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Related Concept Videos

Proteomics01:33

Proteomics

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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

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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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A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues
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Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.

Xinpei Yi1, Bo Wen1, Shuyi Ji2

  • 1Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

Molecular & Cellular Proteomics : MCP
|December 28, 2023
PubMed
Summary

DeepRescore2 enhances shotgun phosphoproteomics by improving phosphopeptide identification and localization using deep learning. This computational workflow significantly increases identified phosphopeptides, aiding biological discovery in complex datasets.

Keywords:
EGFRdeep learningfragment ion intensityliver cancerphosphopeptide identificationphosphoproteomicsphosphosite localizationrescoreretention time

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A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

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Area of Science:

  • Proteomics
  • Computational Biology
  • Bioinformatics

Background:

  • Shotgun phosphoproteomics is crucial for high-throughput analysis of phosphopeptides.
  • Low phosphopeptide identification rates limit the potential of shotgun phosphoproteomics.
  • Accurate phosphosite localization is essential for understanding signaling pathways.

Purpose of the Study:

  • To present DeepRescore2, a novel computational workflow.
  • To enhance phosphopeptide identification and phosphosite localization rates in shotgun phosphoproteomics data.
  • To improve the biological insights derived from phosphoproteomic analyses.

Main Methods:

  • Leveraging deep learning for retention time and fragment ion intensity predictions.
  • Developing a computational workflow (DeepRescore2) for phosphopeptide analysis.
  • Benchmarking against state-of-the-art computational workflows using synthetic and biological datasets.

Main Results:

  • DeepRescore2 increased correctly identified peptide-spectrum matches by 17% in synthetic data.
  • Identified 19% to 46% more phosphopeptides in biological datasets compared to benchmark.
  • Uniquely identified EGFR hyperactivation as a new target in poor-prognosis liver cancer, validated experimentally.

Conclusions:

  • Deep learning integration in DeepRescore2 significantly improves phosphopeptide identification and localization.
  • DeepRescore2 facilitates deeper biological discoveries, including novel therapeutic targets.
  • The workflow enhances the utility of shotgun phosphoproteomics for biological research and clinical applications.