Reviewing the experimental and mathematical factors involved in tight binding inhibitors Ki values determination: The bi-functional protease inhibitor SmCI as a test model.

Different methodologies for determining the dissociation equilibrium constant (Ki) of protein tight binding inhibitors are frequently found in the scientific literature. Taking into account that the Ki value is the main parameter characterizing the inhibition strength, its determination often represents the first step during the characterization of a potential drug. The purpose of this review is to summarize the current information related to tight binding inhibitors Ki values determination and discuss about the importance of different factors as the enzyme concentration, the inhibitor concentration dilution series, the enzyme-inhibitor incubation time and the dose-response data mathematical fitting. For this aim, the bi-functional SmCI protease inhibitor is used as a tool for exemplifying the experimental and mathematical steps performed during tight binding inhibitors Ki values determination. In addition, the natural and the different recombinant forms of SmCI were used to go deeply into the comparison of some mathematic approaches that are frequently used in the literature. Finally, other biochemical techniques that could be potentially used for tight binding inhibitors Ki values determination are also commented.

Sabellastarte magnifica Carboxypeptidase Inhibitor: The first Kunitz inhibitor simultaneously interacting with carboxypeptidases and serine proteases.

Multi-domain inhibitors capable to block the activity of different classes of proteases are not very common in nature. However, these kinds of molecules are attractive systems for biomedical or biotechnological applications, where two or more different targets need to be neutralized. SmCI, the Sabellastarte magnifica Carboxypeptidase Inhibitor, is a tri-domain BPTI-Kunitz inhibitor capable to inhibit serine proteases and A-like metallocarboxypeptidases. The BPTI-Kunitz family of proteins includes voltage gated channel blockers and inhibitors of serine proteases. SmCI is therefore, the only BPTI-Kunitz protein capable of inhibiting metallocarboxypeptidases. The X-ray structure of the SmCI-carboxypeptidase A complex previously obtained by us, revealed that this enzyme interacts with SmCI N-tail. In the complex, the reactive loops for serine protease inhibition remain fully exposed to the solvent in each domain, suggesting SmCI can simultaneously interact with multiple serine proteases. The twofold goals of this study were: i) to establish serine proteases-SmCI binding stoichiometry, given that the inhibitor is comprised of three potential binding domains; and ii) to determine whether or not SmCI can simultaneously bind both classes of enzymes, to which it binds individually. Our experimental approach included a variety of techniques for the study of protein-protein interactions, using as model enzymes pancreatic trypsin, elastase and carboxypeptidase A. In particular, we combined information obtained from gel filtration chromatography, denaturing electrophoresis, nuclear magnetic resonance spectroscopy and enzyme inhibition assays. Our results show that SmCI is able to bind three trypsin molecules under saturating conditions, but only one elastase interacts with the inhibitor. Additionally, we demonstrated that SmCI can bind serine proteases and carboxypeptidases at the same time (at least in the ratio 1:1:1), becoming the first protease inhibitor that simultaneously blocks these two mechanistic classes of enzymes.

An algebraic model to determine substrate kinetic parameters by global nonlinear fit of progress curves.

We propose that the time course of an enzyme reaction following the Michaelis-Menten reaction mechanism can be conveniently described by a newly derived algebraic equation, which includes the Lambert Omega function. Following Northrop's ideas [Anal. Biochem.321, 457-461, 1983], the integrated rate equation contains the Michaelis constant (KM) and the specificity number (kS≡kcat/KM) as adjustable parameters, but not the turnover number kcat. A modification of the usual global-fit approach involves a combinatorial treatment of nominal substrate concentrations being treated as fixed or alternately optimized model parameters. The newly proposed method is compared with the standard approach based on the "initial linear region" of the reaction progress curves, followed by nonlinear fit of initial rates to the hyperbolic Michaelis-Menten equation. A representative set of three chelation-enhanced fluorescence EGFR kinase substrates is used for experimental illustration. In one case, both data analysis methods (linear and nonlinear) produced identical results. However, in another test case, the standard method incorrectly reported a finite (50-70 µM) KM value, whereas the more rigorous global nonlinear fit shows that the KM is immeasurably high.