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Enzyme Kinetics Analysis: An Online Tool for Analyzing Enzyme Initial Rate Data and Teaching Enzyme Kinetics

Authors :
Daniel A. Mak
Sebastian Dunn
David Coombes
Carlo R. Carere
Jane R. Allison
Volker Nock
André O. Hudson
Renwick C. J. Dobson
Source :
Biochemistry and Molecular Biology Education. 2024 52(3):348-358.
Publication Year :
2024

Abstract

Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and cellular biology to pharmacology. However, because enzyme kinetics involves concepts rarely employed in other areas of biology, it can be challenging for students and researchers. Traditional graphical analysis was replaced by computational analysis, requiring another skill not core to many life sciences curricula. Computational analysis can be time-consuming and difficult in free software (e.g., R) or require costly software (e.g., GraphPad Prism). We present Enzyme Kinetics Analysis (EKA), a web-tool to augment teaching and learning and streamline EKA. EKA is an interactive and free tool for analyzing enzyme kinetic data and improving student learning through simulation, built using R and RStudio's ShinyApps. EKA provides kinetic models (Michaelis-Menten, Hill, simple reversible inhibition models, ternary-complex, and ping-pong) for users to fit experimental data, providing graphical results and statistics. Additionally, EKA enables users to input parameters and create data and graphs, to visualize changes to parameters (e.g., K[subscript M] or number of measurements). This function is designed for students learning kinetics but also for researchers to design experiments. EKA (enzyme-kinetics.shinyapps.io/enzkinet_webpage/) provides a simple, interactive interface for teachers, students, and researchers to explore enzyme kinetics. It gives researchers the ability to design experiments and analyze data without specific software requirements.

Details

Language :
English
ISSN :
1470-8175 and 1539-3429
Volume :
52
Issue :
3
Database :
ERIC
Journal :
Biochemistry and Molecular Biology Education
Notes :
https://github.com/damacer/enzKinet2.git
Publication Type :
Academic Journal
Accession number :
EJ1425274
Document Type :
Journal Articles<br />Reports - Descriptive
Full Text :
https://doi.org/10.1002/bmb.21823