Literature DB >> 30084080

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

Edithe Selwa1, Ian M Kenney2, Oliver Beckstein3,4, Bogdan I Iorga5.   

Abstract

Macroscopic pKa values were calculated for all compounds in the SAMPL6 blind prediction challenge, based on quantum chemical calculations with a continuum solvation model and a linear correction derived from a small training set. Microscopic pKa values were derived from the gas-phase free energy difference between protonated and deprotonated forms together with the Conductor-like Polarizable Continuum Solvation Model and the experimental solvation free energy of the proton. pH-dependent microstate free energies were obtained from the microscopic pKas with a maximum likelihood estimator and appropriately summed to yield macroscopic pKa values or microstate populations as function of pH. We assessed the accuracy of three approaches to calculate the microscopic pKas: direct use of the quantum mechanical free energy differences and correction of the direct values for short-comings in the QM solvation model with two different linear models that we independently derived from a small training set of 38 compounds with known pKa. The predictions that were corrected with the linear models had much better accuracy [root-mean-square error (RMSE) 2.04 and 1.95 pKa units] than the direct calculation (RMSE 3.74). Statistical measures indicate that some systematic errors remain, likely due to differences in the SAMPL6 data set and the small training set with respect to their interactions with water. Overall, the current approach provides a viable physics-based route to estimate macroscopic pKa values for novel compounds with reasonable accuracy.

Entities:  

Keywords:  Quantum chemistry; SAMPL challenge; pH; pK a

Mesh:

Substances:

Year:  2018        PMID: 30084080      PMCID: PMC6240492          DOI: 10.1007/s10822-018-0138-6

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


  23 in total

1.  The SAMPL2 blind prediction challenge: introduction and overview.

Authors:  Matthew T Geballe; A Geoffrey Skillman; Anthony Nicholls; J Peter Guthrie; Peter J Taylor
Journal:  J Comput Aided Mol Des       Date:  2010-05-09       Impact factor: 3.686

2.  Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water.

Authors:  Caitlin C Bannan; Gaetano Calabró; Daisy Y Kyu; David L Mobley
Journal:  J Chem Theory Comput       Date:  2016-08-01       Impact factor: 6.006

Review 3.  Progress in the prediction of pKa values in proteins.

Authors:  Emil Alexov; Ernest L Mehler; Nathan Baker; António M Baptista; Yong Huang; Francesca Milletti; Jens Erik Nielsen; Damien Farrell; Tommy Carstensen; Mats H M Olsson; Jana K Shen; Jim Warwicker; Sarah Williams; J Michael Word
Journal:  Proteins       Date:  2011-10-15

4.  A blind challenge for computational solvation free energies: introduction and overview.

Authors:  J Peter Guthrie
Journal:  J Phys Chem B       Date:  2009-04-09       Impact factor: 2.991

Review 5.  Predicting the pKa of small molecule.

Authors:  Matthias Rupp; Robert Körner; Igor V Tetko
Journal:  Comb Chem High Throughput Screen       Date:  2011-06-01       Impact factor: 1.339

6.  Free-Energy Calculations of Ionic Hydration Consistent with the Experimental Hydration Free Energy of the Proton.

Authors:  Haiyang Zhang; Yang Jiang; Hai Yan; Chunhua Yin; Tianwei Tan; David van der Spoel
Journal:  J Phys Chem Lett       Date:  2017-06-06       Impact factor: 6.475

7.  Toward the accurate calculation of pKa values in water and acetonitrile.

Authors:  James T Muckerman; Jonathan H Skone; Ming Ning; Yuko Wasada-Tsutsui
Journal:  Biochim Biophys Acta       Date:  2013-04-06

8.  Prediction of hydration free energies for the SAMPL4 diverse set of compounds using molecular dynamics simulations with the OPLS-AA force field.

Authors:  Oliver Beckstein; Anaïs Fourrier; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2014-02-21       Impact factor: 3.686

Review 9.  Blind prediction of solvation free energies from the SAMPL4 challenge.

Authors:  David L Mobley; Karisa L Wymer; Nathan M Lim; J Peter Guthrie
Journal:  J Comput Aided Mol Des       Date:  2014-03-11       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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  7 in total

1.  Correlation between molecular acidity (pKa) and vibrational spectroscopy.

Authors:  Niraj Verma; Yunwen Tao; Bruna Luana Marcial; Elfi Kraka
Journal:  J Mol Model       Date:  2019-01-30       Impact factor: 1.810

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

Authors:  M R Gunner; Taichi Murakami; Ariën S Rustenburg; Mehtap Işık; John D Chodera
Journal:  J Comput Aided Mol Des       Date:  2020-02-12       Impact factor: 3.686

3.  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

4.  Prediction of octanol-water partition coefficients for the SAMPL6-[Formula: see text] molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields.

Authors:  Shujie Fan; Bogdan I Iorga; Oliver Beckstein
Journal:  J Comput Aided Mol Des       Date:  2020-01-20       Impact factor: 3.686

5.  PROTACs bearing piperazine-containing linkers: what effect on their protonation state?

Authors:  Jenny Desantis; Andrea Mammoli; Michela Eleuteri; Alice Coletti; Federico Croci; Antonio Macchiarulo; Laura Goracci
Journal:  RSC Adv       Date:  2022-08-09       Impact factor: 4.036

6.  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

7.  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

  7 in total

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