Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pK_a were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pK_a values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method.

SAMPL7 blind challenge: quantum-mechanical prediction of partition coefficients and acid dissociation constants for small drug-like molecules

The physicochemical properties of a drug molecule determine the therapeutic effectiveness of the drug. Thus, the development of fast and accurate theoretical approaches for the prediction of such properties is inevitable. The participation to the SAMPL7 challenge is based on the estimation of logP coefficients and pK_a values of small drug-like sulfonamide derivatives. Thereby, quantum mechanical calculations were carried out in order to calculate the free energy of solvation and the transfer energy of 22 drug-like compounds in different environments (water and n-octanol) by employing the SMD solvation model. For logP calculations, we studied eleven different methodologies to calculate the transfer free energies, the lowest RMSE value was obtained for the M06L/def2-TZVP//M06L/def2-SVP level of theory. On the other hand, we employed an isodesmic reaction scheme within the macro pK_a framework; this was based on selecting reference molecules similar to the SAMPL7 challenge molecules. Consequently, highly well correlated pK_a values were obtained with the M062X/6-311+G(2df,2p)//M052X/6-31+G(d,p) level of theory.

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

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pK_a for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pK_a challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pK_a challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pK_a values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pK_a prediction methods.

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

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

Using QSAR Models to Predict Mitochondrial Targeting by Small-Molecule Xenobiotics Within Living Cells

Prediction of mitochondrial targeting, or prediction of exclusion from mitochondria, of small-molecule xenobiotics (biocides, drugs, probes, toxins) can be achieved using an algorithm derived from QSAR modeling. Application of the algorithm requires knowing the chemical structures of all ionic species of the xenobiotic compound in question, and for certain numerical structure parameters (AI, CBN, log P, pK _a, and Z) to be obtained for all such species. Procedures for specification of the chemical structures; estimation of the structure parameters; and application of the algorithm are described in an explicit protocol.

Correlation between the pKa and nuclear shielding of α-hydrogen of ketones

The α-H acidity is an important chemical property of ketones that has attracted much research interest. Theoretical prediction of pK_a for ketone α-H is significant. In this work, we theoretically studied the nuclear shielding of various α-Hs in a set of ketones and that of the corresponding enolic hydroxyl Hs in tautomeric enol forms. It has been demonstrated through linear regression analyses that the pK_a values of these ketones correlate with both sets of the calculated nuclear shielding values. The correlation coefficient R² of the linear correlation relationship is 0.90. The present work has provided a new approach to computationally evaluating the acidity of α-Hs in ketones, enabling us to semi-empirically predict the ketone α-H acidity from the calculated nuclear shielding values. Graphical AbstractExperimental pK_a values in DMSO vs predicted pK_a values calculated from ¹H nuclear shielding for the hydroxyl hydrogens in the enol forms and for the α-Hs in the keto forms. The surrounding solvent effects were modelled by keto/enol-DMSO clusters and SMD solvent models.

The role of halogen substituents and substrate pKa in defining the substrate specificity of 2,6-dichlorohydroquinone 1,2-dioxygenase (PcpA)

2,6-Dichlorohydroquinone 1,2-dioxygenase (PcpA) is a non-heme Fe(II) enzyme that is specific for ortho-dihalohydroquinones. Here we deconvolute the role of halogen polarizability vs. substrate pK_a in defining this specificity, and show how substrate binding compares to the structurally homologous catechol extradiol dioxygenases. The substrates 2,6-dichloro- and 2,6-dibromohydroquinone (polarizable halogens, pK_a1 = 7.3), 2,6-difluorohydroquinone (nonpolarizable halogens, pK_a1 = 7.5), and 2-chloro-6-methylhydroquinone (polarizable halogen, pK_a1 = 9.0) were examined through spectrophotometric titrations and steady-state kinetics. The results show that binding of the substrates to the enzyme decreased [Formula: see text] by about 0.5, except for 2,6-difluorohydroquinone, which showed no change. Additionally, the K_d values of 2,6-dichloro- and 2,6-dibromohydroquinone are about equal to their respective [Formula: see text]. For comparison, with catechol 2,3-dioxygenase (XylE), the substrates 4-methyl- and 3-bromocatechol are bound to the enzyme exclusively in the monoanion form over a wide pH range, indicating a [Formula: see text] of at least - 2.9 and - 1.2, respectively. The steady-state kinetic studies showed that 2,6-difluorohydroquinone is a poor substrate, with [Formula: see text] approximately 40-fold lower and [Formula: see text] 20-fold higher than 2,6-dichlorohydroquinone, despite its similar pK_a1. Likewise, the pH dependence of [Formula: see text] for 2-chloro-6-methylhydroquinone is nearly identical to that of 2,6-dichlorohydroquinone, despite its very different pK_a1. These results show that (1) it is clearly the halogen polarizability and not the lower substrate pK_a that determines the substrate specificity of PcpA, and (2) that PcpA, unlike the catechol extradiol dioxygenases, lacks an active site base that assists with substrate deprotonation, highlighting a key functional difference in what are otherwise similar active sites that defines their different reactivity.

