A strategy for ionic current analysis of nanopore protein readouts

Abstract

BackgroundNanopore-based protein reading holds promise for single-molecule proteomics, but analyzing the resulting ionic current signals presents substantial computational challenges. Unlike nucleic acids, peptide-associated ionic signals exhibit complex patterns with subtle transitions between amino acids that can be obscured by noise. We developed an integrated computational strategy for processing nanopore protein ionic current signals from synthetic protein constructs containing all 20 amino acids in controlled sequence contexts. We started with denoising to remove artifacts, followed by change point detection using variable-number and fixed-number segmentation methods.

ResultsWe employed dynamic time warping barycenter averaging to generate amino acid-specific reference templates. In parallel, we performed classification of amino acid regions using bidirectional LSTM networks trained on segment-level statistical features. Accuracy for 20-way amino acid classification remained limited, while charge-based binary classification reached 93.3% accuracy. Pairwise classification revealed that certain amino acids, particularly negatively charged residues, generate distinctive signatures. We used a profile hidden Markov model to capture sequential structure of protein translocation.

ConclusionThis approach provides computational strategies for automated segmentation, classification, and modeling of nanopore peptide translocation ionic current data.

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BibTeX RIS

BibTeX

@misc{stein2026strategy,
  title = {{A strategy for ionic current analysis of nanopore protein readouts}},
  author = {A. J. Stein and N. Ghohabi Esfahani and S. Akeson and P. D. Kakhaki and D. Kontogiorgos-Heintz and J. Nivala and M. Jain},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.28.755023},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.28.755023v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - A strategy for ionic current analysis of nanopore protein readouts
AU  - A. J. Stein
AU  - N. Ghohabi Esfahani
AU  - S. Akeson
AU  - P. D. Kakhaki
AU  - D. Kontogiorgos-Heintz
AU  - J. Nivala
AU  - M. Jain
PY  - 2026
DA  - 2026/09/28
PB  - bioRxiv
DO  - 10.64898/2026.09.28.755023
UR  - https://www.biorxiv.org/content/10.64898/2026.09.28.755023v1
AB  - BackgroundNanopore-based protein reading holds promise for single-molecule proteomics, but analyzing the resulting ionic current signals presents substantial computational challenges. Unlike nucleic acids, peptide-associated ionic signals exhibit complex patterns with subtle transitions between amino acids that can be obscured by noise. We developed an integrated computational strategy for processing nanopore protein ionic current signals from synthetic protein constructs containing all 20 amino acids in controlled sequence contexts. We started with denoising to remove artifacts, followed by change point detection using variable-number and fixed-number segmentation methods.  ResultsWe employed dynamic time warping barycenter averaging to generate amino acid-specific reference templates. In parallel, we performed classification of amino acid regions using bidirectional LSTM networks trained on segment-level statistical features. Accuracy for 20-way amino acid classification remained limited, while charge-based binary classification reached 93.3% accuracy. Pairwise classification revealed that certain amino acids, particularly negatively charged residues, generate distinctive signatures. We used a profile hidden Markov model to capture sequential structure of protein translocation.  ConclusionThis approach provides computational strategies for automated segmentation, classification, and modeling of nanopore peptide translocation ionic current data.
ER  -

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