The combination of computational design and laboratory evolution is a powerful and potentially versatile strategy for the development of enzymes with new functions1,2,3,4. However, the limited functionality presented by the genetic code restricts the range of catalytic mechanisms that are accessible in designed active sites. Inspired by mechanistic strategies from small-molecule organocatalysis5, here we report the generation of a hydrolytic enzyme that uses Nδ-methylhistidine as a non-canonical catalytic nucleophile. Histidine methylation is essential for catalytic function because it prevents the formation of unreactive acyl-enzyme intermediates, which has been a long-standing challenge when using canonical nucleophiles in enzyme design6,7,8,9,10. Enzyme performance was optimized using directed evolution protocols adapted to an expanded genetic code, affording a biocatalyst capable of accelerating ester hydrolysis with greater than 9,000-fold increased efficiency over free Nδ-methylhistidine in solution. Crystallographic snapshots along the evolutionary trajectory highlight the catalytic devices that are responsible for this increase in efficiency. Nδ-methylhistidine can be considered to be a genetically encodable surrogate of the widely employed nucleophilic catalyst dimethylaminopyridine11, and its use will create opportunities to design and engineer enzymes for a wealth of valuable chemical transformations.
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Coordinates and structure factors have been deposited in the Protein Data Bank under accession numbers 6Q7N, 6Q7O, 6Q7P, 6Q7Q and 6Q7R. The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Source data are available from the corresponding author upon reasonable request.
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We acknowledge the Biotechnology and Biological Sciences Research Council (David Phillips Fellowship BB/M027023/1 to A.P.G) and the European Research Council (ERC Starter Grant, no. 757991 to A.P.G). We thank the Biotechnology and Biological Sciences Research Council and the Faculty of Science and Engineering (University of Manchester) for the award of an UKRI Innovation fellowship to S.L.L. We are grateful to Diamond Light Source for time on beamlines i03, i04 and i04-1 under proposals MX12788-50, MX17773-25, MX17773-34 and MX17773-52, and to Manchester SYNBIOCHEM Centre (BB/M017702/1) and the Future Biomanufacturing Hub (EP/S01778X/1) for access to their facilities and for guidance on automating directed-evolution workflows. We thank R. Spiess (Manchester Institute of Biotechnology) for acquiring protein mass spectra and R. Smithson for assistance with the chemical synthesis of activated esters. The pET29_BH32 plasmid was a gift from D. Baker.
Nature thanks Uwe Bornscheuer, Adam Nelson and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Michaelis–Menten plot showing the rate of His23 acylation (the ‘burst phase’) with varying concentrations of fluorescein 2-phenylacetate. Averaged initial rates were fitted to the Michaelis–Menten equation to derive rate constant kobs 1.3 ± 0.03 min−1 and the enzyme–substrate dissocation constant KS 30.9 ± 1.8 µM (R2 = 0.99). Data are mean ± s.d. of measurements made in triplicate.
Structures showing the amino acid positions randomized during each round of evolution. The Me-His residue is shown in ball-and-stick representation and targeted residues are represented as spheres. a, The round 1 library was prepared by randomizing active-site residues in pairs Ala19/Ser22 (blue) Tyr45/Glu46 (orange) Tyr87/Trp88 (red) Met94/Ser95 (green) and Asp125/Gln128 (purple). b, The round 2 library was prepared by random mutagenesis of the entire gene. Variants with improved activity enabled the identification of ‘hot spots’, which were further interrogated using saturation mutagenesis. Residues Leu42/Glu46 (red) and Phe132/Leu133 (blue) were randomized simultaneously; all other positions were randomized individually (green). c, The round 3 library was prepared by saturation mutagenesis, using NNK degenerate codons to individually randomize 21 positions (see table). d, The round 4 library was prepared by saturation mutagenesis, using NNK degenerate codons to individually randomize 20 positions (see table). Libraries generated during rounds 1–3 were screened for activity towards fluorescein 2-phenylacetate. The round 4 library was screened for activity towards fluorescein (R)-2-phenylpropanoate.
