Institute of Technical Biochemistry, University of Stuttgart,
Allmandring 31,
D-70569 Stuttgart, Germany
http://www.itb.uni-stuttgart.de
1Email: jpleiss@tebio1.biologie.uni-stuttgart.de
Most enzymes are able to differentiate between the two mirror images of a chiral substrate. To understand how this biochemical property is encoded in the protein sequence, we have modelled chiral recognition by the enzyme class of lipases. Our model predicts the enantiopreference of four different lipases towards a broad range of substrates (secondary alcohols, lactones, triacylglycerols and primary alcohols), and quantifies the effect of a mutation on enantioselectivity. The model is based on manual docking of the substrate in its tetrahedral intermediate state to the binding site of the lipase, and subsequent relaxation of the complex by molecular dynamics simulations. Complexes of the two enantiomers were geometrically analyzed. For each substrate class, a parameter (atom-atom distance or torsion angle) was identified which correlates to experimentally determined enantioselectivity.
The model was validated by site-directed mutants with changed enantioselectivity and even reversed enantiopreference. This in silico assay is general, robust and time-efficient; therefore it could applied to predict enantioselectivity of new lipases based on their structure, and to screen large libraries of random mutants in the course of an in silico directed evolution strategy.