Statistical Coupling Analysis-Guided Library Design for the Discovery of Mutant Luciferases

Directed
evolution has proven to be an invaluable tool for protein
engineering; however, there is still a need for developing new approaches
to continue to improve the efficiency and efficacy of these methods.
Here, we demonstrate a new method for library design that applies
a previously developed bioinformatic method, Statistical Coupling
Analysis (SCA). SCA uses homologous enzymes to identify amino acid
positions that are mutable and functionally important and engage in
synergistic interactions between amino acids. We use SCA to guide
a library of the protein luciferase and demonstrate that, in a single
round of selection, we can identify luciferase mutants with several
valuable properties. Specifically, we identify luciferase mutants
that possess both red-shifted emission spectra and improved stability
relative to those of the wild-type enzyme. We also identify luciferase
mutants that possess a >50-fold change in specificity for modified
luciferins. To understand the mutational origin of these improved
mutants, we demonstrate the role of mutations at N229, S239, and G246
in altered function. These studies show that SCA can be used to guide
library design and rapidly identify synergistic amino acid mutations
from a small library.