Simultaneous Use of in Silico Design and a Correlated Mutation Network as a Tool To Efficiently Guide Enzyme Engineering

In order to improve the efficiency of directed evolution experiments, in silico multiple-substrate clustering was combined with an analysis of the variability of natural enzymes within a protein superfamily. This was applied to a Pseudomonas fluorescens esterase (PFE I) targeting the enantioselective hydrolysis of 3-phenylbutyric acid esters. Data reported in the literature for nine substrates were used for the clustering meta-analysis of the docking conformations in wild-type PFE I, and this highlighted a tryptophan residue (W28) as an interesting target. Exploration of the most frequently, naturally occurring amino acids at this position suggested that the reduced flexibility observed in the case of the W28F variant leads to enhancement of the enantioselectivity. This mutant was subsequently combined with mutations identified in a library based on analysis of a correlated mutation network. By interrogation of <80 variants a mutant with 15-fold improved enantioselectivity was found.