pKa Prediction Research Articles

Titration of Adenine in a GA Mismatch with Grand Canonical Simulations

Computational prediction of the pKa of ionizable groups remains a central challenge in biomolecular modeling. Although all-atom fixed-charge force fields could be accurate to describe the interaction network within the biomolecules, proper sampling techniques are required to obtain the thermodynamic information in the (de)protonation event. Sufficient sampling requires an ensemble of structures from simulations with proper treatments of the acid-base equilibria, and the grand canonical simulation technique could be used to model the growth/annihilation of hydrogen atoms by merging Hamiltonians of different protonation states into one simulation ensemble. The electrostatic feature of nucleotide systems is especially difficult to model, and the situation becomes more challenging when the ionizable site is highly perturbed. Although there are many successful predictions obtained from the grand canonical constant pH simulations, few reports focus on highly perturbed nucleotide systems with unconventional base-pair features. In this work, with the discrete constant pH method, we investigate the titration thermodynamics of an adenine in the catalytic triad in a 35-nucleotide single-stranded RNA hairpin, featuring an unconventional GA mismatch and a substantially shifted pKa value. Validation tests are performed with two system setups, both of which provide pKa predictions in good agreement with the experimental value. A single-configuration-based technique is used to calculate the pKa for comparison. The current success indicates the predictive power of the current nucleotide modeling framework.

Prediction of pKa in a system with high orthogonal barriers: Alchemical flying Gaussian method

Alchemical simulations are versatile tools for prediction of relative free energy differences. The accuracy of these methods depends critically on sampling of orthogonal (non-alchemical) degrees of freedom. Here we apply the flying Gaussian method to accelerate orthogonal degrees of freedom in peptide bond cis/trans isomerisation. The approach is demonstrated on prediction of pKa value of N-acetylproline. Isomerization of the amide bond was accelerated in this simulation by multiple orders of magnitude. Alchemical free energy was obtained by reweighting. The results show that a combination of alchemical and flying Gaussian simulations is suitable for systems with high (∼ 60 kJ/mol) energy barrier.

Holistic Prediction of the pKa in Diverse Solvents Based on a Machine-Learning Approach.

While many approaches to predict aqueous pKa values exist, the fast and accurate prediction of non-aqueous pKa values is still challenging. Based on the iBonD experimental pKa database (39 solvents), a holistic pKa prediction model was established using machine learning. Structural and physical-organic-parameter-based descriptors (SPOC) were introduced to represent the electronic and structural features of the molecules. The models trained with a neural network or the XGBoost algorithm showed the best prediction performance with a low MAE value of 0.87 pKa units. The approach allows a comprehensive mapping of all possible pKa correlations between different solvents and it was validated by predicting the aqueous pKa and micro-pKa of pharmaceutical molecules and pKa values of organocatalysts in DMSO and MeCN with high accuracy. An online prediction platform was constructed based on the current model, which can provide pKa prediction for different types of X-H acidity in the most commonly used solvents.

Computational studies of acidities of some hydroxycoumarins

In this article, various computational approaches were applied to compute proton affinities, gas-phase acidities and aqueous acidities (pKa values) of some hydroxycoumarins. The results indicate that using the thermodynamic cycle involving proton transfer to hydroxide ion is the best method and linear Gibbs free energy relationship could be a trustworthy alternative. This work has improved the previous accuracies obtained and therefore, could be a reliable tool for pKa predictions of other hydroxycoumarins of interest. The pKa value of 5-hydroxycoumarin was predicted using the best approaches equal to 7.9.

Predicting pKa Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods.

The acid dissociation constant (pKa) has an important influence on molecular properties crucial to compound development in synthesis, formulation, and optimization of absorption, distribution, metabolism, and excretion properties. We will present a method that combines quantum mechanical calculations, at a semi-empirical level of theory, with machine learning to accurately predict pKa for a diverse range of mono- and polyprotic compounds. The resulting model has been tested on two external data sets, one specifically used to test pKa prediction methods (SAMPL6) and the second covering known drugs containing basic functionalities. Both sets were predicted with excellent accuracy (root-mean-square errors of 0.7-1.0 log units), comparable to other methodologies using a much higher level of theory and computational cost.

Machine learning meets pK a.

We present a small molecule pK a prediction tool entirely written in Python. It predicts the macroscopic pK a value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r 2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

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

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 toarchive and compare the pH dependent behavior of compounds with multiple protonatable sites.

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 pKa 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 pKa 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 pKa values in DMSO vs predicted pKa 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.

