SAMPL6: Calculation of macroscopic pK(a) values from ab initio quantum mechanical free energies

Autor: Bogdan I. Iorga, Edithe Selwa, Oliver Beckstein, Ian M. Kenney
Přispěvatelé: Institut de Chimie des Substances Naturelles (ICSN), Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Department of Physics, Arizona State University (ASU), Arizona State University [Tempe] (ASU), Grant DIM MAL-INF from the Région Ile-de-FranceNational Institute Of General Medical Sciences of the National Institutes of Health under Award Number R01GM118772, ANR-11-IDEX-0003,IPS,Idex Paris-Saclay(2011), ANR-14-JAMR-0002,DesInMBL,Structure-guided design of pan inhibitors of metallo-ß-lactamases(2014)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design, Springer Verlag, 2018, 32 (10), pp.1203-1216. ⟨10.1007/s10822-018-0138-6⟩
ISSN: 0920-654X
1573-4951
Popis: International audience; 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.
Databáze: OpenAIRE
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