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Related Experiment Video

Updated: Jun 23, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

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Published on: November 15, 2017

ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra.

Jiancheng Zhong1, Yicheng Luo1, Chen Yang1

  • 1College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.

Interdisciplinary Sciences, Computational Life Sciences
|May 17, 2025
PubMed
Summary
This summary is machine-generated.

PrSMBooster, a deep learning model, enhances proteoform identification accuracy in mass spectrometry. It improves proteoform spectrum match scoring, increasing characterization results and demonstrating strong generalization capabilities.

Keywords:
Deep learningProteoform characterizationRescoring

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Area of Science:

  • Proteomics
  • Computational Biology
  • Mass Spectrometry

Background:

  • Accurate proteoform identification is crucial in top-down proteomics.
  • Protein primary structure variations create diverse proteoforms, complicating analysis.
  • Lack of a reference set hinders standardization and accuracy.

Purpose of the Study:

  • To develop and validate PrSMBooster, a deep learning model for improving proteoform characterization.
  • To enhance the accuracy of proteoform spectrum match (PrSM) scoring.

Main Methods:

  • Introduced PrSMBooster, a ResNeXt-based deep learning model.
  • Utilized an ensemble approach integrating logistic regression, XGBoost, decision tree, and support vector machine.
  • Input basic and latent PrSM features into the ResNeXt model for rescoring.

Main Results:

  • PrSMBooster increased the number of identified proteoform spectrum matches (PrSMs) at a 1% false discovery rate across 47 datasets.
  • Demonstrated improved accuracy in PrSM scoring compared to the TopPIC algorithm.
  • Showcased strong generalization ability across diverse mass spectrometry datasets.

Conclusions:

  • PrSMBooster significantly enhances the accuracy and scope of proteoform characterization in top-down proteomics.
  • The model offers a robust solution for improving mass spectrometry data analysis.
  • PrSMBooster's generalization ability makes it valuable for various proteomic studies.