GPU-Accelerated Implementation of Continuous Constant pH Molecular Dynamics in Amber: pKa Predictions with Single-pH Simulations.

We present a GPU implementation of the continuous constant pH molecular dynamics (CpHMD) based on the most recent generalized Born implicit-solvent model in the pmemd engine of the Amber molecular dynamics package. To test the accuracy of the tool for rapid pKa predictions, a series of 2 ns single-pH simulations were performed for over 120 titratable residues in 10 benchmark proteins that were previously used to test the various continuous CpHMD methods. The calculated pKa's showed a root-mean-square deviation of 0.80 and correlation coefficient of 0.83 with respect to experiment. Also, 90% of the pKa's were converged with estimated errors below 0.1 pH units. Surprisingly, this level of accuracy is similar to our previous replica-exchange simulations with 2 ns per replica and an exchange attempt frequency of 2 ps-1 (Huang, Harris, and Shen J. Chem. Inf. Model. 2018 , 58 , 1372 - 1383 ). Interestingly, for the linked titration sites in two enzymes, although residue-specific protonation state sampling in the single-pH simulations was not converged within 2 ns, the protonation fraction of the linked residues appeared to be largely converged, and the experimental macroscopic pKa values were reproduced to within 1 pH unit. Comparison with replica-exchange simulations with different exchange attempt frequencies showed that the splitting between the two macroscopic pKa's is underestimated with frequent exchange attempts such as 2 ps-1, while single-pH simulations overestimate the splitting. The same trend is seen for the single-pH vs replica-exchange simulations of a hydrogen-bonded aspartyl dyad in a much larger protein. A 2 ns single-pH simulation of a 400-residue protein takes about 1 h on a single NVIDIA GeForce RTX 2080 graphics card, which is over 1000 times faster than a CpHMD run on a single CPU core of a high-performance computing cluster node. Thus, we envision that GPU-accelerated continuous CpHMD may be used in routine pKa predictions for a variety of applications, from assisting MD simulations with protonation state assignment to offering pH-dependent corrections of binding free energies and identifying reactive hot spots for covalent drug design.

Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines.

The acid-base dissociation constant, pKa, is a key parameter to define the ionization state of a compound and directly affects its biopharmaceutical profile. In this study, we developed a novel approach for pKa prediction using rooted topological torsion fingerprints in combination with five machine learning (ML) methods: random forest, partial least squares, extreme gradient boosting, lasso regression, and support vector regression. With a large and diverse set of 14 499 experimental pKa values, pKa models were developed for aliphatic amines. The models demonstrated consistently good prediction statistics and were able to generate accurate prospective predictions as validated with an external test set of 726 pKa values (RMSE 0.45, MAE 0.33, and R2 0.84 by the top model). The factors that may affect prediction accuracy and model applicability were carefully assessed. The results demonstrated that rooted topological torsion fingerprints coupled with ML methods provide a promising approach for developing accurate pKa prediction models.

Open-source QSAR models for pKa prediction using multiple machine learning approaches

BackgroundThe logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction.MethodsThe experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure–activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN).ResultsThe three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products.ConclusionsThis work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

PH-Dependent Protonation of the Phl p 6 Pollen Allergen Studied by NMR and cpH-aMD.

We use state-of-the-art NMR experiments to measure apparent pKa values in the native protein environment and employ a cutting-edge combination of enhanced sampling and constant pH molecular dynamics (MD) simulations to rationalize strong pKa shifts. The major timothy grass pollen allergen Phl p 6 serves as an ideal model system for both methods due to its high number of titratable residues despite its comparably small size. We present a proton transition analysis as intuitive tool to depict the captured protonation state ensemble in atomistic detail. Combining microscopic structural details from MD simulations and macroscopic ensemble averages from NMR shifts leads to a comprehensive view on pH dependencies of protonation states and tautomers. Overall, we find striking agreement between simulation-based pKa predictions and experiment. However, our analyses suggest subtle differences in the underlying molecular origin of the observed pKa shifts. From accelerated constant pH MD simulations, we identify immediate proximity of opposite charges, followed by vicinity of equal charges as major driving forces for pKa shifts. NMR experiments on the other hand, suggest only a weak relation of pKa shifts and close contacts to charged residues, while the strongest influence derives from the dipolar character of α helices. The presented study hence pinpoints opportunities for improvements concerning the theoretical description of protonation state and tautomer probabilities. However, the coherence in the resulting apparent pKa values from simulations and experiment affirms cpH-aMD as a reliable tool to study allergen dynamics at varying pH levels.

