Substrate structure and computation guided engineering of a lipase for omega-3 fatty acid selectivity.

Enrichment of omega-3 fatty acids (ɷ-3 FAs) in natural oils is important to realize their health benefits. Lipases are promising catalysts to perform this enrichment, however, fatty acid specificity of lipases is poor. We attempted to improve the fatty acid selectivity of a lipase from Geobacillus thermoleovorans (GTL) by two approaches. In a semi-rational approach, amino acid positions critical for binding were identified by docking the substrate to the GTL and best substitutes at these positions were identified by site saturation mutagenesis followed by screening to obtain a variant of GTL (CM-GTL). In the second approach based on rational design, a variant of GTL was designed (DM-GTL) wherein the active site was narrowed by incorporating two heavier amino acids in the lining of acyl-binding pocket to hinder access to bulky ɷ-3 FAs. The affinities DM-GTL with designed substrates were evaluated in silico. Both, CM-GTL and DM-GTL have shown excellent ability to discriminate against the ɷ-3 FAs during hydrolysis of oils. Engineering the binding pocket of an enzyme of a complex substrate, such as a triglyceride, by incorporating the information on substrate structure and computationally derived binding modes, has resulted in designing two efficient lipase variants with improved substrate selectivity.