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

Trends and future projections of the disease burden of pulmonary arterial Hypertension-related heart failure in China and G20 countries: Based on the global burden of disease 2023 database.

International journal of cardiology. Cardiovascular risk and prevention·2026

Same author

Observation of the therapeutic effect and analysis of risk factors in the acute repair of traumatic digital nerve injury.

Chinese journal of traumatology = Zhonghua chuang shang za zhi·2026

Same author

Population pharmacokinetic analysis and dosing regimen optimization of eravacycline in critically ill patients with carbapenem-resistant Acinetobacter baumannii pneumonia.

The Journal of antimicrobial chemotherapy·2026

Same author

Multi-organ protective effects of ursodeoxycholic acid in a rat model of type 2 diabetes mellitus.

Biochemical and biophysical research communications·2025

Same author

Ultra-rapid preparation of large-area high-crystallinity covalent organic framework membranes.

Nature communications·2025

Same author

The Split-Level Folding, Step-Type Tension-Relieving Suture Technique Improves the Wound Tensile Strength.

Aesthetic plastic surgery·2025

Search research articles

Related Experiment Video

Updated: Sep 6, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides.

You Li¹, Xueyong Li¹, Yuewu Liu²

¹School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China.

Pharmaceuticals (Basel, Switzerland)

|June 24, 2022

Summary

We developed a deep learning method to identify multiple activities of bioactive peptides. This approach accurately recognizes peptide functions, outperforming existing methods and aiding in drug discovery.

Keywords:

bioactive peptide convolution neural network deep learning long short-term memory multi-label issues

More Related Videos

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Published on: May 31, 2022

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 6, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Published on: May 31, 2022

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:

Biochemistry
Bioinformatics
Computational Biology

Background:

Bioactive peptides are small molecules (2-20 amino acids) with diverse biological roles.
Identifying multiple functions of bioactive peptides simultaneously is a significant challenge due to their complexity.

Purpose of the Study:

To develop a novel deep learning model for recognizing the multi-activities of bioactive peptides.
To improve the accuracy and efficiency of bioactive peptide function prediction.

Main Methods:

A deep learning model, MPMABP, was proposed, integrating Convolutional Neural Networks (CNNs) and Bi-directional Long Short-Term Memory (Bi-LSTM).
The model utilizes stacked CNNs at various scales and residual networks to prevent information loss during processing.

Main Results:

The MPMABP model demonstrated superior performance compared to existing state-of-the-art methods in recognizing multi-activities.
Analysis revealed specific amino acid preferences for different activities: lysine (anti-cancer), leucine (anti-diabetic), and proline (anti-hypertensive).

Conclusions:

The proposed MPMABP method offers an effective solution for identifying multiple activities of bioactive peptides.
The findings provide valuable insights into the relationship between amino acid composition and peptide function, aiding in the design of novel therapeutic peptides.