Predicting pKa for Small Molecules on Public and In-house Datasets Using Fast Prediction Methods Combined with Data Fusion.

Data fusion approach was investigated in the context of pKa prediction for 391 small molecules derived from a public data source as well as for 681 compounds from an internal corporate database. Four different pKa prediction methods (Simulations Plus ADMET-Predictor S+pKa, ACD/Labs Percepta Classic, ACD/Labs Percepta GALAS and Epik) were used to predict the most acidic or basic pKa for each of the compounds. By using data fusion, the median absolute error for the internal compounds was reduced from the best performing single model's value of 0.69 down to 0.50. In addition to the improved accuracy, data fusion also enabled predictions for all of the compounds in the dataset as individual methods failed on some of the molecules.

A Search for the Protonation Model with Thermodynamic Dissociation Constants and (Extra)-Thermodynamics of Nilotinib Hydrochloride (TASIGNA)

Nilotinib hydrochloride (AMN107, TASIGNA, Novartis) is used to treat adults with chronic myeloid leukemia (CML), a type of leukemia. It is a novel, orally active BCR-ABL tyrosine kinase inhibitor derived from aminopyrimidine that is 30 times more effective against CML cells than is Imatinib. The nonlinear regression of the A versus pH spectra with REACTLAB and SQUAD84 and of the pH-titration curve with ESAB determined the four close and consecutive dissociation constants in the 11 steps of the newly proposed procedure. Prediction of pKa performed by MARVIN, PALLAS and ACD/Percepta determined the protonation sites. The sparingly soluble nilotinib hydrochloride denoted as L forms four water-soluble LH+, $${\text{LH}}_{2}^{2 + }$$ , $${\text{LH}}_{3}^{3 + }$$ , and $${\text{LH}}_{4}^{4 + }$$ cations. Although the adjusted pH has less effect on the absorbance changes in the chromophore, four thermodynamic dissociation constants were reliably determined: $${\text{p}}K_{\text{a1}}^{\text{T}}$$ = 3.60 ± 0.04, $${\text{p}}K_{\text{a2}}^{\text{T}}$$ = 4.42 ± 0.07, $${\text{p}}K_{\text{a3}}^{\text{T}}$$ = 4.71 ± 0.04, and $${\text{p}}K_{\text{a4}}^{\text{T}}$$ = 4.84 ± 0.03 at 25 °C and $${\text{p}}K_{\text{a1}}^{\text{T}}$$ = 3.61 ± 0.11, $${\text{p}}K_{\text{a2}}^{\text{T}}$$ = 4.29 ± 0.18, $${\text{p}}K_{\text{a3}}^{\text{T}}$$ = 4.49 ± 0.02, and $${\text{p}}K_{\text{a4}}^{\text{T}}$$ = 5.05 ± 0.03 at 37 °C, and by regression analysis of potentiometric titration curves with ESAB, $${\text{p}}K_{\text{a1}}^{\text{T}}$$ = 3.74 ± 0.01, $${\text{p}}K_{\text{a2}}^{\text{T}}$$ = 4.05 ± 0.01, $${\text{p}}K_{\text{a3}}^{\text{T}}$$ = 4.25 ± 0.01, and $${\text{p}}K_{\text{a4}}^{\text{T}}$$ = 4.91 ± 0.20 at 25 °C and $${\text{p}}K_{\text{a1}}^{\text{T}}$$ = 3.63 ± 0.03, $${\text{p}}K_{\text{a2}}^{\text{T}}$$ = 3.96 ± 0.03, $${\text{p}}K_{\text{a3}}^{\text{T}}$$ = 4.18 ± 0.03, and $${\text{p}}K_{4 1}^{\text{T}}$$ = 4.81 ± 0.05 at 37 °C. Positive enthalpy values ΔH° at 25 °C showed that the dissociation process is endothermic and is accompanied by heat absorption. Inasmuch as the entropy values of the dissociation process ΔS° at 25 °C and 37 °C were negative, the dissociation process is reversible.

