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Cited by 159

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Nanopore Sensing of Protein Folding

Open access
Si, Wei, Aksimentiev, Aleksei·2017·ACS Nano

Abstract

Single-molecule studies of protein folding hold keys to unveiling protein folding pathways and elusive intermediate folding states-attractive pharmaceutical targets. Although conventional single-molecule approaches can detect folding intermediates, they presently lack throughput and require elaborate labeling. Here, we theoretically show that measurements of ionic current through a nanopore containing a protein can report on the protein's folding state. Our all-atom molecular dynamics (MD) simulations show that the unfolding of a protein lowers the nanopore ionic current, an effect that originates from the reduction of ion mobility in proximity to a protein. Using a theoretical model, we show that the average change in ionic current produced by a folding-unfolding transition is detectable despite the orientational and conformational heterogeneity of the folded and unfolded states. By analyzing millisecond-long all-atom MD simulations of multiple protein transitions, we show that a nanopore ionic current recording can detect folding-unfolding transitions in real time and report on the structure of folding intermediates.

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Metadata

Authors
Si, W., & Aksimentiev, A.
Venue
ACS Nano
Published
2017-07-13
Volume
11
Issue
7
Pages
7091-7100
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2735310269
Cited by
159
References
6
FWCI
6.63
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Nanopore Sensing of Protein Folding. https://app.cochat.ai/papers/10.1021/acsnano.7b02718
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Si, W., & Aksimentiev, A. (2017). Nanopore Sensing of Protein Folding. ACS Nano. https://doi.org/10.1021/acsnano.7b02718

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.