Acidity of persulfides and its modulation by the protein environments in sulfide quinone oxidoreductase and thiosulfate sulfurtransferase.

Persulfides (RSSH/RSS-) participate in sulfur metabolism and are proposed to transduce hydrogen sulfide (H2S) signaling. Their biochemical properties are poorly understood. Herein, we studied the acidity and nucleophilicity of several low molecular weight persulfides using the alkylating agent, monobromobimane. The different persulfides presented similar pKa values (4.6-6.3) and pH-independent rate constants (3.2-9.0 × 103 M-1 s-1), indicating that the substituents in persulfides affect properties to a lesser extent than in thiols because of the larger distance to the outer sulfur. The persulfides had higher reactivity with monobromobimane than analogous thiols and putative thiols with the same pKa, providing evidence for the alpha effect (enhanced nucleophilicity by the presence of a contiguous atom with high electron density). Additionally, we investigated two enzymes from the human mitochondrial H2S oxidation pathway that form catalytic persulfide intermediates, sulfide quinone oxidoreductase and thiosulfate sulfurtransferase (TST, rhodanese). The pH dependence of the activities of both enzymes was measured using sulfite and/or cyanide as sulfur acceptors. The TST half-reactions were also studied by stopped-flow fluorescence spectroscopy. Both persulfidated enzymes relied on protonated groups for reaction with the acceptors. Persulfidated sulfide quinone oxidoreductase appeared to have a pKa of 7.8 ± 0.2. Persulfidated TST presented a pKa of 9.38 ± 0.04, probably due to a critical active site residue rather than the persulfide itself. The TST thiol reacted in the anionic state with thiosulfate, with an apparent pKa of 6.5 ± 0.1. Overall, our study contributes to a fundamental understanding of persulfide properties and their modulation by protein environments.

Torque generation mechanism in Fo motor of ATP synthase elucidated by free-energy and Coulomb-energy landscapes along the c-ring rotation.

Fo portion of ATP synthase is a proton-motive rotary motor. The Coulombic attraction between the conserved acidic residues in the c-ring and the arginine in the a-subunit (aR) was early proposed to drive the c-ring rotation relative to the a-subunit, and has been actually observed in our previous molecular dynamics simulation with full atomistic description of Fo embedded in the membrane. In this study, to quantify the driving force, we conducted the umbrella sampling (US) and obtained the free-energy landscape for the c-ring rotation. We first show that the free-energy gradient toward the ATP-synthesis direction appears in the deprotonated state of cE. Using the sampled snapshots that cover a wide range of the rotational angle, we further analyzed the rotational-angle dependence of the hydration and the protonation states and obtained the Coulomb-energy landscapes with a focus on the cE-aR interaction. The results indicate that both the Coulombic solvation energy of cE and the interaction energy between cE and aR contribute to the torque generation for the c-ring rotation.

Spectroscopic and Density Functional Studies on the Interaction of a Naphthalene Derivative with Anions.

This article highlights the investigation of anion interactions and recognition abilities of naphthalene derivative, [(E)-1-(((4-nitrophenyl)imino)methyl)naphthalen-2-ol], (NIMO) by UV-visible spectroscopically and colorimetrically. NIMO shows selective recognition of F- ions colorimetrically, and a visual color change from yellow to pink is observed by the naked eye. The F- ions recognition is fully reversible in the presence of HSO4- ions. The limit of F- ions detection by NIMO could be possible down to 0.033 ppm-level. A paper strips-based test kit has been demonstrated to detect F- ions selectively by the naked eye, and a smartphone-based method for real sample analysis in the non-aqueous medium has also been demostrated. Spectroscopic behavior is well supported by pKa value calculation and DFT analysis, to find a correlation with receptor analyte interaction. The optical response of NIMO towards the accumulation of F- ions and, subsequently, HSO4- ions as chemical inputs provides an opportunity to construct INH and IMP molecular logic gates.

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 pKa 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 pKa 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 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa 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 pKa 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.

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 pKa 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 pKa framework; this was based on selecting reference molecules similar to the SAMPL7 challenge molecules. Consequently, highly well correlated pKa values were obtained with the M062X/6-311+G(2df,2p)//M052X/6-31+G(d,p) level of theory.