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

Calculations of acidities of molecules with multiple tautomeric and/or conformational states require adequate treatment of the relative energetics of accessible states accompanied by a statistical-mechanical formulation of their contribution to the macroscopic pKa value. Here, we demonstrate rigorously the formal equivalence of two such approaches: a partition function treatment and statistics over transitions between molecular tautomeric and conformational states in the limit of a theory that does not require adjustment by empirical parameters correcting energetic values. However, for a frequently employed correction scheme, linear scaling of (free) energies and regression with respect to reference data taking an additive constant into account, this equivalence breaks down if more than one acid or base state is involved. The consequences of the resulting inconsistency are discussed on our datasets developed for aqueous pKa predictions during the recent SAMPL6 challenge, where molecular state energetics were computed based on the "embedded cluster reference interaction site model" (EC-RISM). This method couples integral equation theory as a solvation model to quantum-chemical calculations and yielded a test set root mean square error of 1.1 pK units from a partition function ansatz. For all practical purposes, the present results indicate that a state transition approach yields comparable accuracy despite the formal theoretical inconsistency, and that an additive regression intercept, which is strictly constant in the limit of large compound mass only, is a valid approximation. Graphical abstract Embedded cluster reference interaction site model-derived vs. experimental pKa for thetest set calculatedwith either the partition function (blue) or the state transition approach (red), using m as a free parameter.

Surface-Free Protocol for Computing PKA's (DelPhiPKa): Applications to Protein-Protein Interactions

DelPhiPKa is widely used for accurate prediction of pKa's of ionizable residues in proteins, DNAs and RNAs. DelPhiPKa approach is unique because it does not define the molecular surface, rather it uses a smooth Gaussian-based dielectric function throughout the space. The new development considers the presence of salt in the modeling protocol in a non-trivial manner via desolvation penalty term within the Boltzmann factor of Poisson-Boltzmann equation. This factor penalizes the salt ions to enter in the solute interior in a smooth manner without requiring sharp border between solute-solvent. Therefore, the presence of salt near macromolecule is determined by a combination of favorable electrostatic interactions and desolvation penalty. The DelPhiPKa-computed pKa's are in very good agreement with the experimentally determined pKa, having RMSD of 0.74. The unique approach of Gaussian-based smooth dielectric results in improved pKa's predictions compared to the standard method of two dielectric regions. DelPhiPKa also enables the calculations of pKa's of polar residues such as serine, cysteine, tyrosine and threonine. It has been demonstrated that DelPhiPKa outperforms all the other existing methods, even the explicit water models, while benchmarking the calculated pKa's against experimental pKa's of cysteine. Furthermore, we calculated the pKa's shifts due to the mutations in the protein-protein complexes taken from the SKEMPI database. We demonstrate that significant fraction of mutations listed in SKEMPI database results in unusual pKa's for introduced mutation or induce pKa's shifts of neighboring titratable groups. This is discussed in term of altering pH-dependence of the binding free energy.

Hydrogen-bond-based protein engineering for the acidic adaptation of Bacillus acidopullulyticus pullulanase

Pullulanase is a starch-debranching enzyme that is generally employed to efficiently break down starch for the production of high-glucose syrup. Acidic adaptation of pullulanases is of special interest. In this study, we conducted protein engineering to improve the acidic adaptation of Bacillus acidopullulyticus pullulanase (BaPul) and used a hydrogen-bond-based approach to identify promising residues that may change the deprotonation constants (pKa) of the catalytic residues. A total of 19 amino acids were selected for mutation according to the crystal structure of BaPul. The pH optimum of the L627R mutant shifted from 5.0 to 4.0, and its relative activity at pH 4.0 was 117% that of the wide-type enzyme. The improved efficacy of the L627R mutant at pH 4.0 was confirmed by kinetic parameters and pKa prediction. Moreover, the L627R mutant exhibited increased tolerance against acid-mediated denaturation, and its maximum d-glucose content (97.4%) was obtained after 40 h incubation, which is shorter by 10 h compared with the time required by the wide-type enzyme to produce a comparable amount of the monosaccharide. The L627R mutant may be suitable for industrial application because its shortened reaction time translates to reduced energy consumption.

Hyperactive mutation occurs adjacent to the essential glutamate 286 for transport in the yeast tryptophan permease Tat2

