Improving biocatalyst performance by integrating statistical methods into protein engineering

Abstract

Directed evolution and rational design were used to generate active variants of toluene-4-monooxygenase (T4MO) on 2-phenylethanol (PEA), with the aim of producing hydroxytyrosol, a potent antioxidant. Due to the complexity of the enzymatic system-four proteins encoded by six genes-mutagenesis is labor-intensive and time-consuming. Therefore, the statistical model of Nov and Wein (J. Comput. Biol. 12:247-282) was used to reduce the number of variants produced and evaluated in a lab. From an initial data set of 24 variants, with mutations at nine positions, seven double or triple mutants were identified through statistical analysis. The average activity of these mutants was 4.6-fold higher than the average activity of the initial data set. In an attempt to further improve the enzyme activity to obtain PEA hydroxylation, a second round of statistical analysis was performed. Nine variants were considered, with 3, 4, and 5 point mutations. The average activity of the variants obtained in the second statistical round was 1.6-fold higher than in the first round and 7.3-fold higher than that of the initial data set. The best variant discovered, TmoA I100A E214G D285Q, exhibited an initial oxidation rate of 4.4 ± 0.3 nmol/min/mg protein, which is 190-fold higher than the rate obtained by the wild type. This rate was also 2.6-fold higher than the activity of the wild type on the natural substrate toluene. By considering only 16 preselected mutants (out of ∼13,000 possible combinations), a highly active variant was discovered with minimum time and effort.

FIG. 1.

Flowchart describing the design strategy. The first phase of the experiment consists of traditional protocols—directed evolution, rational design, and saturation mutagenesis—to produce initial genetic diversity. The resulting sequence-activity data are the basis for the second phase, in which several rounds of statistical analysis (two rounds in our study but possibly more, in general) point to a few promising variants with high expected activities; these variants are produced via site-directed mutagenesis and tested for their activity, and the resulting data serve as additional input for the next round.

FIG. 2.

Evolving T4MO for PEA hydroxylation by the aid of a statistical model. Relative activities on PEA (indicated above each column) of three evolving generations: (i) 10 representative variants out of the 24 T4MO variants comprising the initial data set used for the statistical model (filled bars), (ii) the first-generation mutants obtained by the statistical model (hatched bars); and (iii) the second-generation mutants obtained in the second statistical round (gray bars). Relative activity is presented as the initial PEA oxidation rate (determined via HPLC analysis) normalized to that of WT T4MO. The value for the WT, which had an activity of 0.023 nmol/min/mg protein on an initial PEA concentration of 0.25 mM, is designated 1.