Systematic engineering of artificial metalloenzymes for new-to-nature reactions

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Science Advances  22 Jan 2021:
Vol. 7, no. 4, eabe4208
DOI: 10.1126/sciadv.abe4208
  • Fig. 1 Systematic screening of ArMs.

    (A) Sav is secreted to the periplasm, where it binds an externally added biotinylated cofactor, which consists of a catalytic metal M and ligands L and L′, to afford an ArM. OmpA, periplasmic export signal from outer membrane protein A. (B) Left: Biotinylated metathesis cofactor embedded in the biotin-binding vestibule of homotetrameric Sav (Protein Data Bank 5IRA). Symmetry-related residues S112/S′112 (blue) and K121/K′121 (purple) are in the immediate vicinity of the cofactor and were mutated (right) to afford an Sav library of 400 amino acid combinations. (C) Expression level of periplasmic Sav mutants as determined in cell lysate using a fluorescence quenching assay (Methods) (41). Means of biological triplicates (n = 3) normalized to the OD600 of the cultures are displayed (see fig. S4 for SD). (D) Estimated percentage of Sav mutants with unoccupied biotin-binding sites as a function of the cofactor concentration added to the cell suspension (assuming 50% uptake into the periplasm). Ten micromolars was selected as maximum-permitted cofactor concentration for further experiments to ensure an excess of binding sites for >90% of Sav variants (dashed lines). (E) Overview of the screening workflow (see Results and Methods). Circular arrows represent centrifugation steps for buffer exchange.

  • Fig. 2 Systematic screening for ArMs catalyzing diverse reactions.

    (A) ArM-catalyzed reactions: I, ring-closing metathesis (RCM) with a diallyl-sulfonamide 1 yielding a 2,5-dihydro-pyrrole 2. II, Deallylation of allylcarbamate-protected coumarin 3 to the corresponding amino coumarin 4. III, Deallylation of allyl carbamate indole 5 to indole 6. IV, Hydroamination of 2-ethynylaniline 7 to indole 6. V, Hydroarylation of profluorophore 8 to afford amino coumarin 9. (B) Biotinylated cofactors used in this study. Biot: d-biotin. (C) Cell-specific activity of 400 ArMs mutated at Sav positions 112 and 121 normalized to the activity of wild-type Sav (S112 K121). The displayed activities are product concentrations after 20 hours of reaction (mean of biological duplicates; for SDs, refer to fig. S8). Note that the screenings for reactions II and V were performed using robotics. (D) Activity distribution in the Sav mutant library for the five ArM reactions. Violins comprise 400 double mutants with the 10 most active ArMs depicted as circles. (E) Validation of hits from the 400 mutant screens. Bars are mean activity of eight biological replicates with SD (error bars) and individual replicates (circles). Mutants are designated by the amino acids in positions 112 and 121.

  • Fig. 3 In vitro turnover number of ArM variants identified in the periplasmic screening.

    For each reaction, the three most active variants identified in the whole-cell screening were purified, and their TON was determined. Reactions were carried out at 37°C and 200 rpm for 20 hours. Bars represent mean TONs of technical triplicate reactions with SD as error bars and individual replicates as circles. For comparison, the free cofactor and wild-type Sav variant (wt) were included. Mutants are designated by the amino acids in positions 112 and 121.

  • Fig. 4 Amino acid effects and implications for future ArM engineering.

    (A) Effect of amino acids on ArM activity. Points outside and inside the dark gray circles indicate a positive and negative effect, respectively. Values were standardized by subtracting the mean activity of all 400 mutants and dividing by the corresponding SD. The mean across all 20 variants harboring the respective amino acid is shown. (B) Hierarchical clustering of amino acids across both positions and all reactions (Methods). NDT-encoded amino acids are highlighted (blue). (C) Comparison of enzyme engineering strategies. Strategies are compared to the full-factorial approach (left to right): screening single mutants (both positions), combining the best single mutations at both positions, ISM, and combinatorial screening with reduced amino acid sets (NDT/NRT codons). Bars are the mean across hits identified by the respective strategy. (D) Predictive machine learning models based on SVM, gradient boosting, and neural networks (Methods). Models were trained on 320 randomly selected data points and evaluated on the remaining 80 variants. Mean and SD of five training runs are displayed for gradient boosting and neural networks. (E) Performance of gradient boosting model for reaction III. Predictions are plotted against experimental measurements for 80 held-out variants (predicted top 10% highlighted in blue).

Supplementary Materials

  • Supplementary Materials

    Systematic engineering of artificial metalloenzymes for new-to-nature reactions

    Tobias Vornholt, Fadri Christoffel, Michela M. Pellizzoni, Sven Panke, Thomas R. Ward, Markus Jeschek

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    • Figs. S1 to S29
    • Tables S1 to S5
    • Supplementary Methods
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