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      Engineering the Turnover Stability of Cellobiose Dehydrogenase toward Long-Term Bioelectronic Applications

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          Abstract

          Cellobiose dehydrogenase (CDH) is an attractive oxidoreductase for bioelectrochemical applications. Its two-domain structure allows the flavoheme enzyme to establish direct electron transfer to biosensor and biofuel cell electrodes. Yet, the application of CDH in these devices is impeded by its limited stability under turnover conditions. In this work, we aimed to improve the turnover stability of CDH by semirational, high-throughput enzyme engineering. We screened 13 736 colonies in a 96-well plate setup for improved turnover stability and selected 11 improved variants. Measures were taken to increase the reproducibility and robustness of the screening setup, and the statistical evaluation demonstrates the validity of the procedure. The selected CDH variants were expressed in shaking flasks and characterized in detail by biochemical and electrochemical methods. Two mechanisms contributing to turnover stability were found: (i) replacement of methionine side chains prone to oxidative damage and (ii) the reduction of oxygen reactivity achieved by an improved balance of the individual reaction rates in the two CDH domains. The engineered CDH variants hold promise for the application in continuous biosensors or biofuel cells, while the deduced mechanistic insights serve as a basis for future enzyme engineering approaches addressing the turnover stability of oxidoreductases in general.

          Abstract

          In this work, we aimed to improve the turnover stability of CDH by semirational, high-throughput enzyme engineering.

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          Most cited references71

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          Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

          Background Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. Results We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. Conclusion Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives.
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            Engineering the third wave of biocatalysis.

            Over the past ten years, scientific and technological advances have established biocatalysis as a practical and environmentally friendly alternative to traditional metallo- and organocatalysis in chemical synthesis, both in the laboratory and on an industrial scale. Key advances in DNA sequencing and gene synthesis are at the base of tremendous progress in tailoring biocatalysts by protein engineering and design, and the ability to reorganize enzymes into new biosynthetic pathways. To highlight these achievements, here we discuss applications of protein-engineered biocatalysts ranging from commodity chemicals to advanced pharmaceutical intermediates that use enzyme catalysis as a key step.
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              Extracellular electron transfer systems fuel cellulose oxidative degradation.

              Ninety percent of lignocellulose-degrading fungi contain genes encoding lytic polysaccharide monooxygenases (LPMOs). These enzymes catalyze the initial oxidative cleavage of recalcitrant polysaccharides after activation by an electron donor. Understanding the source of electrons is fundamental to fungal physiology and will also help with the exploitation of LPMOs for biomass processing. Using genome data and biochemical methods, we characterized and compared different extracellular electron sources for LPMOs: cellobiose dehydrogenase, phenols procured from plant biomass or produced by fungi, and glucose-methanol-choline oxidoreductases that regenerate LPMO-reducing diphenols. Our data demonstrate that all three of these electron transfer systems are functional and that their relative importance during cellulose degradation depends on fungal lifestyle. The availability of extracellular electron donors is required to activate fungal oxidative attack on polysaccharides.
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                Author and article information

                Journal
                ACS Sustain Chem Eng
                ACS Sustain Chem Eng
                sc
                ascecg
                ACS Sustainable Chemistry & Engineering
                American Chemical Society
                2168-0485
                12 May 2021
                24 May 2021
                : 9
                : 20
                : 7086-7100
                Affiliations
                []Biocatalysis and Biosensing Laboratory, Department of Food Science and Technology, BOKU − University of Natural Resources and Life Sciences , Muthgasse 18, 1190 Vienna, Austria
                []DirectSens Biosensors GmbH , Am Rosenbühel 38, 3400 Klosterneuburg, Austria
                Author notes
                [* ]E-mail: alfons.felice@ 123456directsens.com . Telephone: +436505000167.
                Author information
                http://orcid.org/0000-0002-5058-5874
                http://orcid.org/0000-0002-3842-1471
                http://orcid.org/0000-0002-8722-8176
                Article
                10.1021/acssuschemeng.1c01165
                8296668
                34306835
                1582a4c1-efbe-42d6-88c7-7fc869802169
                © 2021 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 21 February 2021
                : 16 April 2021
                Funding
                Funded by: H2020 European Research Council, doi 10.13039/100010663;
                Award ID: 726396
                Funded by: Österreichische Forschungsförderungsgesellschaft, doi 10.13039/501100004955;
                Award ID: 867994
                Funded by: Austrian Science Fund, doi 10.13039/501100002428;
                Award ID: FWF-W1224
                Funded by: H2020 Marie Sklodowska-Curie Actions, doi 10.13039/100010665;
                Award ID: 813006
                Categories
                Research Article
                Custom metadata
                sc1c01165
                sc1c01165

                cellobiose dehydrogenase,high-throughput screening,turnover stability,flavoenzymes,direct electron transfer

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