An uncharacteristically low-potential flavin governs the energy landscape of electron bifurcation

Wise, Courtney E.; Ledinina, Anastasia E.; Mulder, David W.; Chou, Katherine; Peters, John W.; King, Paul W.; Lubner, Carolyn E.

doi:10.1073/pnas.2117882119

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…An increase of experimental information would also allow the development of predictive models for E 1 and E 2 or for the gap between the two. This last application would be particularly intriguing, since the separation between flavin first and second redox potentials has recently emerged as a key feature for the design of electron bifurcating proteins, with potential implications in the context of energy conversion. ,,− Finally, an important advantage of the model is that all the considered features can be automatically extracted from the PDB file of the associated protein. We would also like to specify that we reperformed predictions including additional features, describing the pattern of interaction of the isoalloxazine ring of the flavin with the protein neighborhood (e.g., presence of H-bonds or aromatic/aliphatic stacking interactions between one atom of the isoalloxazine ring and the protein matrix).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case

Galuzzi

Mirarchi

Viganò

et al. 2022

J. Chem. Inf. Model.

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Determining the redox potentials of protein cofactors and how they are influenced by their molecular neighborhoods is essential for basic research and many biotechnological applications, from biosensors and biocatalysis to bioremediation and bioelectronics. The laborious determination of redox potential with current experimental technologies pushes forward the need for computational approaches that can reliably predict it. Although current computational approaches based on quantum and molecular mechanics are accurate, their large computational costs hinder their usage. In this work, we explored the possibility of using more efficient QSPR models based on machine learning (ML) for the prediction of protein redox potential, as an alternative to classical approaches. As a proof of concept, we focused on flavoproteins, one of the most important families of enzymes directly involved in redox processes. To train and test different ML models, we retrieved a dataset of flavoproteins with a known midpoint redox potential (E m) and 3D structure. The features of interest, accounting for both short- and long-range effects of the protein matrix on the flavin cofactor, have been automatically extracted from each protein PDB file. Our best ML model (XGB) has a performance error below 1 kcal/mol (∼36 mV), comparing favorably to more sophisticated computational approaches. We also provided indications on the features that mostly affect the E m value, and when possible, we rationalized them on the basis of previous studies.

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Section: Discussionmentioning

confidence: 99%

Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case

Galuzzi

Mirarchi

Viganò

et al. 2022

J. Chem. Inf. Model.

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“…Another ATP‐dependent low‐potential substrate‐reducing enzyme system has been shown to catalyse defluorination from an aromatic ring (Tiedt et al., 2016 ). Most recently, many new electron‐bifurcating enzyme systems have been discovered and shown to attain a reduction potential as low as −911 mV vs the standard hydrogen electrode (Alleman & Peters, 2023 ; Mostafa et al., 2022 ; Wise et al., 2022 ). These recent reports offer an as‐yet‐underexplored potential for PFAS biodegradation.…”

Section: Many Types Of C–f Bonds In Pfas...mentioning

confidence: 99%

Evolutionary obstacles and not C–F bond strength make PFAS persistent

Wackett

2024

Microbial Biotechnology

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The fate of organic matter in the environment, including anthropogenic chemicals, is largely predicated on the enzymatic capabilities of microorganisms. Microbes readily degrade, and thus recycle, most of the ~100,000 commercial chemicals used in modern society. Per‐ and polyfluorinated compounds (PFAS) are different. Many research papers posit that the general resistance of PFAS to microbial degradation is based in chemistry and that argument relates to the strength of the C–F bond. Here, I advance the opinion that the low biodegradability of PFAS is best formulated as a biological optimization problem, hence evolution. The framing of the problem is important. If it is framed around C–F bond strength, the major effort should focus on finding and engineering new C–F cleaving enzymes. The alternative, and preferred approach suggested here, is to focus on the directed evolution of biological systems containing known C–F cleaving systems. There are now reports of bacteria degrading and/or growing on multiply fluorinated arenes, alkenoic and alkanoic acids. The impediment to more efficient and widespread biodegradation in these systems is biological, not chemical. The rationale for this argument is made in the five sections below that follow the Introduction.

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“…This occurs when the rate of electron transfer is slow or the scan rate fast, so that the redox species are not in equilibrium with the electrode potential. The value of Δ E 0 was sometimes directly estimated from the peak separation, or the value of the inverted potential directly from the peak position (such as, e.g., in the interpretation of the square-wave voltammogram in ref ), and indeed it has been claimed that the two peak potentials should be “close to” the values of E 0 (see, e.g., ref , p 406). We shall explain below that this is a misconception.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Extent of Potential Inversion by Cyclic Voltammetry: Theory, Pitfalls, and Application to a Nickel Complex with Redox-Active Iminosemiquinone Ligands

et al. 2023

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Potential inversion refers to the situation where a protein cofactor or a synthetic molecule can be oxidized or reduced twice in a cooperative manner; that is, the second electron transfer is easier than the first. This property is very important regarding the catalytic mechanism of enzymes that bifurcate electrons and the properties of bidirectional redox molecular catalysts that function in either direction of the reaction with no overpotential. Cyclic voltammetry is the most common technique for characterizing the thermodynamics and kinetics of electron transfer to or from these molecules. However, a gap in the literature is the absence of analytical predictions to help interpret the values of the voltammetric peak potentials when potential inversion occurs; the cyclic voltammograms are therefore often analyzed by simulating the data, with no discussion of the possibility of overfitting and often no estimation of the error on the determined parameters. Here we formulate the theory for the voltammetry of freely diffusing or surface-confined two-electron redox species in the experimentally relevant irreversible limit where the peak separation depends on the scan rate. We explain why the model is intrinsically underdetermined, and we illustrate this conclusion by analysis of the voltammetry of a nickel complex with redox-active iminosemiquinone ligands. Being able to characterize the thermodynamics of two-electron electron-transfer reactions will be crucial for designing more efficient catalysts.

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An uncharacteristically low-potential flavin governs the energy landscape of electron bifurcation

Cited by 7 publications

References 52 publications

Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case

Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case

Evolutionary obstacles and not C–F bond strength make PFAS persistent

Assessing the Extent of Potential Inversion by Cyclic Voltammetry: Theory, Pitfalls, and Application to a Nickel Complex with Redox-Active Iminosemiquinone Ligands

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