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Related Concept Videos

Proteomics01:33

Proteomics

7.6K
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...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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...
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Updated: Jul 31, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep

Moran Chen1, Pujia Zhu1, Qiongqiong Wan1

  • 1The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China.

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|May 1, 2023
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Deep4D, a deep learning model, enhances four-dimensional (4D) data-independent acquisition (DIA) proteomics by predicting peptide properties. This enables library-free DIA analysis, significantly increasing protein identification coverage.

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Area of Science:

  • Proteomics and Bioinformatics
  • Computational Biology
  • Mass Spectrometry

Background:

  • Four-dimensional (4D) data-independent acquisition (DIA) proteomics offers advanced analytical capabilities.
  • Current limitations include time-intensive experimental library generation and incomplete peptide coverage.
  • These challenges hinder the full potential of 4D DIA-based proteomic analyses.

Purpose of the Study:

  • To develop a versatile deep learning model, Deep4D, for predicting key peptide properties in 4D DIA proteomics.
  • To establish comprehensive workflows for high-coverage 4D DIA proteomics and phosphoproteomics using multidimensional predictions.
  • To enable experimental library-free DIA analysis for improved efficiency and coverage.

Main Methods:

  • Development of Deep4D, a self-attention-based deep learning model.
  • Prediction of collisional cross section, retention time, fragment ion intensity, and charge state for unmodified and phosphorylated peptides.
  • Creation of a large-scale predicted peptide library (∼2 million peptides) for library-free DIA analysis.

Main Results:

  • Deep4D achieved high accuracy in predicting peptide properties.
  • A comprehensive 4D predicted library was established, facilitating library-free DIA proteomics.
  • In HeLa cell analysis, 33% more proteins were identified compared to using a single-shot experimental library.

Conclusions:

  • Deep4D significantly advances 4D DIA proteomics by enabling accurate multidimensional peptide predictions.
  • The developed workflows provide a convenient and high-coverage approach for proteomic and phosphoproteomic studies.
  • This method demonstrates substantial value in enhancing protein identification and streamlining DIA-based analyses.