Proteoform Identification Using Multiplexed Top‐Down Mass Spectra
Top-down mass spectrometry (TDMS) is the method of choice for analyzing intact proteoforms, as well as their posttranslational modifications and sequence variations. In top-down tandem mass spectrometry (TD-MS/MS) experiments, multiple proteoforms are often co-fragmented, resulting in multiplexed TD-MS/MS spectra. Due to their increased complexity, compared to spectra from single proteoforms, multiplexed TD-MS/MS spectra present significant challenges for proteoform identification and quantification. Here we present TopMPI, a new computational tool specifically designed for the identification of multiplexed TD-MS/MS spectra. Experimental results demonstrate that TopMPI substantially increases the sensitivity and accuracy of proteoform identification in multiplexed TD-MS/MS spectral analysis compared to existing tools. SUMMARY: Top-down mass spectrometry (TDMS) is a powerful technique for analyzing intact proteoforms; however, identifying multiple co-fragmented proteoforms from multiplexed tandem mass spectrometry (MS/MS) spectra remains a significant challenge. In this paper, we introduce TopMPI, a new computational tool specifically designed to identify multiplexed TD-MS/MS spectra using a two-round database search strategy. Compared to existing tools, TopMPI significantly improves the sensitivity and accuracy of proteoform identification from multiplexed MS/MS spectra. The development of TopMPI enhances the identification of low abundance proteoforms in complex biological samples and increases the potential of TDMS for discovering proteoform biomarkers in disease studies.
Authors
- Xingzhao Xiong (ORCID: https://orcid.org/0000-0002-9384-4573)
- Xiaowen Liu (ORCID: https://orcid.org/0000-0003-4139-1127)
- Zhige Wang
Institutions
- Tulane University (US)
Publication Details
- Journal
- PROTEOMICS
- Published
- 2025-07-30
- DOI
- https://doi.org/10.1002/pmic.70020
- Citations
- 1
- Primary Topic
- Mass Spectrometry Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.76
Funders
- National Institutes of Health