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Dataset for Macromolecular crowding enhances the detection of DNA and proteins by a solid-state nanopore.

Citation

Chau, Chalmers C. and Radford, Sheena E. and Hewitt, Eric W. and Actis, Paolo (2020) Dataset for Macromolecular crowding enhances the detection of DNA and proteins by a solid-state nanopore. University of Leeds. [Dataset] https://doi.org/10.5518/841

Dataset description

Nanopore analysis of nucleic acid is now routine, but detection of proteins remains challenging. Here, we report the systematic characterization of the effect of macromolecular crowding on the detection sensitivity of a solid-state nanopore for circular and linearized DNA plasmids, globular proteins (beta-galactosidase), and filamentous proteins (alpha-synuclein amyloid fibrils). We observe a remarkable ca. 1000-fold increase in the molecule count for the globular protein beta-galactosidase and a 6-fold increase in peak amplitude for plasmid DNA under crowded conditions. We also demonstrate that macromolecular crowding facilitates the study of the topology of DNA plasmids and the characterization of amyloid fibril preparations with different length distributions. A remarkable feature of this method is its ease of use; it simply requires the addition of a macromolecular crowding agent to the electrolyte. We therefore envision that macromolecular crowding can be applied to many applications in the analysis of biomolecules by solid-state nanopores.

Keywords: Nanopore, nanopipette, macromolecular crowding, single-molecule, DNA, protein, amyloid fibril.
Subjects: H000 - Engineering > H600 - Electronic & electrical engineering
Divisions: Faculty of Biological Sciences > Astbury Centre for Structural Molecular Biology
Faculty of Biological Sciences > School of Molecular and Cellular Biology
Faculty of Engineering and Physical Sciences > School of Electronic and Electrical Engineering
Related resources:
LocationType
https://doi.org/10.1021/acs.nanolett.0c02246Publication
https://eprints.whiterose.ac.uk/162126/Publication
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited: 24 Jun 2020 14:29
URI: https://archive.researchdata.leeds.ac.uk/id/eprint/699

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