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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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MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics.

Marc Isaksson1,2, Christofer Karlsson3, Thomas Laurell1

  • 1Department of Biomedical Engineering, Lund University, 22100 Lund, Sweden.

Journal of Proteome Research
|January 19, 2022
PubMed
Summary

MSLibrarian optimizes spectral libraries for data-independent acquisition-mass spectrometry (DIA-MS) proteomics. This tool improves peptide and protein quantification accuracy in library-free DIA-MS analysis, reducing experimental costs.

Keywords:
R-softwaredata-independent acquisitiondeep-learningproteomicsspectral predictions

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Data-independent acquisition-mass spectrometry (DIA-MS) is crucial for deep, consistent proteomics profiling.
  • Traditional DIA-MS workflows rely on spectral libraries from data-dependent acquisition (DDA-MS), increasing experimental effort and cost.
  • Library-free DIA-MS approaches using in silico prediction offer efficiency but face limitations in coverage and sensitivity due to large library sizes and data deviations.

Purpose of the Study:

  • To introduce MSLibrarian, a novel workflow and tool for generating optimized predicted spectral libraries for DIA-MS.
  • To enhance the accuracy and efficiency of library-free DIA-MS analysis by integrating spectrum-centric DIA data interpretation.
  • To improve peptide and protein quantification in proteomics by calibrating in silico predictions with experimental data.

Main Methods:

  • Development of the MSLibrarian workflow integrating DIA-Umpire for spectrum-centric DIA data interpretation.
  • Calibration of in silico spectral library parameters, including intensity and retention time predictions, using predicted-vs-observed comparisons.
  • Optimization of library scope and sample representativeness for improved DIA-MS analysis.

Main Results:

  • MSLibrarian enables optimization of intensity and retention time predictions, crucial for accurate DIA-MS analysis.
  • The tool refines library scope and sample representativeness, enhancing overall analysis performance.
  • Benchmarking demonstrated significant gains: up to 13% on peptide and 8% on protein level quantification at equivalent FDR control.

Conclusions:

  • MSLibribrarian significantly improves the performance of library-free DIA-MS proteomics by optimizing predicted spectral libraries.
  • The integrated approach enhances quantification accuracy and sensitivity, reducing experimental complexity and cost.
  • MSLibrarian is provided as an open-source R package, facilitating its adoption in proteomics research.