Computational estimation of the acidities of purines and indoles

Purines and related compounds are central ingredients in the genetic code and form the structural framework for many drugs and other bioactive compounds. A key feature of these compounds is their acidity, as expressed by their pK_a values. For a proper understanding of the behaviors of these compounds, it is important to have a theoretical means for estimating their acidities. Here we present a quantum-chemical quantitative structure-activity relationship (QSAR) study of these compounds aimed at estimating the aqueous pK_a values of purines and related compounds based on the energy differences in solution ΔE(H₂O) between the parent compounds and their dissociation products. This method was applied to both the cation → neutral (pK_a1) and neutral → anion (pK_a2) dissociations of the compounds. Computations were performed using density functional theory at the B3LYP/6-31 + G** level with the SM8 aqueous solvent model. Good-quality QSAR regression equations were obtained for both dissociations using the ΔE(H₂O) descriptor. These equations were applied to estimate missing pK_a values for compounds in this category, and should also be applicable to the acidities of other related heterocyclic compounds.

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

In this work, quantum mechanical methods were used to predict the microscopic and macroscopic pK_a values for a set of 24 molecules as a part of the SAMPL6 blind challenge. The SMD solvation model was employed with M06-2X and different basis sets to evaluate three pK_a calculation schemes (direct, vertical, and adiabatic). The adiabatic scheme is the most accurate approach (RMSE = 1.40 pK_a units) and has high correlation (R² = 0.93), with respect to experiment. This approach can be improved by applying a linear correction to yield an RMSE of 0.73 pK_a units. Additionally, we consider including explicit solvent representation and multiple lower-energy conformations to improve the predictions for outliers. Adding three water molecules explicitly can reduce the error by 2-4 pK_a units, with respect to experiment, whereas including multiple local minima conformations does not necessarily improve the pK_a prediction.

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

Macroscopic pK_a 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 pK_a 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 pK_as with a maximum likelihood estimator and appropriately summed to yield macroscopic pK_a values or microstate populations as function of pH. We assessed the accuracy of three approaches to calculate the microscopic pK_as: 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 pK_a. The predictions that were corrected with the linear models had much better accuracy [root-mean-square error (RMSE) 2.04 and 1.95 pK_a 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 pK_a values for novel compounds with reasonable accuracy.

Correlations between the 1H NMR chemical shieldings and the pKa values of organic acids and amines

The acid dissociation constants and ¹H NMR chemical shieldings of organic compounds are important properties that have attracted much research interest. However, few studies have explored the relationship between these two properties. In this work, we theoretically studied the NMR chemical shifts of a series of carboxylic acids and amines in the gas phase and in aqueous solution. It was found that the negative logarithms of the experimental acid dissociation constants (i.e., the pK_a values) of the organic acids and amines in aqueous solution correlate almost linearly with the corresponding calculated NMR chemical shieldings. Key factors that affect the theoretically predicted pK_a values are discussed in this paper. The present work provides a new way to predict the pK_a values of organic/biochemical compounds. Graphical abstract The chemical shielding values of organic acids and amines correlate near linearly with their corresponding pK_a values.

In Vitro Alkylation Methods for Assessing the Protein Redox State

Cysteines are important residues for protein structure, function, and regulation. Owing to their modified reactivity, some cysteines can undergo very diverse redox posttranslational modifications, including the reversible formation of disulfide bonds, a widespread protein regulatory process as well exemplified in plant chloroplasts for Calvin-Benson cycle enzymes. Both core- and peripheral-photorespiratory enzymes possess conserved cysteines, some of which have been identified as being subject to oxidative modifications. This is not surprising considering their presence in subcellular compartments where the production of reactive species can be important. However, in most cases, the types of modifications and their biochemical effect on protein activity have not been validated, meaning that the possible impact of these modifications in a complex physiological context, such as photorespiration, remains obscure.We here describe a detailed set of protocols for alkylation methods that have been used so far to (1) study the protein cysteine redox state either in vitro by submitting purified recombinant proteins to reducing/oxidation treatments or in vivo by western blots on protein extracts from plants subject to environmental constraints, and its dependency on the two major reducing systems in the cell, i.e., the thioredoxin and glutathione/glutaredoxin systems, and (2) determine two key redox parameters, i.e., the cysteine pK _a and the redox midpoint potential.

Interpreting physicochemical experimental data sets

With the wealth of experimental physicochemical data available to chemoinformaticians from the literature, commercial, and company databases an increasing challenge is the interpretation of such datasets. Subtle differences in experimental methodology used to generate these datasets can give rise to variations in physicochemical property values. Such methodology nuances will be apparent to an expert experimentalist but not necessarily to the data analyst and modeller. This paper describes the differences between common methodologies for measuring the four most important physicochemical properties namely aqueous solubility, octan-1-ol/water distribution coefficient, pK(a) and plasma protein binding highlighting key factors that can lead to systematic differences. Insight is given into how to identify datasets suitable for combining.