Final Report Summary - DIREVENZYME (Computational Exploration of Directed Evolution rules for tuning enzymatic activities)
MD simulations and QM calculations have been carried out to investigate the reversal of enantioselectivity achieved either by single active site mutations or remote aminoacid substitutions in an alcohol dehydrogenase (ADH) and D-sialic acid aldolase to L-KDO aldolase enzymes. Additionally, the molecular basis for converting natural enzymes into other biocatalysts has been elucidated for the case of esterases and epoxide hydrolases. The simulations indicate that just by mutating the obvious catalytic residues is not enough for conferring the enzyme new catalytic activity, as neither the catalytic triad, nor the binding residues required for stabilizing the transition states of the reaction are well positioned in catalytically competent arrangements. The application of long timescale MD simulations to epoxide hydrolases has also revealed a key conformational state, not previously observed by means of X-ray crystallography and short MD simulations, that presents the loop containing one of the catalytic residues in a wide-open conformation, which is likely involved in the binding of the epoxide substrate. Thus, the identification of such conformational state is key for the engineering of the enzyme active site and conformational dynamics to alter its substrate scope and allow the acceptance of bulkier pharmacologically-relevant targets.
The computational study of the laboratory-engineered enzyme variants has elucidated the strengths and weaknesses of existing computational protocols. Based on the results obtained in this project, the conformational dynamics of the enzyme is a key feature that needs to be carefully analyzed for computationally predicting mutations to engineer the enzyme activity, selectivity, and substrate scope. 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.