Linear competitive inhibition of human tissue kallikrein by 4-aminobenzamidine and benzamidine and linear mixed inhibition by 4-nitroaniline and aniline

Thiohydantoins and hydantoins derived from amino acids as potent urease inhibitors: Inhibitory activity and ligand-target interactions

We report the investigation of hydantoins and thiohydantoins derived from L and d-amino acids as inhibitors against the Canavalia ensiformis urease (CEU). The biochemical in vitro assay against CEU revealed a promising inhibitory potential for most thiohydantoins with six of them showing %I higher than the reference inhibitor thiourea (56.5%). In addition, thiohydantoin derived from l-valine, 1b, as well as the hydantoin 2d, derived from l-methionine, were identified as the most potent inhibitors with %I = 90.5 and 85.9 respectively. Enzyme kinetic studies demonstrated a mixed and uncompetitive inhibition profile for these compounds with K_i values of 0.42 mM for 1b and 0.99 mM for 2d. These kinetic parameters, obtained from traditional colorimetric assay, were strictly related to the K_D values measured spectroscopically by the Saturation Transfer Difference (STD) technique for the urease complex. STD was also used to evince the moieties of the ligands responsible for the binding with the enzyme. Molecular docking studies showed that the thiohydantoin and hydantoin rings can act as a pharmacophoric group due to their binding affinity by hydrogen bonding interactions with critical amino acid residues in the enzyme active and/or allosteric site. These findings agreed with the experimental alpha values, demonstrating that 1b has affinity by free enzyme, and 2d derivative, an uncompetitive inhibitor, has great binding affinity at the allosteric site. The results for the thiohydantoin 1a, derived from d-valine, demonstrated a drastic stereochemical influence on inhibition, kinetics, and binding parameters in comparison to its enantiomer 1b.

Comparing enzyme activity modifier equations through the development of global data fitting templates in Excel

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1 + ([I]∕K_i)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel and via the global fitting of these equations to simulated and previously published datasets. In both cases, this modifier equation was able to match or outperform the other equations by providing superior fits to the datasets. The ability of this single equation to outperform the other equations suggests an over-complication of the field. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.

Are improper kinetic models hampering drug development?

Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits γ-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions.

Limitations of conventional inhibitor classifications

Enzyme inhibitors are usually classified as competitive, non-competitive or mixed non-competitive. Each of these designations has a serious limitation in that it only describes an extreme of inhibitory behaviour. The non-competitive inhibition equation only considers an approach to complete inhibition of the catalytic turnover rate, while the competitive inhibition equation predicts an infinite increase in the Michaelis-Menten constant (decrease in enzyme affinity for substrate), resulting from increased inhibitor concentration. Both of these models exclude the possibility of a finite inhibitor-induced change in the kinetic parameters of the enzyme they are affecting. They also exclude the possibility of an inhibitor affecting both the substrate affinity and the catalytic turnover at the same time. Mixed non-competitive inhibition describes a hybrid form of inhibition displaying some characteristics of both competitive and non-competitive inhibition. It also suffers from an inability to describe finite changes in activity and to describe concomitant changes in substrate affinity and catalytic turnover. Two inhibitor binding constants are invoked in this equation, suggesting that such inhibitors interact with the enzyme in two completely independent manners. From these considerations, it is suggested here that conventional equations do not adequately describe observed kinetic data due to a lack of distinction between the mass action binding term describing inhibitor-enzyme association and the terms representing the actual effect of the inhibitor on the enzyme. Herein we describe an alternate approach for representing enzyme activity modulation based on a re-examination of conventional inhibition equations. The arguments presented are illustrated using the known competitive inhibition of Kallikrein with benzamidine.

The Effect of Cations on the Amidase Activity of Human Tissue Kallikrein: 1-Linear Competitive Inhibition by Sodium, Potassium, Calcium and Magnesium. 2-Linear Mixed Inhibition by Aluminium

Hydrolysis of D-valyl-L-leucyl-L-arginine p-nitroanilide by human tissue kallikrein (hK1) was studied in the absence and in the presence of increasing concentrations of the following chloride salts: sodium, potassium, calcium, magnesium and aluminium. The data indicate that the inhibition of hK1 by sodium, potassium, calcium and magnesium is linear competitive and that divalent cations are more potent inhibitors of hK1 than univalent cations. However the inhibition of hK1 by aluminium cation is linear mixed, with the cation being able to bind to both the free enzyme and the ES complex. This cation was the best hK1 inhibitor. Aluminium is not a physiological cation, but is a known neurotoxicant for animals and humans. The neurotoxic actions of aluminium may relate to neuro-degenerative diseases.

Structural transformation of cytochrome c and apo cytochrome c induced by sulfonated polystyrene

The structural transformation of cytochrome c (cyt c) and its heme-free precursor, apo cyt c, induced by negatively charged sulfonated polystyrene (SPS) with different charge density (degree of sulfonation) and chain length was studied to understand the factors that influence the folding and unfolding of the protein. SPS forms stable transparent nanoparticles in aqueous solution. The hydrophobic association of the backbone chain and phenyl groups is balanced by the electrostatic repulsion of the sulfonate groups on the particle surface. The binding of cyt c to negatively charged SPS particles causes an extensive disruption of the native compact structure of cyt c: the cleavage of Fe-Met80 ligand, about 40% loss of the helical structure, and the disruption of the asymmetry environment of Trp59. On the other hand, SPS particle-bound apo cyt c undergoes a conformational change from the random coil to alpha-helical structure. The folding of apo cyt c in SPS particles was influenced by pH and ionic strength of the solution, SPS concentration, and the degree of sulfonation and chain length of SPS. The folding can reach more than 90% of the alpha-helix content of native cyt c in solution. Poly(sodium 4-styrenesulfonate) (PSS), which is 100% sulfonated polystyrene and cannot form hydrophobic cores in the solution, induces only two-thirds of the alpha-helix content compared with SPS. It appears that the electrostatic interaction between PSS/SPS and apo cyt c induces an early partially folded state of apo cyt c. The hydrophobic interaction between nonpolar residues in apo cyt c and the hydrophobic cores in SPS particles extends the alpha-helical structure of apo cyt c.

Kinetic peculiarities of human tissue kallikrein: 1--substrate activation in the catalyzed hydrolysis of H-D-valyl-L-leucyl-L-arginine 4-nitroanilide and H-D-valyl-L-leucyl-L-lysine 4-nitroanilide; 2--substrate inhibition in the catalyzed hydrolysis of N alpha-p-tosyl-L-arginine methyl ester

Hydrolysis of D-valyl-L-leucyl-L-lysine 4-nitroanilide (1), D-valyl-L-leucyl-L-arginine 4-nitroanilide (2), and N alpha-p-tosyl-L-arginine methyl ester (3) by human tissue kallikrein was studied throughout a wide range of substrate concentrations. At low substrate concentrations, the hydrolysis followed Michaelis-Menten kinetics but, at higher substrate concentrations, a deviation from Michaelis-Menten behavior was observed. With the nitroanilides, a significant increase in hydrolysis rates was observed, while with the ester, a significant decrease in hydrolysis rates was observed. The results for substrates (1) and (3) can be accounted for by a model based on the hypothesis that a second substrate molecule binds to the ES complex to produce a more active or an inactive SES complex. The deviation observed for substrate (2) can be explained as a bimolecular reaction between the enzyme-substrate complex and a free substrate molecule.