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Improved spectral resolution of [¹³C,¹H]-HSQC spectra of aromatic amino acid residues in proteins produced by cell-free synthesis from inexpensive ¹³C-labelled precursors.

ABSTRACT: Cell-free protein synthesis using eCells allows production of amino acids from inexpensive ¹³C-labelled precursors. We show that the metabolic pathway converting pyruvate, glucose and erythrose into aromatic amino acids is maintained in eCells. Judicious choice of ¹³C-labelled starting material leads to proteins, where the sidechains of aromatic amino acids display [¹³C,¹H]-HSQC cross-peaks free of one-bond ¹³C-¹³C couplings. Selective ¹³C-labelling of tyrosine and phenylalanine residues is achieved simply by using different compositions of the reaction buffers.

SUBMITTER: Van Raad D

PROVIDER: S-EPMC10406723 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Improved spectral resolution of [13C,1H]-HSQC spectra of aromatic amino acid residues in proteins produced by cell-free synthesis from inexpensive 13C-labelled precursors.

Van Raad Damian D Huber Thomas T Otting Gottfried G

Journal of biomolecular NMR 20230620 4

Cell-free protein synthesis using eCells allows production of amino acids from inexpensive 13C-labelled precursors. We show that the metabolic pathway converting pyruvate, glucose and erythrose into aromatic amino acids is maintained in eCells. Judicious choice of 13C-labelled starting material leads to proteins, where the sidechains of aromatic amino acids display [13C,1H]-HSQC cross-peaks free of one-bond 13C-13C couplings. Sele ...[more]

PMID: 37338652

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Project description:Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

Project description:BackgroundL-phenylalanine is an essential amino acid with various promising applications. The microbial pathway for L-phenylalanine synthesis from glucose in wild strains involves lengthy steps and stringent feedback regulation that limits the production yield. It is attractive to find other candidates, which could be used to establish a succinct and cost-effective pathway for L-phenylalanine production. Here, we developed an artificial bioconversion process to synthesize L-phenylalanine from inexpensive aromatic precursors (benzaldehyde or benzyl alcohol). In particular, this work opens the possibility of L-phenylalanine production from benzyl alcohol in a cofactor self-sufficient system without any addition of reductant.ResultsThe engineered L-phenylalanine biosynthesis pathway comprises two modules: in the first module, aromatic precursors and glycine were converted into phenylpyruvate, the key precursor for L-phenylalanine. The highly active enzyme combination was natural threonine aldolase LtaEP.p and threonine dehydratase A8HB.t, which could produce phenylpyruvate in a titer of 4.3 g/L. Overexpression of gene ridA could further increase phenylpyruvate production by 16.3%, reaching up to 5 g/L. The second module catalyzed phenylpyruvate to L-phenylalanine, and the conversion rate of phenylpyruvate was up to 93% by co-expressing PheDH and FDHV120S. Then, the engineered E. coli containing these two modules could produce L-phenylalanine from benzaldehyde with a conversion rate of 69%. Finally, we expanded the aromatic precursors to produce L-phenylalanine from benzyl alcohol, and firstly constructed the cofactor self-sufficient biosynthetic pathway to synthesize L-phenylalanine without any additional reductant such as formate.ConclusionSystematical bioconversion processes have been designed and constructed, which could provide a potential bio-based strategy for the production of high-value L-phenylalanine from low-cost starting materials aromatic precursors.

Dataset Information

Improved spectral resolution of [¹³C,¹H]-HSQC spectra of aromatic amino acid residues in proteins produced by cell-free synthesis from inexpensive ¹³C-labelled precursors.

Publications

Improved spectral resolution of [<sup>13</sup>C,<sup>1</sup>H]-HSQC spectra of aromatic amino acid residues in proteins produced by cell-free synthesis from inexpensive <sup>13</sup>C-labelled precursors.

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