Description du projet
La nouvelle génération d’enzymes artificielles
Les enzymes naturelles, qui sont de puissants catalyseurs dotés d’une efficacité et d’une sélectivité élevées, trouvent de nombreuses applications dans les processus industriels. Pour adapter les propriétés des enzymes en fonction des besoins, les chercheurs utilisent une méthode connue sous le nom d’évolution dirigée, qui imite l’évolution naturelle en générant diverses variantes d’enzymes. Celles qui présentent les caractéristiques souhaitées sont ensuite sélectionnées. Financé par le Conseil européen de la recherche, le projet ProFF vise à relever les défis de l’évolution dirigée en introduisant des ordinateurs biochimiques qui intègrent des informations génétiques pertinentes. Ces ordinateurs devraient renforcer la polyvalence, la rapidité et la capacité de l’évolution dirigée à résoudre des problèmes complexes. En fin de compte, cette recherche vise à développer les outils moléculaires nécessaires à la sélection programmable de la nouvelles génération d’outils catalytiques fonctionnant dans des environnements non naturels.
Objectif
Natural enzymes are awesome catalysts, in terms of their catalytic efficiency, selectivity, control mechanisms, etc. Revamped as laboratory or industrial tools, they have allowed more than a few breakthroughs, such as PCR, next generation sequencing or green chemistry. The next revolution will be brought by a new generation of extensively modified “enzymatic” catalysts working in non-natural environments, possibly build from non-natural chemistries and targeting an unlimited range of non-natural functions. However, their design is still an arduous process; computational design lacks precision while the combinatorial approach, directed evolution, is limited by labor-intensive or ad hoc selection stages.
We will remove the selection bottleneck in directed evolution by introducing biochemical computers able to perform this step autonomously. Based on recent developments in DNA-based molecular programming, these molecular scouts will be co-compartmentalized with genetic libraries into billions of individual compartments in micrometric emulsions. At each generation and in each droplet, after expression of the genotype, these molecular programs will autonomously: i- evaluate the phenotypic signature of a candidate, ii- integrate this information into a predefined scoring function and iii- propagate the relevant genetic information according to this score.
The programmability of this approach will make directed evolution versatile, faster, and able to address more challenging problems. The evolution dynamics itself become tunable, offering new perspectives on the fitness landscape of biopolymer catalysts. A quantitative in silico model will be built and integrated in a computer-assisted tool for the fast set-up of in vitro experiments and tuning of the various experimental knobs. Overall, we will close a virtuous circle by evolving the molecular tools enabling the programmable selection of the next generation of catalytic tools.
Champ scientifique
- social sciencespolitical sciencespolitical transitionsrevolutions
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural scienceschemical sciencescatalysisbiocatalysis
- natural sciencesbiological sciencesmolecular biologymolecular evolution
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
Programme(s)
Régime de financement
ERC-COG - Consolidator GrantInstitution d’accueil
75794 Paris
France