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 pKa 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 pKa 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.

PKA17-A Coarse-Grain Grid-Based Methodology and Web-Based Software for Predicting Protein pK a Shifts.

We have developed and tested PKA17, a coarse-grain grid-based model for predicting protein pK a shifts. Our pK a predictor is currently deployed via a website interface. We have carried out parameter fitting using 442 Asp, Glu, His, Lys, and Arg residues for which experimental results are available in the literature. PROPKA software has been used for benchmarking. The average unsigned error and root-mean-square deviation (RMSD) have been found to be 0.628 and 0.831 pH units, respectively, for PKA17. The corresponding results with PROPKA are 0.761 and 1.063 units. We have assessed the robustness of the developed PKA17 methodology with a number of tests and have also explored the possibility of using a combination of PROPKA and PKA17 calculations in order to improve the accuracy of predicted pK a values for protein residues. We have also once again confirmed that protein acidity constants are influenced almost entirely by residues in the immediate spatial proximity of the ionizable amino acids. The resulting PKA17 software has been deployed online with a web-based interface at http://users.wpi.edu/~jpcvitkovic/pka_calc.html. © 2019 Wiley Periodicals, Inc.

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 pKa 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, pKa1 = 7.3), 2,6-difluorohydroquinone (nonpolarizable halogens, pKa1 = 7.5), and 2-chloro-6-methylhydroquinone (polarizable halogen, pKa1 = 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 Kd 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 pKa1. 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 pKa1. These results show that (1) it is clearly the halogen polarizability and not the lower substrate pKa 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.

Altering the Y137-K164-K166 triad of mandelate racemase and its effect on the observed pKa of the Brønsted base catalysts.

Mandelate racemase (MR) catalyzes the interconversion of the enantiomers of mandelate using a two-base mechanism with Lys 166 acting as the Brønsted base to abstract the α-proton from (S)-mandelate. The resulting intermediate is subsequently re-protonated by the conjugate acid of His 297 to yield (R)-mandelate. The roles of these amino acids are reversed when (R)-mandelate is the substrate. The side chains of Tyr 137, Lys 164, and Lys 166 form a H-bonding network and the proximity of the two ε-NH3+ groups is believed to lower the pKa of Lys 166. We used site-directed mutagenesis, kinetics, and pH-rate studies to explore the roles of Lys 164 (K164 C/M) and Tyr 137 (Y137  L/F/S/T) in catalysis. The efficiency (kcat/Km) was reduced ∼3.5 × 105-fold for K164C MR, relative to wild-type MR, indicating a major role for this residue in catalysis. The efficiency of Y137F MR, however, was reduced only 25-30-fold. pH-Rate profiles (log kcat vs. pH) revealed that substitution of Tyr 137 by Phe increased the kinetic pKa of Lys 166 from 5.88 ±â€¯0.02 to 7.3 ±â€¯0.2. Hence, Tyr 137 plays an important role in facilitating the reduction of the pKa of the Brønsted base Lys 166 by ∼1.4 units. Interestingly, the Phe substitution also increased the kinetic pKa of His 297 from 5.97 ±â€¯0.04 to 7.1 ±â€¯0.1. Thus, the Tyr 137-Lys 164-Lys 166 H-bonding network plays a broader role in modulating the pKa of catalytic residues by influencing the electrostatic character of the entire active site, not only by decreasing the observed pKa value of Lys 166, but also by decreasing the pKa of His 297 by 1.1 units.

Predicting herbicide movement across semi-permeable membranes using three phase partitioning.

