Descripción del proyecto
La próxima generación de enzimas artificiales
Las enzimas naturales, potentes catalizadores de gran eficacia y selectividad, ofrecen numerosas aplicaciones para los procesos industriales. Para adaptar las propiedades de las enzimas a las distintas necesidades, los investigadores emplean un método conocido como evolución dirigida, que imita la evolución natural mediante la generación de diversas variantes enzimáticas. A continuación, se seleccionan aquellas enzimas que presentan los rasgos deseados. El equipo del proyecto ProFF, financiado por el Consejo Europeo de Investigación, pretende superar los retos de la evolución dirigida introduciendo ordenadores bioquímicos capaces de integrar la información genética pertinente. Se espera que estos ordenadores doten a la evolución dirigida de una mayor versatilidad, rapidez y capacidad para resolver problemas complejos. En última instancia, el mencionado proyecto de investigación pretende desarrollar las herramientas moleculares necesarias para la selección programable de la próxima generación de herramientas catalíticas empleadas en entornos no naturales.
Objetivo
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.
Ámbito científico
- social sciencespolitical sciencespolitical transitionsrevolutions
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural scienceschemical sciencescatalysisbiocatalysis
- natural sciencesbiological sciencesmolecular biologymolecular evolution
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
75794 Paris
Francia