Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.4K
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...
6.4K
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

4.7K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
4.7K
Mitochondrial Precursor Proteins01:39

Mitochondrial Precursor Proteins

2.5K
Mitochondrial precursors are partially unfolded or loosely folded polypeptide chains. Newly synthesized precursors are inhibited from spontaneously folding into their native conformation by the cytosolic chaperones, heat shock proteins 70 (Hsp70), and mitochondrial import stimulation factors (MSFs). Precursors bound to MSFs are guided to the TOM70-TOM37 receptors, while precursors bound to Hsp70  chaperones are targetted to TOM20-TOM22 receptor complexes.
Most of the mitochondrial...
2.5K
Proteomics01:33

Proteomics

7.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.3K

You might also read

Related Articles

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

Sort by
Same author

Unveiling the occurrence and ecological risks of phthalate esters in the municipal wastewater treatment plant by a specific fragment-based GC-EI&PCI-HRMS method.

Water research·2026
Same author

Bridging data and discovery: a survey on knowledge graphs in AI for science.

National science review·2026
Same author

Simultaneous In-Depth Single-Cell Proteomic and Metabolomic Analysis.

Analytical chemistry·2026
Same author

Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.

Genome biology·2026
Same author

Gegen Qinlian decoction alleviates DSS-induced colitis in mice through coordinated modulation of gut microbiota, serum metabolome, and colonic γδT cell responses.

Frontiers in immunology·2026
Same author

Response to "Comment on 'Unsupervised Machine Learning for Differential Analysis in Proteomics' ".

Analytical chemistry·2026
Same journal

Proteomic Profiling of Extracellular Vesicle-Enriched Plasma Using Mag-Net for Biomarker Discovery in Pancreatic Ductal Adenocarcinoma.

Journal of proteome research·2026
Same journal

Computationally Efficient Bayesian Estimation of Graphical Networks for Omics Data.

Journal of proteome research·2026
Same journal

Hierarchy of MS-Based Evidence.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Exosomes from HPV-Positive and HPV-Negative Head and Neck Squamous Cell Carcinoma: Selective Cargo Packaging.

Journal of proteome research·2026
Same journal

Proteomic Analysis Identifies ATE1-Dependent Arginylation Dysregulation across Meningioma Grades.

Journal of proteome research·2026
Same journal

Proteomic Impact of Peripheral Expression of Mutant Huntingtin in <i>C. elegans</i>.

Journal of proteome research·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2025

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry
11:54

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

9.5K

Deep Learning Powers Protein Identification from Precursor MS Information.

Yameng Dai1, Yi Yang2, Enhui Wu1

  • 1Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai 200000, China.

Journal of Proteome Research
|August 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces MonoMS1, a novel computational approach that enhances proteome analysis by utilizing MS1 features alongside tandem mass spectrometry (MS/MS). This method significantly improves peptide and protein identification, especially for complex and low-abundance samples.

Keywords:
MS1 featuredeep learningproteomicsserum proteomicssingle cell

More Related Videos

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
09:35

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

13.8K
High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis
09:33

High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis

Published on: October 15, 2019

7.2K

Related Experiment Videos

Last Updated: Jun 15, 2025

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry
11:54

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

9.5K
Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
09:35

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

13.8K
High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis
09:33

High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis

Published on: October 15, 2019

7.2K

Area of Science:

  • Proteomics
  • Computational Biology
  • Mass Spectrometry

Background:

  • Tandem mass spectrometry (MS/MS) is a primary technique for proteome analysis.
  • MS/MS often underutilizes MS1 features, leading to incomplete identification, particularly in low-abundance or wide dynamic range samples.
  • Complementing MS/MS with MS1 features offers a promising strategy to enhance proteomic coverage.

Purpose of the Study:

  • To develop and validate MonoMS1, a novel computational approach for MS1 feature identification.
  • To improve the comprehensive identification of peptides and proteins in complex proteomic samples.
  • To demonstrate the utility of MonoMS1 in enhancing proteomic analysis of low-abundance and wide dynamic range samples.

Main Methods:

  • Developed MonoMS1, integrating deep learning for retention time, ion mobility, and detectability prediction.
  • Employed logistic regression-based scoring for MS1 feature identification.
  • Applied the MonoMS1 approach to diverse proteomic datasets, including *E. coli*, human serum, and single-cell proteome samples.

Main Results:

  • MonoMS1 significantly increased MS1 feature identification in an *E. coli* dataset.
  • The approach substantially complemented MS/MS-based identification in human serum (wide dynamic range) and single-cell (low abundance) proteome samples.
  • Demonstrated enhanced peptide and protein coverage through the integration of MS1 features.

Conclusions:

  • MonoMS1 offers a powerful new method for MS1 feature identification in proteomic analysis.
  • This approach significantly boosts the identification capabilities for complex and challenging proteomic samples.
  • MonoMS1 represents a valuable advancement for proteomic research, particularly in areas requiring deep coverage.