Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore
- PMID: 31844293
- PMCID: PMC7008938
- DOI: 10.1038/s41587-019-0345-2
Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore
Abstract
Efforts to sequence single protein molecules in nanopores1-5 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.
Conflict of interest statement
Competing financial interest
AU include any patents pending as a result of this work.
The authors declare the following competing financial interest(s): A.O, J.P and P.M are co-founders of DreamPore S.A.S. and F.P. is the head of research development of DreamPore S.A.S. J.C.B is cofounder of Ionera Technologies GmbH, Freiburg, Germany and of Nanion Technologies GmbH, Munich, Germany.
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Comment in
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Reading amino acids in a nanopore.Nat Biotechnol. 2020 Feb;38(2):159-160. doi: 10.1038/s41587-019-0401-y. Nat Biotechnol. 2020. PMID: 31974421 No abstract available.
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Sensing proteinogenic amino acids.Nat Methods. 2020 Feb;17(2):126. doi: 10.1038/s41592-020-0741-z. Nat Methods. 2020. PMID: 32020089 No abstract available.
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