ArticleNature nanotechnology2026
High-resolution nanopore peptide sensing, profiling and sequence assembly.
Article in Nature nanotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Sensing forces that shape tumours.Nature nanotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Nanopores have been explored as a potential platform for protein analysis following the success of nanopore nucleic acid sequencing. However, protein sequencing remains technically challenging and has not yet been established for proteomics use. Here a nickel-immobilized Mycobacterium smegmatis porin A (MspA-NTA-Ni) nanopore is shown to enable the identification of a range of proteomic analytes, including amino acids and peptides up to 39 amino acids in length. Under identical conditions, signals corresponding to 20 proteinogenic amino acids, 4 post-translationally modified amino acids, 32 peptides, 6 modified peptides, 11 bioactive peptides and 2 neoantigen peptides were recorded. Machine-learning-based analysis enabled classification of these analytes with a validation accuracy of up to 97.4% within the studied dataset. The MspA-NTA-Ni nanopore supports both direct peptide identification and peptide profiling following enzymatic hydrolysis. As a proof of concept, a reference peptide was digested using exo- and endopeptidases to generate overlapping peptide fragments. Nanopore measurements combined with machine learning predictions enabled the identification of fragment compositions and partial sequences, allowing reconstruction of the original peptide sequence. This hydrolysis-based approach shows sensitivity to sequence alterations, including mutations, deletions and post-translational modifications, indicating potential utility for targeted peptide characterization.
Indexed as
Identifiers
42298104What Socratic 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 Socratic graph.