Continuous Enzyme Evolution – solving bottlenecks in enzyme engineering to design next-generation biocatalysts | ContiZymes | Projet | Fiche descriptive | HORIZON | CORDIS

Description du projet

Nouvelles méthodes pour la conception de biocatalyseurs de nouvelle génération

L’évolution dirigée a fait progresser l’utilisation des enzymes dans l’industrie en permettant de les adapter à des besoins spécifiques. Les méthodes d’évolution continue (EC) rationalisent ce processus au sein d’un organisme qui se réplique, ce qui permet le développement rapide d’enzymes efficaces. Cependant, la plupart des approches se concentrent sur des enzymes modèles dont l’application industrielle est limitée. Le projet ContiZymes, financé par le CER, élargira le potentiel de l’EC en établissant une plateforme d’économie circulaire à faible technicité pour les biocatalyseurs. Il intégrera un système de sélection polyvalent avec des stratégies de diversification des gènes et une installation autonome pour une croissance bactérienne continue. Cette intégration permettra d’explorer les voies évolutives pour relever les défis des réactions de formation des liaisons C-C, C-halogène et C-N, en concevant des enzymes tout en cartographiant leurs paysages de capacités physiques pour faciliter la conception à la demande de biocatalyseurs de la prochaine génération.

Objectif

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