In Saccharomyces cerevisiae, high-affinity tryptophan import is mediated by the plasma membrane permease Tat2. Herein, we identified hyperactive Tat2 mutations, I285V and I285T, which allowed the cells to grow at very low tryptophan concentrations (<4 μg/mL). The Km value of wild-type Tat2 for tryptophan appeared to be 24 μg/mL, whereas that of Tat2I285V and Tat2I285T was 17 and 11 μg/mL, respectively. Normalized values of Vmax/Km for Tat2I285V- and Tat2I285T-mediated tryptophan import were 2-fold higher than that for Tat2, suggesting that these mutations increase the affinity for tryptophan, and mediate transport at very low tryptophan concentrations. I285 resides adjacent to E286, a fully conserved residue among amino acid pemreases. According to a pKa prediction for E208 (pKa ∼8.3–11.7) of Escherichia coli AdiC antiporter, a structural homologue of Tat2, the E286 carboxyl chain of Tat2 could get loaded with a proton during tryptophan/H+ symport. Hence, I285V and I285T mutations might affect the buried residue environment of Tat2, thereby facilitating tryptophan import. Additionally, Tat2I285V and Tat2I285T levels increased rapidly, and were efficiently localized to the cell surface after transferring the cells to low tryptophan medium (0.5 μg/mL). Our findings provide a clue to gain insights into the property of high-affinity transport mechanisms, and offer a unique approach to improve the functionality of broad types of amino acid permeases.

Insights into the substrate binding specificity of quorum-quenching acylase PvdQ

Quorum sensing is a cell to cell signaling mechanism that enables them to coordinate their behaviors in a density-dependent manner mediated by small diffusible signaling molecules, which can control the virulence and biofilm gene expression in many Gram-negative and positive bacteria. N-acyl homoserine lactone acylase PvdQ from human opportunistic pathogen Pseudomonas aeruginosa is a quorum-quenching enzyme that can hydrolyze the amide bond of the quorum signaling N-acyl homoserine lactones (AHLs) thereby degrading the signaling molecules, turning off the biofilm phenotype and resulting in a reduction of bacterial virulence. Previous studies demonstrated that PvdQ has different preferences for N-acyl substrates with different acyl chain lengths and substituents. However, the substrate binding specificity determinants of the quorum-quenching enzyme PvdQ with the different bacterial ligands are unknown and unintuitive. Further, elucidation of these determinants can lead to mutants with efficiency and broader substrate promiscuity. To investigate this question, a computational study was carried out combining multiple molecular docking methods, molecular dynamics simulations, residue interaction network analysis, and binding free energy calculations. The main findings are: firstly, the results from pKa predictions support that the pKa of the N-terminus of Serβ1 was depressed due to the surrounding residues. Multiple molecular docking studies provide useful information about the detailed binding modes and binding affinities. Secondly, 300 ns molecular dynamics simulations were carried out to analyze the overall molecular motions of substrate-bound and substrate-free PvdQ. The specific interactions between the active site of PvdQ and different ligands revealed the determinants for the preference among the ligands. A systematic comparison and analysis of the protein dynamic fingerprint of each complex demonstrated that binding of the most favorable ligand, C12-homoserine lactone (C12-HSL), reduced the global motions of the complex and maintained the correct arrangement of the catalytic site. Further, the residue interaction network analysis of each system illustrated that there are more communication contacts and pathways between the residues in the C12-HSL complex as compared to complexes with the other ligands. The binding of the C12-HSL ligand facilitates structural communication between the two knobs and the active site. While the binding of the other ligands tend to impair specific communication pathways between the two knobs and the active site, and lead to a catalytically inefficient state. Finally, simulation results from free energy landscape and binding free energy analysis revealed that the C12-HSL ligand has the lowest binding free energy and greater stability than the less favored ligands. Each of the following residues: Serβ1, Hisβ23, Pheβ24, Metβ30, Pheβ32, Leuβ50, Asnβ57, Thrβ69, Valβ70, Trpβ162, Trpβ186, Asnβ269, Argβ297 and Leuα146, play different roles in substrate binding specificity. This is the first computational study that provides molecular information for structure-dynamic-function relationships of PvdQ with different ligands and demonstrates determinants of bacterial substrate binding specificity.

PKAD: a database of experimentally measured pKa values of ionizable groups in proteins.

Ionizable residues play key roles in many biological phenomena including protein folding, enzyme catalysis and binding. We present PKAD, a database of experimentally measured pKas of protein residues reported in the literature or taken from existing databases. The database contains pKa data for 1350 residues in 157 wild-type proteins and for 232 residues in 45 mutant proteins. Most of these values are for Asp, Glu, His and Lys amino acids. The database is available as downloadable file as well as a web server (http://compbio.clemson.edu/pkad). The PKAD database can be used as a benchmarking source for development and improvement of pKa’s prediction methods. The web server provides additional information taken from the corresponding structures and amino acid sequences, which allows for easy search and grouping of the experimental pKas according to various biophysical characteristics, amino acid type and others.