput Aided Mol Des       Date:  2018-10-15       Impact factor: 3.686

6.  Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge.

Authors:  Qiao Zeng; Michael R Jones; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2018-08-20       Impact factor: 3.686

7.  Are acidic and basic groups in buried proteins predicted to be ionized?

Authors:  Jinrang Kim; Junjun Mao; M R Gunner
Journal:  J Mol Biol       Date:  2005-04-07       Impact factor: 5.469

8.  An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge.

Authors:  Samarjeet Prasad; Jing Huang; Qiao Zeng; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2018-10-01       Impact factor: 3.686

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

Authors:  Edithe Selwa; Ian M Kenney; Oliver Beckstein; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2018-08-06       Impact factor: 3.686

10.  Constant pH Replica Exchange Molecular Dynamics in Explicit Solvent Using Discrete Protonation States: Implementation, Testing, and Validation.

Authors:  Jason M Swails; Darrin M York; Adrian E Roitberg
Journal:  J Chem Theory Comput       Date:  2014-02-05       Impact factor: 6.006

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  5 in total

1.  Quantifying charge state heterogeneity for proteins with multiple ionizable residues.

Authors:  Martin J Fossat; Ammon E Posey; Rohit V Pappu
Journal:  Biophys J       Date:  2021-11-23       Impact factor: 4.033

2.  Improving Small Molecule pK a Prediction Using Transfer Learning With Graph Neural Networks.

Authors:  Fritz Mayr; Marcus Wieder; Oliver Wieder; Thierry Langer
Journal:  Front Chem       Date:  2022-05-26       Impact factor: 5.545

3.  Stacking Gaussian processes to improve [Formula: see text] predictions in the SAMPL7 challenge.

Authors:  Robert M Raddi; Vincent A Voelz
Journal:  J Comput Aided Mol Des       Date:  2021-08-07       Impact factor: 4.179

4.  Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions.

Authors:  Mehtap Işık; Ariën S Rustenburg; Andrea Rizzi; M R Gunner; David L Mobley; John D Chodera
Journal:  J Comput Aided Mol Des       Date:  2021-01-04       Impact factor: 3.686

5.  Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge.

Authors:  Teresa Danielle Bergazin; Nicolas Tielker; Yingying Zhang; Junjun Mao; M R Gunner; Karol Francisco; Carlo Ballatore; Stefan M Kast; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2021-06-24       Impact factor: 3.686

  5 in total

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