Project description
New methods for next-generation biocatalyst design
Directed evolution has advanced enzyme use in industry by enabling customisation for specific needs. Continuous evolution (CE) methods streamline this process within a replicating organism, allowing for the rapid development of efficient enzymes. However, most approaches focus on model enzymes with limited industrial application. The ERC-funded ContiZymes project will expand the potential of CE by establishing a low-tech circular economy platform for biocatalysts. It will integrate a versatile selection system with gene diversification strategies and an autonomous setup for continuous bacterial growth. This integration will enable the exploration of evolutionary paths to address challenges in C-C, C-halogen, and C-N bond-forming reactions, engineering enzymes while mapping their fitness landscapes to facilitate on-demand design of next-generation biocatalysts.
Objective
Directed evolution has revolutionized the application of enzymes in industrial settings by allowing users to tailor the properties and activities of biocatalysts to their needs. But classic directed evolution is notoriously labor- and time-intensive, as it manually stages mutation, selection, and amplification cycles. In contrast, continuous evolution (CE) approaches aim to achieve these steps within a replicating organism, making it possible to engineer efficient enzymes in a matter of days rather than months or years. Unfortunately, current CE approaches are typically applicable only to model enzymes with little industrial value.
To unleash the full potential of CE, we will develop a scalable, low-tech CE platform, which is readily applicable to biocatalysts that provide value-added products. Toward this end, we will merge a versatile selection system we recently developed with strategies to diversify the genes of targeted enzymes in vivo and an autonomous setup to grow bacterial populations continuously. Combined the resulting CE-platform will enable us to engineer biocatalysts along many and long evolutionary trajectories. Moreover, analyzing the fate of these populations by sequencing will allow us to map the sequence-structure-function relationships of these biocatalysts. Based on the systematic datasets generated in these efforts, we will train machine-learning (ML)-models to predict functional sequences. Lastly, in a ML-directed CE approach, we will establish a design-build-test-learn cycle to improve models and guide CEs toward promising, but otherwise inaccessible sequence spaces.
Overall, ContiZymes will overcome unaddressed challenges associated with the application of biocatalysts that promote sought-after C-C, C-halogen, and C-N-bond forming reactions. We will not only engineer these enzymes at an unprecedented rate and scale, but also map their fitness landscapes and take a critical step toward the on-demand design of next-generation biocatalyst
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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(opens in new window) ERC-2023-COG
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9712CP Groningen
Netherlands
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