Herbicide efficacy depends on herbicides crossing cell and organelle membranes. We evaluated an artificial membrane system to understand how herbicides cross biological membranes. This understanding aids in predicting herbicide behavior in planta and, consequently, efficacy, mode of action, and whether active transporter-based herbicide resistance mechanisms may be possible. Five herbicides with different log Kow and pKa values were assessed: glyphosate, 2,4-D, clopyralid, sulfentrazone and glufosinate. The artificial membrane apparatus included four semipermeable membranes containing buffers with pH 2.7, 5 and/or 7.4, floating in a bath of diethyl ether. These conditions were based on the pH from different cellular compartments and the pKa for these herbicides. Changes in herbicide concentration due to movement were measured using radioactivity or liquid chromatography mass spectrometry. In general, herbicide behavior followed the pattern predicted by their calculated pKa and log Kow. Herbicides added to an acidic phase (pH 2.7) were more mobile than when they were added to the more basic phase (pH 7.4), except when herbicide's pKa was lower than the pH of the starting phase. Clopyralid, 2,4-D, and sulfentrazone showed significant acid trapping behavior due to their weak acid functional groups. Sulfentrazone and 2,4-D had a high affinity for the nonpolar, diethyl ether bath, especially when they were protonated at low pH. Our findings illustrate the robustness of the system to provide predictions about herbicide behavior at the subcellular level.

Discovery of amino-1,4-oxazines as potent BACE-1 inhibitors.

New amino-1,4-oxazine derived BACE-1 inhibitors were explored and various synthetic routes developed. The binding mode of the inhibitors was elucidated by co-crystallization of 4 with BACE-1 and X-ray analysis. Subsequent optimization led to inhibitors with low double digit nanomolar activity in a biochemical and single digit nanomolar potency in a cellular assays. To assess the inhibitors for their permeation properties and potential to cross the blood-brain-barrier a MDR1-MDCK cell model was successfully applied. Compound 8a confirmed the in vitro results by dose-dependently reducing Aß levels in mice in an acute treatment regimen.

1,2,4-Triazolsulfone: A novel isosteric replacement of acylsulfonamides in the context of NaV1.7 inhibition.

Recently, the identification of several classes of aryl sulfonamides and acyl sulfonamides that potently inhibit NaV1.7 and demonstrate high levels of selectivity over other NaV isoforms have been reported. The fully ionizable nature of these inhibitors has been shown to be an important part of the pharmacophore for the observed potency and isoform selectivity. The requirement of this functionality, however, has presented challenges associated with optimization toward inhibitors with drug-like properties and minimal off-target activity. In an effort to obviate these challenges, we set out to develop an orally bioavailable, selective NaV1.7 inhibitor, lacking these acidic functional groups. Herein, we report the discovery of a novel series of inhibitors wherein a triazolesulfone has been designed to serve as a bioisostere for the acyl sulfonamide. This work culminated in the delivery of a potent series of inhibitors which demonstrated good levels of selectivity over NaV1.5 and favorable pharmacokinetics in rodents.

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

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.

pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments.

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transfer to another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pKa) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pKa prediction component to assess the accuracy with which contemporary pKa prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pKa values currently exist, predicting the pKas of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors-an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid-base titrations, we used UV absorbance-based pKa measurements to construct a high-quality experimental reference dataset of macroscopic pKas for the evaluation of computational pKa prediction methodologies that was utilized in the SAMPL6 pKa challenge. For several compounds in which the microscopic protonation states associated with macroscopic pKas were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pKa prediction methodologies on kinase inhibitor-like 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 pKa 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 pKa calculation schemes (direct, vertical, and adiabatic). The adiabatic scheme is the most accurate approach (RMSE = 1.40 pKa 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 pKa 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 pKa units, with respect to experiment, whereas including multiple local minima conformations does not necessarily improve the pKa prediction.

