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

González, M.; Cornell-Kennon, Susan; Schaefer, Erik; Kuzmič, Petr

doi:10.1016/j.ab.2016.11.001

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…However, the latter numerical approach can have drawbacks that are widely ignored and show several weak points 12 , although the most common computing method to progress–curve analysis these days is to use numerical integration solvers linked to nonlinear regression algorithms. Hence, we also estimated the kinetic parameters in terms of an algebraic approach that gives the direct solution of Equation (1) to the integrated Michaelis–Menten rate equation (Table 1, DynaFit Lambert W) 9 . Unfortunately, the requirement for the Lambert W ( x ) built-in function in software (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…Progress curve experimental data were analysed by nonlinear regression fitting programme Dynafit using either numerical integration of differential equations that describe simple Michaelis–Menten reaction model E + S ⇆ ES → E + P or newly described algebraic solution for the Michaelis–Menten equation, which includes the Lambert W ( x ) function 9 , 10 : …”

Section: Methodsmentioning

confidence: 99%

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Evaluation of the paraoxonase-1 kinetic parameters of the lactonase activity by nonlinear fit of progress curves

Goličnik

Bavec

2019

Journal of Enzyme Inhibition and Medicinal Chemistry

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Although paraoxonase-1 (PON1) activity has been demonstrated to be a reliable biomarker of various diseases, clinical studies have been based only on relative comparison of specific enzyme activities, which capture differences mainly due to (usually unknown) PON1 concentration. Hence, the aim of this report is to present for the first time the simple evaluation method for determining autonomous kinetic parameter of PON1 that could be also associated with polymorphic forms and diseases; i.e. the Michaelis constant which is enzyme concentration independent quantity. This alternative approach significantly reduces the number of experiments needed, and it yields the results with great accuracy. ARTICLE HISTORY

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evaluation of the paraoxonase-1 kinetic parameters of the lactonase activity by nonlinear fit of progress curves

Goličnik

Bavec

2019

Journal of Enzyme Inhibition and Medicinal Chemistry

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“…An algebraic correction for the observed nonlinearity due to spontaneous enzyme degradation can result in good estimates of kinetic parameters (Figure F); however, this type of algebraic correction does not accurately correct nonlinearity due to substrate depletion. Thus, numeric fitting is an alternative to fit the progress curve that results from depletion of substrate, enzyme, as well as inhibitor. , …”

Section: Introductionmentioning

confidence: 99%

“…Thus, numeric fitting is an alternative to fit the progress curve that results from depletion of substrate, enzyme, as well as inhibitor. 41,43 Following the same strategy, we can derive the rate equation for a two-step reversible process (Figure 1C) which is given in eq 5 and Figure S2. From the plot, we can calculate the steady state inhibition constant K i app and use it as a measure of potency for a reversible covalent inhibitor.…”

Section: ■ Introductionmentioning

confidence: 99%

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Hasan,

Ray,

Saha

2023

J. Phys. Chem. B

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Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

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Inspecting the Role of PI3K/AKT Signaling Pathway in Cancer Development Using an In Silico Modeling and Simulation Approach

González-Pérez

Cárdenas-García

2018

Bioinformatics and Biomedical Engineering

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

Cited by 7 publications

References 20 publications

Evaluation of the paraoxonase-1 kinetic parameters of the lactonase activity by nonlinear fit of progress curves

Evaluation of the paraoxonase-1 kinetic parameters of the lactonase activity by nonlinear fit of progress curves

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Inspecting the Role of PI3K/AKT Signaling Pathway in Cancer Development Using an In Silico Modeling and Simulation Approach

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