High Accuracy Protein Identification: Fusion of Solid-State Nanopore Sensing and Machine Learning.
biophysics
biosensors
biotechnology
machine learning
nanopores
protein identification
Journal
Small methods
ISSN: 2366-9608
Titre abrégé: Small Methods
Pays: Germany
ID NLM: 101724536
Informations de publication
Date de publication:
11 2023
11 2023
Historique:
revised:
25
07
2023
received:
29
05
2023
medline:
16
11
2023
pubmed:
18
9
2023
entrez:
18
9
2023
Statut:
ppublish
Résumé
Proteins are arguably one of the most important class of biomarkers for health diagnostic purposes. Label-free solid-state nanopore sensing is a versatile technique for sensing and analyzing biomolecules such as proteins at single-molecule level. While molecular-level information on size, shape, and charge of proteins can be assessed by nanopores, the identification of proteins with comparable sizes remains a challenge. Here, solid-state nanopore sensing is combined with machine learning to address this challenge. The translocations of four similarly sized proteins is assessed using amplifiers with bandwidths (BWs) of 100 kHz and 10 MHz, the highest bandwidth reported for protein sensing, using nanopores fabricated in <10 nm thick silicon nitride membranes. F-values of up to 65.9% and 83.2% (without clustering of the protein signals) are achieved with 100 kHz and 10 MHz BW measurements, respectively, for identification of the four proteins. The accuracy of protein identification is further enhanced by classifying the signals into different clusters based on signal attributes, with F-value and specificity of up to 88.7% and 96.4%, respectively, for combinations of four proteins. The combined use of high bandwidth instruments, advanced clustering and machine learning methods allows label-free identification of proteins with high accuracy.
Identifiants
pubmed: 37718979
doi: 10.1002/smtd.202300676
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2300676Subventions
Organisme : AINSE
Organisme : Australian Research Council
ID : DP180100068
Organisme : Nvidia
Informations de copyright
© 2023 The Authors. Small Methods published by Wiley-VCH GmbH.
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