Computational design of industrial enzymes for green chemistry

Catalysts are able to reduce activation barriers of reactions making them possible at lower pressure an temperatures. Enzymes are the most efficient, specific, and selective catalysts known. Green chemistry has emerged as a new area focusing on use of environmentally friendly, non-hazardous and efficient solvents and catalysts in the synthesis of new products. Enzymes are non-toxic, and capable of operating under mild biological conditions, which makes them green catalysts offering an attractive alternative to traditional catalysis. However, their application in industry is rather limited as most industrial processes lack a natural enzyme. The solution is the routine design of enzymes, but this task has not yet been achieved due to several limitations, such as the high complexity of enzyme catalysis, the lack of accurate computational approaches for designing and estimating the catalytic potential of the new variants, and the inability to identify potential mutation sites far away from the active site of the enzyme. GREENZYME provides a new protocol able to capture this high complexity and design new enzymes capable of predicting active site and distal mutations, thus achieving high levels of activity (as it would occur in nature). This is achieved by integrating current Shortest Path Map-Ancestral Sequence Reconstruction (SPM-ASR)-based computational protocol developed in previous projects such as the ERC-StG NetMoDEzyme with deep learning techniques. Thanks to a well-thought-out exploitation and communication strategy, will make possible the premise of routine enzyme design. This will have a large-scale socio-economic impact, as it will reduce the production costs of many drugs and will allow industries to use environmentally friendly alternatives in line with new European policies.