a–f, Michaelis–Menten plots of fluorescein 2-phenylacetate hydrolysis catalysed by OE1 (a), OE1.1 (b), OE1.2 (c), OE1.3 (d), OE1.3(N46E) (e) and OE1.3(Y45F) (f). g, A Hill plot of fluorescein 2-phenylacetate hydrolysis catalysed by CUT190, n = 2.3 ± 0.4. Data are mean ± s.d. of measurements made in triplicate. h, A table summarizing the kinetic parameters of OE1 and its variants, and the cutinase variant Ser226Pro/Arg228Ser (CUT190).
Extended Data Fig. 4 Kinetic characterization of OE1.3 and OE1.4 towards the hydrolysis of (R)- and (S)- fluorescein 2-phenylpropanoate.
a, Michaelis–Menten plots of fluorescein (R)-2-phenylpropanoate and fluorescein (S)-2-phenylpropanoate hydrolysis (shown in black and red, respectively), catalysed by OE1.3. The averaged initial rates were fitted to the Michaelis–Menten equation using Origin software. b, Michaelis–Menten plots of fluorescein (R)-2-phenylpropanoate and fluorescein (S)-2-phenylpropanoate hydrolysis (shown in black and red, respectively) catalysed by OE1.4. The averaged initial rates were fitted to the equation for Michaelis–Menten with substrate inhibition using Origin software. Data are mean ± s.d. of measurements made in triplicate. c, Table summarizing the kinetic parameters for the hydrolysis of both enantiomers of fluorescein 2-phenylpropanoate catalysed by OE1.3 and OE1.4. Data are mean ± s.d. of measurements made in triplicate.
Extended Data Fig. 5 Rate constants of the hydrolysis of fluorescein 2-phenylacetate catalysed by small-molecule nucleophilic catalysts.
a–c, Linear plots showing the rate of hydrolysis of fluorescein 2-phenylacetate catalysed by 3-methylhistidine (Me-His) kMe-His = 0.35 M−1 s−1, R2 = 0.99) (a), dimethylaminopyridine (kDMAP = 1.13 M−1 s−1, R2 = 0.99) (b) and N-methylimidazole (kNMI = 1.16 M−1 s−1, R2 = 0.99) (c). d, Linear plot showing the rate of uncatalysed fluorescein 2-phenylacetate hydrolysis (kobs = 5.9 × 10−4 min−1, R2 = 0.98). Data are mean ± s.d. of measurements made in triplicate.
Extended Data Fig. 6 Comparison of the ester hydrolysis reaction catalysed by OE1.3 and OE1.3(C186A/C212A).
Time course of the hydrolysis of fluorescein 2-phenylacetate (100 µM) catalysed by OE1.3 (purple) and OE1.3(C186A/C212A) (black) (0.1 µM) in PBS pH 7.4, 22 °C, showing that the Cys186Ala and Cys212Ala mutations have a negligible effect on catalytic activity.
A ball-and-stick representation of H23 from BH32 inhibited with 2-bromoacetophenone, coloured by atom type with H23 carbon atoms in yellow and acetophenone carbons in white. Clear FEM electron density (blue, contoured at 1σ) extends between the Nε of H23 and acetophenone.
A global superposition of BH32-inhibited (purple), OE1.2-inhibited (blue) and OE1.3-inhibited (green) structures performed using ICM Pro. The r.m.s.d. values (backbone atoms), derived from the global superposition are calculated separately for the core and cap domains and are reported in the table. BH32-inhibited and OE1.2-inhibited structures retain similar domain orientations, whereas OE1.3 undergoes a reorientation of the cap domain upon inhibition with 2-bromoacetophenone.
This file contains the DNA and protein sequence of the most active variant OE1.3, 1H and 13C NMR spectra and X-ray crystallography tables
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