| 1. |
- Dagys, Marius, et al.
(författare)
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Oxygen electroreduction catalysed by laccase wired to gold nanoparticles via the trinuclear copper cluster
- 2017
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Ingår i: Energy & Environmental Science. - : Royal Society of Chemistry. - 1754-5692 .- 1754-5706. ; 10:2, s. 498-502
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Tidskriftsartikel (refereegranskat)abstract
- Specific wiring of biocatalysts par excellence, viz. redox enzymes, to an electrode can be exploited in the fabrication of high-performance bioelectronic devices. Here we report oxygen electroreduction catalysed by Didymocrea sp. J6 laccase wired to gold nanoparticles via the trinuclear copper cluster. Bypassing the intramolecular electron transfer, which under certain conditions is the rate-limiting step of oxygen bioelectroreduction, has resulted in the fabrication of a high current density biocathode based on high-redox-potential laccase, which is able to operate in electrolytes with a broad pH range in the presence of high fluoride concentrations.
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| 2. |
- Laurynenas, Audrius, et al.
(författare)
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Consecutive Marcus Electron and Proton Transfer in Heme Peroxidase Compound II-Catalysed Oxidation Revealed by Arrhenius Plots
- 2019
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Electron and proton transfer reactions in enzymes are enigmatic and have attracted a great deal of theoretical, experimental, and practical attention. The oxidoreductases provide model systems for testing theoretical predictions, applying experimental techniques to gain insight into catalytic mechanisms, and creating industrially important bio(electro) conversion processes. Most previous and ongoing research on enzymatic electron transfer has exploited a theoretically and practically sound but limited approach that uses a series of structurally similar ("homologous") substrates, measures reaction rate constants and Gibbs free energies of reactions, and analyses trends predicted by electron transfer theory. This approach, proposed half a century ago, is based on a hitherto unproved hypothesis that pre-exponential factors of rate constants are similar for homologous substrates. Here, we propose a novel approach to investigating electron and proton transfer catalysed by oxidoreductases. We demonstrate the validity of this new approach for elucidating the kinetics of oxidation of "non-homologous" substrates catalysed by compound II of Coprinopsis cinerea and Armoracia rusticana peroxidases. This study-using the Marcus theory-demonstrates that reactions are not only limited by electron transfer, but a proton is transferred after the electron transfer event and thus both events control the reaction rate of peroxidase-catalysed oxidation of substrates.
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| 3. |
- Repecka, Donatas, et al.
(författare)
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Expanding functional protein sequence spaces using generative adversarial networks
- 2021
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Ingår i: Nature Machine Intelligence. - : Springer Science and Business Media LLC. - 2522-5839. ; 3:4, s. 324-333
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Tidskriftsartikel (refereegranskat)abstract
- De novo protein design for catalysis of any desired chemical reaction is a long-standing goal in protein engineering because of the broad spectrum of technological, scientific and medical applications. However, mapping protein sequence to protein function is currently neither computationally nor experimentally tangible. Here, we develop ProteinGAN, a self-attention-based variant of the generative adversarial network that is able to ‘learn’ natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino-acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase (MDH) as a template enzyme, we show that 24% (13 out of 55 tested) of the ProteinGAN-generated and experimentally tested sequences are soluble and display MDH catalytic activity in the tested conditions in vitro, including a highly mutated variant of 106 amino-acid substitutions. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse functional proteins within the allowed biological constraints of the sequence space.
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