Project description
The next generation of artificial enzymes
Natural enzymes, which are powerful catalysts with high efficiency and selectivity, are found in many applications in industrial processes. To tailor enzyme properties according to need, researchers employ a method known as directed evolution, which mimics natural evolution by generating diverse enzyme variants. Those with desired traits are then selected. Funded by the European Research Council, the ProFF project aims to overcome challenges in directed evolution by introducing biochemical computers that integrate relevant genetic information. These computers are expected to make directed evolution more versatile, faster, and capable of solving complex problems. Ultimately, this research aims to evolve the molecular tools needed for programmable selection of the next generation of catalytic tools that function in non-natural environments.
Objective
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.
Fields of science
- 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)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
75794 Paris
France