Lloyd-Laney, HO, J. Yates, ND, Robinson, MJ et al. (6 more authors) (2023) Recovering Biological Electron Transfer Reaction Parameters from Multiple Protein Film Voltammetric Techniques Informed by Bayesian Inference. Journal of Electroanalytical Chemistry, 935. 117264. ISSN 1572-6657
Abstract
Deciphering the mechanism, kinetics and energetics of biological electron-transfer reactions requires a robust, rapid and reproducible protein-film voltammetry information recovery process. Here we describe a semi-automated computational approach for inferring the chemical reaction parameters for a simple protein system, a bacterial cytochrome domain from Cellvibrio japonicus that displays reversible one-electron
redox chemistry. Despite the relative simplicity of the experimental system, developing a robust data analysis approach to find the global optimum in 13-dimensional parameter space is a challenging task because the Faradaic-to-background current ratio in such experiments is often low. We describe how a multiple-technique approach, whereby data from three voltammetry techniques (direct-current, pure sinusoidal and Fourier transform alternating current voltammetry) is combined, ultimately enables the automatic extraction of both (i) quantitative “best-fit” redox reaction parameter point values that are robust across multiple experiments performed on different protein-electrode films, and (ii) a statistical description of parameter correlation relationships, along with uncertainty in the individual parameter values, obtained using Bayesian inference. It is the latter achievement which is particularly important as it represents a method for visualising the possible limitations in the mathematical model of the experimental system. Our multi-voltammetry analysis approach enables such powerful insight because of the complementarity between the information content, simulation-speed and parameter sensitivity of the current-time data generated by the different techniques, illustrating the value of adding purely sinusoidal voltammetry to the bioelectrochemistry measurement toolkit.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Voltammetry, Inference, Bio-electrochemistry |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Feb 2023 12:32 |
Last Modified: | 20 Feb 2024 01:13 |
Published Version: | http://dx.doi.org/10.1016/j.jelechem.2023.117264 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jelechem.2023.117264 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196859 |
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