ArticlePLoS computational biology2019
Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Interrogating nanopores with light: optipore sensing for single molecule analyses.Journal of nanobiotechnology · 2026Review
- Principles ofbioRxiv : the preprint server for biology · 2026Article
- Article
- Towards improved biofilm models.Nature reviews. Microbiology · 2025Review
- Nanopore DNA sequencing technologies and their applications towards single-molecule proteomics.Nature chemistry · 2024Review
- Recent methodological advances towards single-cell proteomics.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2023Review
- Review
- Single-Molecule Protein Fingerprinting with Photonic Hexagonal Boron Nitride Nanopores.Accounts of materials research · 2023Article
- Portable nanopore-sequencing technology: Trends in development and applications.Frontiers in microbiology · 2023Review
- Protein Sequencing, One Molecule at a Time.Annual review of biophysics · 2022Review
- Distinguishing between Similar Miniproteins with Single-Molecule Nanopore Sensing: A Computational Study.ACS nanoscience Au · 2022Article
- Biological nanopores for single-molecule sensing.iScience · 2022Review
- Lifetime-based analysis of binary fluorophores mixtures in the low photon count limit.iScience · 2022Article
- Article
- A Guide to Signal Processing Algorithms for Nanopore Sensors.ACS sensors · 2021Article
- Article
- Article
- Herding cats: Label-based approaches in protein translocation through nanopore sensors for single-molecule protein sequence analysis.iScience · 2021Review
- Theoretical Considerations for Next-Generation Proteomics.Journal of proteome research · 2021Article
- The emerging landscape of single-molecule protein sequencing technologies.Nature methods · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Single-molecule techniques for protein sequencing are making headway towards single-cell proteomics and are projected to propel our understanding of cellular biology and disease. Yet, single cell proteomics presents a substantial unmet challenge due to the unavailability of protein amplification techniques, and the vast dynamic-range of protein expression in cells. Here, we describe and computationally investigate the feasibility of a novel approach for single-protein identification using tri-color fluorescence and plasmonic-nanopore devices. Comprehensive computer simulations of denatured protein translocation processes through the nanopores show that the tri-color fluorescence time-traces retain sufficient information to permit pattern-recognition algorithms to correctly identify the vast majority of proteins in the human proteome. Importantly, even when taking into account realistic experimental conditions, which restrict the spatial and temporal resolutions as well as the labeling efficiency, and add substantial noise, a deep-learning protein classifier achieves 97% whole-proteome accuracies. Applying our approach for protein datasets of clinical relevancy, such as the plasma proteome or cytokine panels, we obtain ~98% correct protein identification. This study suggests the feasibility of a method for accurate and high-throughput protein identification, which is highly versatile and applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.