Stable and Functionally Diverse Versatile Peroxidases Designed Directly from Sequences

Abstract

White-rot fungi secrete a repertoire of high-redox potential oxidoreductases to efficiently decompose lignin. Of these enzymes, versatile peroxidases (VPs) are the most promiscuous biocatalysts. VPs are attractive enzymes for research and industrial use but their recombinant production is extremely challenging. To date, only a single VP has been structurally characterized and optimized for recombinant functional expression, stability, and activity. Computational enzyme optimization methods can be applied to many enzymes in parallel but they require accurate structures. Here, we demonstrate that model structures computed by deep-learning-based ab initio structure prediction methods are reliable starting points for one-shot PROSS stability-design calculations. Four designed VPs encoding as many as 43 mutations relative to the wildtype enzymes are functionally expressed in yeast, whereas their wildtype parents are not. Three of these designs exhibit substantial and useful diversity in their reactivity profiles and tolerance to environmental conditions. The reliability of the new generation of structure predictors and design methods increases the scale and scope of computational enzyme optimization, enabling efficient discovery and exploitation of the functional diversity in natural enzyme families directly from genomic databases.

Figure 1

Key steps in the design of a diverse set of VPs. (A) VP sequences were collected from different databases, a phylogenetic tree was computed and 12 representative sequences were selected. (B) Selected sequences were modeled by trRosetta.^, For visualization, heme (red), manganese (pink), and calcium ions (blue) were superimposed from the VPL structure (PDB entry: 3FJW). The surface-reactive tryptophan is presented in purple balls-and-sticks. (C) PROSS stability-design calculations^, suggested dozens of mutations (yellow spheres). For each sequence, three designs with different mutational loads were selected for further experimental examination. (D) In an activity screen of proteins heterologously produced in yeast, the wildtype proteins show negligible functional expression, while the designs with the highest mutational load are highly active on the peroxidase substrate ABTS.

Figure 2

Functional expression levels, thermal, pH, and H₂O₂ stability in VP designs. (A) Expression levels were calculated using the initial activity against ABTS in the supernatant immediately after growth and the ABTS kinetic values (see Methods, Supporting Information). Apparent T₅₀ values were calculated based on the residual activity of enzymes incubated at different temperatures ranging from 25 to 80 °C (see Figure S4A); n.d.: not determined. pH stability was assessed by incubation at pH values ranging from 2 to 9 and measuring the residual activity at times 0, 4, 24, 50, 75, and 165 h, compared to the activity at pH = 3 at time zero (see Figure S5 for complete data). (B) Kinetic thermostability (t_1/2) profiles were determined by incubating VP supernatants at 60 °C and measuring their residual activity at times 0–120 min, compared to the initial activity. (C) H₂O₂ stability (t_1/2) profiles were determined by incubating designs in 750 μM H₂O₂ (molar ratio of 1:3000) and measuring their residual activity at times 0–48 min, compared to the initial activity. All of the results are expressed as the mean ± S.D. of three independent experiments.

Figure 3

High functional diversity among VP designs. For each VP (5H, 8H, 11H, and R4) and substrate (ABTS, DMP, RB5, VA, and Mn²⁺), two kinetic parameters, K_M and k_cat, are shown (in μM and s^–1, respectively; K_M values given in mM are marked with asterisks); bars are normalized to the larger value in each substrate (worst K_M and best k_cat). For ABTS and DMP, light bars refer to the kinetic parameters of the low-efficiency site and the dark bars to the high-efficiency site; kinetic values are normalized separately for each (Table S4 shows all kinetic parameters). The best affinity (lowest K_M) and turnover number (highest k_cat) for each substrate are highlighted in bold. pH-dependent activity profiles are shown for each enzyme–substrate pair, and the bars are normalized to the activity at optimal pH for each such pair (Figure S6 shows full activity plots).

Figure 4

Structural basis for the different activity profiles in VPs. (A) AlphaFold2 models of 5H, 8H, and 11H are superimposed onto the VPL crystallographic structure (PDB entry 3FJW). All VP backbones are presented in gray cartoons, VPL calcium and manganese ions are in teal and pink spheres, respectively, and the heme group in pink sticks in all panels. Backbone segments that are unique for 8H are colored in purple. (B) Manganese-oxidation site. VPL and 8H residues that chelate manganese and vicinal side chains are in gray and purple sticks, respectively. Significant variations are marked in arrows and their position identities and numbers are relative to the PDB entry 3FJW in all panels. (C) Reactive surface tryptophanyl site. Tryptophan is presented in salmon sticks. VPL and 8H residues in the tryptophan vicinity are presented in gray and purple sticks, respectively. (D) Access channel to the heme-oxidation site. Guaiacol (GUA), which is chemically similar to DMP, from a VPL crystallographic structure (PDB entry 4G05(32)) is in yellow sticks. VPL residues in the GUA vicinity are gray. Mutations relative to VPL are indicated in green, purple, and orange sticks for 5H, 8H, and 11H, respectively, and are marked by arrows.