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Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore.

ABSTRACT: Efforts to sequence single protein molecules in nanopores^1-5 have been hampered by the lack of techniques with sufficient sensitivity to discern the subtle molecular differences among all twenty amino acids. Here we report ionic current detection of all twenty proteinogenic amino acids in an aerolysin nanopore with the help of a short polycationic carrier. Application of molecular dynamics simulations revealed that the aerolysin nanopore has a built-in single-molecule trap that fully confines a polycationic carrier-bound amino acid inside the sensing region of the aerolysin. This structural feature means that each amino acid spends sufficient time in the pore for sensitive measurement of the excluded volume of the amino acid. We show that distinct current blockades in wild-type aerolysin can be used to identify 13 of the 20 natural amino acids. Furthermore, we show that chemical modifications, instrumentation advances and nanopore engineering offer a route toward identification of the remaining seven amino acids. These findings may pave the way to nanopore protein sequencing.

SUBMITTER: Ouldali H

PROVIDER: S-EPMC7008938 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore.

Ouldali Hadjer H Sarthak Kumar K Ensslen Tobias T Piguet Fabien F Manivet Philippe P Pelta Juan J Behrends Jan C JC Aksimentiev Aleksei A Oukhaled Abdelghani A

Nature biotechnology 20191216 2

Efforts to sequence single protein molecules in nanopores<sup>1-5</sup> have been hampered by the lack of techniques with sufficient sensitivity to discern the subtle molecular differences among all twenty amino acids. Here we report ionic current detection of all twenty proteinogenic amino acids in an aerolysin nanopore with the help of a short polycationic carrier. Application of molecular dynamics simulations revealed that the aerolysin nanopore has a built-in single-molecule trap that fully ...[more]

PMID: 31844293

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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

Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore.

Publications

Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore.

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