Structure, mechanism, and enantioselectivity shifting of lipase LipK107 with a simple way

Because of the complex mechanisms of enzymatic reactions, no precise and simple method of understanding and controlling the chiral selectivity of enzymes has been developed. However, structure-based rational design is a powerful approach to engineering enzymes with desired catalytic activities. In this work, a simple, structure-based, large-scale in silico design and virtual screening strategy was developed and successfully applied to enzyme engineering. We first performed protein crystallization and X-ray diffraction to determine the structure of lipase LipK107, which is a novel family I.1 lipase displaying activity for both R and S isomers in chiral resolution reactions. The catalytic mechanism of family I.1, which includes LipK107, was ascertained first through comparisons of the sequences and structures of lipases from other families. The binding states of LipK107, including the energy and the conformation of complexes with the R and S enantiomers, have been evaluated by careful biocomputation to figure out the reason for the higher S selectivity. Based on this study, a simple strategy for manipulating the chiral selectivity by modulating a crucial distance in the enzyme–substrate complex and judging virtual mutations in silico is recommended. Then, a novel electrostatic interaction analysis protocol was used to design LipK107 mutants to validate our strategy. Both positive and negative mutations determined using this theoretical protocol have been implemented in wet experiments and were proved to produce the desired enantioselectivity, showing a 176% increase or 50% decrease in enantioselectivity as desired. Because of its accuracy and versatility, the strategy is promising for practical applications. •The structure and catalytic mechanism of a novel family I.1 lipase was determined.•A simple strategy for manipulating the chiral selectivity was improved.•A crucial distance in enzyme-substrate complex to judge virtual mutations was identified.•A novel electrostatic interaction analysis protocol was used to design mutants.•All the computational designs matched perfectly with the experimental results.