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

The "embedded cluster reference interaction site model" (EC-RISM) integral equation theory is applied to the problem of predicting aqueous pKa values for drug-like molecules based on an ensemble of tautomers. EC-RISM is based on self-consistent calculations of a solute's electronic structure and the distribution function of surrounding water. Following-up on the workflow developed after the SAMPL5 challenge on cyclohexane-water distribution coefficients we extended and improved the methodology by taking into account exact electrostatic solute-solvent interactions taken from the wave function in solution. As before, the model is calibrated against Gibbs energies of hydration from the "Minnesota Solvation Database" and a public dataset of acidity constants of organic acids and bases by adjusting in total 4 parameters, among which only 3 are relevant for predicting pKa values. While the best-performing training model yields a root-mean-square error (RMSE) of 1 pK unit, the corresponding test set prediction on the full SAMPL6 dataset of macroscopic pKa values using the same level of theory exhibits slightly larger error (1.7 pK units) than the best test set model submitted (1.7 pK units for corresponding training set vs. test set performance of 1.6). Post-submission analysis revealed a number of physical optimization options regarding the numerical treatment of electrostatic interactions and conformational sampling. While the experimental test set data revealed after submission was not used for reparametrizing the methodology, the best physically optimized models consequentially result in RMSEs of 1.5 if only improved electrostatic interactions are considered and of 1.1 if, in addition, conformational sampling accounts for quantum-chemically derived rankings. We conclude that these numbers are probably near the ultimate accuracy achievable with the simple 3-parameter model using a single or the two best-ranking conformations per tautomer or microstate. Finally, relations of the present macrostate approach to microstate pKa results are discussed and some illustrative results for microstate populations are presented.

Insights into proton translocation in cbb3 oxidase from MD simulations.

Heme-copper oxidases are membrane protein complexes that catalyse the final step of the aerobic respiration, namely the reduction of oxygen to water. The energy released during catalysis is coupled to the active translocation of protons across the membrane, which contributes to the establishment of an electrochemical gradient that is used for ATP synthesis. The distinctive C-type (or cbb3) cytochrome c oxidases, which are mostly present in proteobacteria, exhibit a number of unique structural and functional features, including high catalytic activity at low oxygen concentrations. At the moment, the functioning mechanism of C-type oxidases, in particular the proton transfer/pumping mechanism presumably via a single proton channel, is still poorly understood. In this work we used all-atom molecular dynamics simulations and continuum electrostatics calculations to obtain atomic-level insights into the hydration and dynamics of a cbb3 oxidase. We provide the details of the water dynamics and proton transfer pathways for both the "chemical" and "pumped" protons, and show that formation of protonic connections is strongly affected by the protonation state of key residues, namely H243, E323 and H337.

Modeling covalent-modifier drugs.

In this review, we present a summary of how computer modeling has been used in the development of covalent-modifier drugs. Covalent-modifier drugs bind by forming a chemical bond with their target. This covalent binding can improve the selectivity of the drug for a target with complementary reactivity and result in increased binding affinities due to the strength of the covalent bond formed. In some cases, this results in irreversible inhibition of the target, but some targeted covalent inhibitor (TCI) drugs bind covalently but reversibly. Computer modeling is widely used in drug discovery, but different computational methods must be used to model covalent modifiers because of the chemical bonds formed. Structural and bioinformatic analysis has identified sites of modification that could yield selectivity for a chosen target. Docking methods, which are used to rank binding poses of large sets of inhibitors, have been augmented to support the formation of protein-ligand bonds and are now capable of predicting the binding pose of covalent modifiers accurately. The pKa's of amino acids can be calculated in order to assess their reactivity towards electrophiles. QM/MM methods have been used to model the reaction mechanisms of covalent modification. The continued development of these tools will allow computation to aid in the development of new covalent-modifier drugs. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Photophysical and Photoprototropic Characteristics of 2-Aminobenzothiazole in ß-Cyclodextrin Medium.

The formation of stable inclusion complexes between neutral and monocationic species of 2-aminobenzothiazole (2ABT) with ß-CDx is observed with significant enhancement in absorbance and fluorescence intensity. The stoichiometry of the 2ABT and 2ABTH⊕ with ß-CDx is found to be 1:1 at pH 6.8 and 1.1. Their binding constant values for neutral and monocationic species are found to be 1239.3 and 1259.2 M-1, respectively. Lifetime analysis, FT-IR spectral and SEM image analysis strongly supports the formation of the inclusion complex between 2ABT and ß-CDx. Photoprototropic study shows that there is no remarkable difference in the pK a and pK a* in aqueous and ß-CDx media, which is rules out the formation of the inclusion complex with -NH2 group of 2ABT inside the cavity of ß-CDx i.e., the -NH2 group of 2ABT is in aqueous environment. Based on stoichiometry, binding constant and acidity constant values, the structure of the 1:1 complex is proposed.