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Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection.

David L Tabb1, Kyowon Jeong2, Karen Druart1

  • 1Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France.

Journal of Proteome Research
|May 26, 2023
PubMed
Summary
This summary is machine-generated.

State-of-the-art top-down proteomics algorithms offer high proteoform-spectrum match (PrSM) yields, but results vary significantly between workflows and deconvolution engines, impacting post-translational modification detection. Comprehensive analysis requires using multiple search engines for improved experimental assessment.

Keywords:
bioinformaticsdeconvolutionidentification algorithmspost-translational modificationsproteoformstop-down proteomics.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Top-down tandem mass spectrometry (MS/MS) is crucial for analyzing complex proteoforms.
  • Advancements in separation, fragmentation, and mass analysis have improved MS/MS data generation.
  • Sophisticated algorithms are essential for matching MS/MS spectra to protein sequences, yielding proteoform-spectrum matches (PrSMs).

Purpose of the Study:

  • To assess the performance of leading top-down identification algorithms (ProSight PD, TopPIC, MSPathFinderT, pTop) in terms of PrSM yield and false discovery rate control.
  • To evaluate deconvolution engines for accurate precursor charge and mass determination using Orbitrap and Q-TOF data.
  • To investigate the detection consistency of post-translational modifications (PTMs) across different algorithms.

Main Methods:

  • Comparative analysis of four state-of-the-art top-down identification algorithms.
  • Evaluation of five deconvolution engines on ThermoFisher Orbitrap and Bruker maXis Q-TOF datasets (PXD033208).
  • Analysis of proteoforms and PTMs in bovine milk (PXD031744) and human ovarian tissue samples.

Main Results:

  • Contemporary top-down workflows achieve high PrSM yields, but approximately 50% of identified proteoforms were unique to a single algorithm.
  • Deconvolution algorithms exhibited discrepancies in precursor mass and charge assignments, contributing to identification variability.
  • PTM detection was inconsistent across algorithms; for example, singly phosphorylated proteoforms in bovine milk varied from 18% to 1% depending on the algorithm.

Conclusions:

  • While current top-down identification workflows are effective, significant variability exists due to differences in algorithms and deconvolution engines.
  • Inconsistent PTM detection highlights a critical area for improvement in top-down proteomics.
  • Employing multiple search engines is recommended for a more comprehensive proteomic analysis, and greater interoperability among top-down algorithms is needed.