Computational identification of amino-acid mutations that further improve the activity of a chalcone–flavonone isomerase from Glycine max

Abstract

Protein design for improving enzymatic activity remains a challenge in biochemistry, especially to identify target amino-acid sites for mutagenesis and to design beneficial mutations for those sites. Here, we employ a computational approach that combines multiple sequence alignment, positive selection detection, and molecular docking to identify and design beneficial amino-acid mutations that further improve the intramolecular-cyclization activity of a chalcone–flavonone isomerase from Glycine max (GmCHI). By this approach, two GmCHI mutants with higher activities were predicted and verified. The results demonstrate that this approach could determine the beneficial amino-acid mutations for improving the enzymatic activity, and may find more applications in engineering of enzymes.