Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

pH-Triggered Coassembly of Cello-oligosaccharides and Hyaluronic Acid into Hydrogels.

ACS applied bio materials·2025

Same author

Propargylated cello-oligosaccharide nanosheets: a water-dispersible, antibiofouling, and post-functionalizable solid support for immunoassays.

Journal of materials chemistry. B·2025

Same author

Nanospiked Cellulose Gauze That Attracts Bacteria with Biomolecules for Reducing Bacterial Load in Burn Wounds.

Nano letters·2025

Same author

Synthesis of Bimetallic Dinuclear Hydrido Complexes of Re and Rh/Ir Supported by a Direct M-M' Bond: The Role of the M-M' Bond in the Site Exchange of Hydrides.

Inorganic chemistry·2024

Same author

Peri-implant fractures after Intramedullary fixation for femoral trochanteric fracture: a multicenter (TRON Group) study.

European journal of trauma and emergency surgery : official publication of the European Trauma Society·2024

Same author

Freeze-Dryable, Stable, and Click-Reactive Nanoparticles Composed of Cello-oligosaccharides for Biomolecular Sensing.

ACS applied bio materials·2024

Same journal

Strategic Design and Engineering of CRISPR/Cas-Powered Sensing Platforms for Enhanced Nucleic Acid Detection.

ACS sensors·2026

Same journal

Broad-Temperature Polymerase in Nucleic Acid Amplification-Based Diagnostics: From Thermal Precision to Dynamic Conditions.

ACS sensors·2026

Same journal

Fluidic Lipid-Bilayer-Enhanced Iontronic Nanopore: Machine-Learning-Driven Ultrasensitive MicroRNA Detection in Cancer Diagnostics.

ACS sensors·2026

Same journal

Plant-Plant Communication for Systemic Acquired Resistance under Biotic Stress Spatiotemporally Tracked by an <i>In Situ</i> Surface-Enhanced Raman Spectroscopy Aerosol Spraying Analyzer.

ACS sensors·2026

Same journal

Modulating Electronic Structure via Bimetallic D<i>-</i>Band Engineering toward an Ultrasensitive Sensor Platform for Caffeic Acid in Food.

ACS sensors·2026

Same journal

Indiscriminate <i>T</i><i>rans</i>-Cleavage Activity of CRISPR/SuCas12a2 Enables Sensitive Detection of SARS-CoV-2.

ACS sensors·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging

Published on: December 8, 2023

Identification of Polymeric Nanoparticles Using Strategic Peptide Sensor Configurations and Machine Learning.

Shion Hasegawa¹, Toshiki Sawada¹, Yuzo Kitazawa²

¹Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo, 2-12-1-H121 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.

|June 21, 2025

Summary

This summary is machine-generated.

Scientists developed peptide sensors to identify nanoplastics in water. This breakthrough aids in detecting these tiny plastic pollutants, crucial for environmental and human health protection.

Keywords:

fluorescence signals machine learning nanoplastics peptides polymeric nanoparticles sensor

More Related Videos

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Related Experiment Videos

Last Updated: Sep 18, 2025

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging

Published on: December 8, 2023

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Environmental Science
Analytical Chemistry
Nanotechnology

Background:

Environmental pollution from microplastics and nanoplastics is a growing global concern.
Existing methods for microplastic detection are advanced, but nanoplastic identification remains challenging due to sampling and sensing difficulties.
Accurate identification of nanoplastics is critical for assessing their ecological and health risks.

Purpose of the Study:

To demonstrate a novel method for identifying polymeric nanoparticles in water using peptide-based sensors.
To explore the application of machine learning in analyzing sensor data for precise nanoplastic identification.
To evaluate the effectiveness of sensor design, including the number and combination of peptide sensors.

Main Methods:

Utilized peptide sensors functionalized with a microenvironment-sensitive fluorophore to detect polymeric nanoparticles.
Analyzed fluorescence spectra generated by the peptide sensors in response to different polymer species.
Applied supervised and unsupervised machine learning algorithms to classify and identify polymeric nanoparticles based on spectral signal patterns.

Main Results:

Peptide sensors exhibited distinct fluorescence spectra for different types of polymeric nanoparticles.
Machine learning successfully identified polymeric nanoparticles, even those with subtle chemical structure differences.
The number and specific combination of peptide sensors were found to be critical for accurate identification.

Conclusions:

The developed peptide sensor system offers a promising approach for identifying nanoplastics in aqueous environments.
This method provides foundational insights for future nanoplastic detection technologies.
The study highlights the synergy between peptide-based sensing and machine learning for advanced environmental monitoring.