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Aerolysin nanopore-based identification of proteinogenic amino acids using a bipolar peptide probe.

ABSTRACT: Nanopore technology has attracted extensive attention due to its rapid, highly sensitive, and label-free performance. In this study, we aimed to identify proteinogenic amino acids using a wild-type aerolysin nanopore. Specifically, bipolar peptide probes were synthesised by linking four aspartic acid residues to the N-terminal and five arginine residues to the C-terminal of individual amino acids. With the help of the bipolar peptide carrier, 9 proteinogenic amino acids were reliably recognised based on current blockade and dwell time using an aerolysin nanopore. Furthermore, by changing the charge of the peptide probe, two of the five unrecognized amino acids above mentioned were identified. These findings promoted the application of aerolysin nanopores in proteinogenic amino acid recognition.

SUBMITTER: Ge Y

PROVIDER: S-EPMC9470019 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Aerolysin nanopore-based identification of proteinogenic amino acids using a bipolar peptide probe.

Ge Yaxian Y Cui Mengjie M Zhang Qiuqi Q Wang Ying Y Xi Dongmei D

Nanoscale advances 20220811 18

Nanopore technology has attracted extensive attention due to its rapid, highly sensitive, and label-free performance. In this study, we aimed to identify proteinogenic amino acids using a wild-type aerolysin nanopore. Specifically, bipolar peptide probes were synthesised by linking four aspartic acid residues to the N-terminal and five arginine residues to the C-terminal of individual amino acids. With the help of the bipolar peptide carrier, 9 proteinogenic amino acids were reliably recognised ...[more]

PMID: 36133334

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Project description:Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.

Dataset Information

Aerolysin nanopore-based identification of proteinogenic amino acids using a bipolar peptide probe.

Publications

Aerolysin nanopore-based identification of proteinogenic amino acids using a bipolar peptide probe.

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