Biocatalysis is based on the application of natural catalysts for new purposes, for which the enzymes were not designed. Although the first examples of biocatalysis were reported more than a century ago, biocatalysis was revolutionized after the discovery of an in vitro version of Darwinian evolution called Directed Evolution (DE). Despite the recent advances in the field, major challenges remain to be addressed. Up to date, the best experimental approach consists of creating multiple mutations simultaneously but limit the choices using statistical methods. Still, tens of thousands of variants need to be tested experimentally. In addition to that, little information is available as to how these mutations lead to enhanced enzyme proficiency. Significant advances in computational tools have enabled the de novo design of enzymes catalyzing unnatural reactions making use of the so-called inside-out computational protocol developed by the groups of Prof. Baker and Prof. Houk. Despite the initial computational successes, the most active computationally designed enzymes still perform quite poorly in comparison with the natural and DE-engineered enzymes. This project aims to computationally unveil the molecular basis of improved catalysis achieved by Directed Evolution. In particular, quantum mechanics, Molecular Dynamics simulations, and QM/MM strategies will be used to study some selected DE-engineered enzymes. The strengths and weaknesses of the current version of the computational protocol will be explored, and a more efficient approach will be proposed. The development of more robust computational methods to predict amino-acid changes needed for activity is of the utmost importance as the need for experimentally probing randomized sequences would be greatly reduced, rendering the route to novel biocatalysts much more efficient. This might represent a cheap and environmentally friendly alternative for industries to produce active catalysts for any desired target.
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