Dataset Information

Efficient Reassignment of a Frequent Serine Codon in Wild-Type Escherichia coli.

ABSTRACT: Expansion of the genetic code through engineering the translation machinery has greatly increased the chemical repertoire of the proteome. This has been accomplished mainly by read-through of UAG or UGA stop codons by the noncanonical aminoacyl-tRNA of choice. While stop codon read-through involves competition with the translation release factors, sense codon reassignment entails competition with a large pool of endogenous tRNAs. We used an engineered pyrrolysyl-tRNA synthetase to incorporate 3-iodo-l-phenylalanine (3-I-Phe) at a number of different serine and leucine codons in wild-type Escherichia coli. Quantitative LC-MS/MS measurements of amino acid incorporation yields carried out in a selected reaction monitoring experiment revealed that the 3-I-Phe abundance at the Ser208AGU codon in superfolder GFP was 65 ± 17%. This method also allowed quantification of other amino acids (serine, 33 ± 17%; phenylalanine, 1 ± 1%; threonine, 1 ± 1%) that compete with 3-I-Phe at both the aminoacylation and decoding steps of translation for incorporation at the same codon position. Reassignments of different serine (AGU, AGC, UCG) and leucine (CUG) codons with the matching tRNA(Pyl) anticodon variants were met with varying success, and our findings provide a guideline for the choice of sense codons to be reassigned. Our results indicate that the 3-iodo-l-phenylalanyl-tRNA synthetase (IFRS)/tRNA(Pyl) pair can efficiently outcompete the cellular machinery to reassign select sense codons in wild-type E. coli.

SUBMITTER: Ho JM

PROVIDER: S-EPMC4807657 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Publications

Efficient Reassignment of a Frequent Serine Codon in Wild-Type Escherichia coli.

Ho Joanne M JM Reynolds Noah M NM Rivera Keith K Connolly Morgan M Guo Li-Tao LT Ling Jiqiang J Pappin Darryl J DJ Church George M GM Söll Dieter D

ACS synthetic biology 20151120 2

Expansion of the genetic code through engineering the translation machinery has greatly increased the chemical repertoire of the proteome. This has been accomplished mainly by read-through of UAG or UGA stop codons by the noncanonical aminoacyl-tRNA of choice. While stop codon read-through involves competition with the translation release factors, sense codon reassignment entails competition with a large pool of endogenous tRNAs. We used an engineered pyrrolysyl-tRNA synthetase to incorporate 3- ...[more]

PMID: 26544153

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Project description:It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes.IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today's whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli Being equally computationally lightweight as classical stoichiometric models and allowing for the application of established in silico tools, the PAM and related simulation approaches will foster the use of a model-driven metabolic research. Applications range from the investigation of mechanisms of microbial evolution to the determination of optimal strain design strategies in metabolic engineering, thus supporting basic scientists and engineers alike.

Dataset Information

Efficient Reassignment of a Frequent Serine Codon in Wild-Type Escherichia coli.

Publications

Efficient Reassignment of a Frequent Serine Codon in Wild-Type Escherichia coli.

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