Electrifying Peptide Synthesis for Directed Evolution of Artificial Enzymes
This project aims to develop robust artificial enzymes through directed evolution with artificial amino acids, enhancing energy conversion efficiency for renewable energy applications.
Projectdetails
Introduction
Global climate and energy challenges require efficient, robust, and scalable catalysts for the conversion of renewable energies. Nature has evolved extremely active catalysts (enzymes) for the conversion of small molecules relevant to energy (H2, CO2, N2). The scalability of these enzymes offers distinct advantages over the rare, precious metals that are currently used in energy conversion.
Challenges with Enzymes
Unfortunately, the enzymes are unable to tolerate the extreme conditions of operating fuel cells or electrolyzers. Directed evolution is a powerful approach for improving enzymes, but it is mostly restricted to natural amino acids and biological conditions, with limited compatibility for evolving enzymes toward enhanced resistance in abiotic systems.
Proposed Approach
Here, I aim to establish directed evolution in fully abiotic systems, using artificial amino acids to make artificial enzymes that are stable even in extreme conditions. Towards this, I will establish new electrochemical peptide synthesis platforms to enable the generation of enzyme-length peptides using both natural and artificial amino acids.
Methodology
- Extended libraries of artificial enzyme variants will be produced.
- These variants will be screened directly on electrode microarrays.
- Top enzyme candidates for the conversion of H2 will be selected using fuel cell/electrolyzer conditions as the evolutionary criteria.
Expected Outcomes
By the end, I will have a new procedure for synthesizing libraries of full-length artificial proteins, enabling the creation of thousands of enzyme variants using artificial building blocks. The generation of high-quality datasets will be transformative to drive future machine learning-based evolution steps for both full-size enzymes and small-molecule catalysts with applications beyond H2 evolution.
Conclusion
We will have discovered highly active catalysts able to sustain conditions of large-scale energy conversion devices, accelerating breakthroughs toward the economically competitive use of renewable energies for fuel and chemical production.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.997.993 |
Totale projectbegroting | € 1.997.993 |
Tijdlijn
Startdatum | 1-9-2022 |
Einddatum | 31-8-2027 |
Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- TECHNISCHE UNIVERSITAET MUENCHENpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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Computational design of industrial enzymes for green chemistryGREENZYME aims to revolutionize enzyme design using deep learning and computational methods to create efficient, eco-friendly catalysts, reducing drug production costs and promoting green chemistry. | ERC Proof of... | € 150.000 | 2023 | Details |
Evolution of shape-defined macromolecules into functional systems
Develop abiotic enzymes by fine-tuning macromolecular shape and sequence to catalyze chemical transformations in non-physiological environments, rivaling natural enzyme functionality.
Continuous Enzyme Evolution – solving bottlenecks in enzyme engineering to design next-generation biocatalysts
The ContiZymes project aims to develop a scalable continuous evolution platform for rapidly engineering valuable biocatalysts, enhancing enzyme efficiency and mapping their functional properties.
Directed Evolution of Metastable Electrocatalyst Interfaces for Energy Conversion
This project aims to revolutionize electrocatalysis by leveraging high entropy materials and advanced techniques to discover stable, active catalysts for energy conversion reactions.
Development of rationally designed enzyme kits
KITZYME aims to create patentable enzyme kits for stereoselective carbon-carbon bond formation using advanced computational methods to enhance catalytic efficiency sustainably and cost-effectively.
Computational design of industrial enzymes for green chemistry
GREENZYME aims to revolutionize enzyme design using deep learning and computational methods to create efficient, eco-friendly catalysts, reducing drug production costs and promoting green chemistry.
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ECOMO aims to innovate sustainable production of high-value diamines from CO2 and nitrogen using bioelectrocatalysis and engineered microbes, enhancing chemical industry building blocks.
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