The ERC Consolidator “ProFF” targets is the development of a new class of directed evolution strategies, in order to facilitate the process of finding a new catalyst for a given reaction.
Designing a catalyst is extremely difficult. The best examples we know come from nature: enzymes are incredible catalysts, providing tremendous rate accelerations along with exquisite molecular control. Enzyme-like man-made catalysts made on purpose to accelerate a given reaction could solve many problems in molecular biology, biocatalysis and green chemistry, as well as finding some therapeutic applications. However, we do not have general rules that, given a desired catalytic function, could give us the appropriate protein sequence.
Directed evolution is a set of techniques that mimicks natural evolution in the lab and are used to obtain new molecules with improved or unnatural properties, including new enzymes. Directed evolution has been refined over years and recent advances, using microfluidic tools, allow to screen millions of variants per day, in search for the best performing ones. While efficient to generate tailored enzymes, this approach is still very laborious because it requires that each vriant is sequentially observed and manipulated. We want to use molecular programming concepts to make it faster, more autonomous and more efficient. Our approach is to pre-program a chemical system so as to bias the process of molecular evolution toward the desired target activity. Once designed, the DNA-based molecular program, which includes a sensing layer, an amplification/processing layer and a actuation layer, can be dispatched into billions of tiny compartments, where each candidate enzymatic mutant is then automatically evaluated. Directed evolution then becomes autonomous (without screening) and very large libraries can be manipulated. In addition our approach can be performed entirely in vitro, thus avoiding the bias and bottlenecks associated with in vivo (e.g. bacterial) approaches.
To achieve this result, we had to put together a number of experimental building blocks: generation of libraries of modified genes, expression of this genes into compartments using different display strategies, and molecular programs able to link various enzymatic activities to the amplification of the encoding genes. We have also looked at selection and evolution process in a more theoretical way.
In conclusion, we were able to demonstrate that molecular programming techniques are useful in the context of enzyme optimization. We have demonstrated a umber of proof of principle directed evolution platforms, that can apply to enzymes with different activities, including DNA-processing enzymes and metabolic enzymes. We have studied the dynamics associated with such synthetic self-selection process and also shown that the combination of these autonomous selection operators and next generation sequencing can be used to map, at very high throughput, the fitness landscape of an enzymes
