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Biosynthesis and genetic encoding of phosphothreonine through parallel selection and deep sequencing


The phosphorylation of threonine residues in proteins regulates diverse processes in eukaryotic cells, and thousands of threonine phosphorylations have been identified. An understanding of how threonine phosphorylation regulates biological function will be accelerated by general methods to biosynthesize defined phosphoproteins. Here we describe a rapid approach for directly discovering aminoacyl-tRNA synthetase–tRNA pairs that selectively incorporate non-natural amino acids into proteins; our method uses parallel positive selections combined with deep sequencing and statistical analysis and enables the direct, scalable discovery of aminoacyl-tRNA synthetase–tRNA pairs with mutually orthogonal substrate specificity. By combining a method to biosynthesize phosphothreonine in cells with this selection approach, we discover a phosphothreonyl-tRNA synthetase–tRNACUA pair and create an entirely biosynthetic route to incorporating phosphothreonine in proteins. We biosynthesize several phosphoproteins and demonstrate phosphoprotein structure determination and synthetic protein kinase activation.

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Figure 1: Expression of S. enterica PduX in E. coli enables the intracellular biosynthesis of phosphothreonine at millimolar concentrations.
Figure 2: tRNAv1.0CUA evolution removes aminoacylation of tRNAv1.0CUA by natural aminoacyl-tRNA synthetases in E.coli.
Figure 3: Parallel positive selections, with and without non-natural amino acid, coupled to deep sequencing and analysis rapidly identify efficient and specific aminoacyl-tRNA synthetases for genetic code expansion.
Figure 4: Scalable parallel positive selections, deep sequencing, and analysis directly identifies aminoacyl-tRNA synthetases with mutually orthogonal non-natural substrate specificity.
Figure 5: Discovery and characterization of a pThrRS–tRNAv2.0CUA pair for the biosynthesis and genetic encoding of pThr in recombinant proteins.
Figure 6: Biosynthesis of proteins with genetically encoded pThr enables structural and biochemical characterization of biologically relevant phosphoproteins (Ub (pThr12), Ub (pThr66), Cdk2 (pThr160)).

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This work was supported by the Medical Research Council, UK (MC_U105181009 and MC_UP_A024_1008), BBSRC (BB/M000842/1, for automation) and an ERC Advanced Grant (SGCR, 669351), all to J.W.C. M.S.Z. was supported by an EMBO Fellowship (ALTF 297-2015), S.F.B. was supported by a Boehringer Fonds PhD fellowship, and A.D.L. was supported by an NSF fellowship (1523390). We are grateful to D. Cervettini, M. Mahesh, and Y.-H. Tsai for contributions; and to T. Elliott (MRC-LMB) for compound 4. We are grateful to the MRC-LMB mass spectrometry facility for performing ESI-MS/MS. We are grateful to M. Yu, P. Emsley, G. Murshudov, O. Perisic, and R. Williams for help in crystallization and structural data analysis.

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Authors and Affiliations



J.W.C. defined the direction of research. D.T.R. suggested using PduX. M.S.Z. demonstrated pThr biosynthesis in E. coli and optimized the tRNA with the help of W.H.S. A.D.L. developed the assay for determining intracellular amino acid concentration. S.F.B. developed the parallel selection and sequencing approach. M.S.Z. and S.F.B. applied the approach to pThrRS discovery. M.S.Z. performed protein expression and biochemical experiments. N.H.-D. carried out protein expression, purification and structural studies with assistance from M.S.Z. M.S.Z., S.F.B., and J.W.C. wrote the paper with input from all authors.

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Correspondence to Jason W Chin.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–21 and Supplementary Tables 1–4. (PDF 8259 kb)

Supplementary Data Set 1

Sequences of all plasmids used in this study. Genbank file with all plasmid sequences. (TXT 146 kb)

Supplementary Protocol

Parallel positive selections for the discovery of selective aminoacyl-tRNA synthetase. (PDF 302 kb)

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Zhang, M., Brunner, S., Huguenin-Dezot, N. et al. Biosynthesis and genetic encoding of phosphothreonine through parallel selection and deep sequencing. Nat Methods 14, 729–736 (2017).

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