Improving Proteoform Identifications in Complex Systems Through Integration of Bottom-Up and Top-Down Data

Area of Science:

• Proteomics
• Mass Spectrometry
• Molecular Biology

Background:

• Cellular functions rely on diverse proteoforms, arising from genetic variations, RNA splicing, and post-translational modifications (PTMs).
• Top-down mass spectrometry identifies intact proteoforms, while bottom-up proteomics identifies peptides, requiring protein inference and lacking proteoform-level identification.
• Integrating these complementary data types can improve proteomic analysis depth and quality.

Purpose of the Study:

• To develop and apply a software tool for large-scale integration of top-down and bottom-up mass spectrometry data.
• To exploit the synergies between these methods for enhanced proteoform identification and characterization.
• To improve the quality and depth of proteomic analysis.

Main Methods:

• Development of the Proteoform Suite software for automated integration of multiprotease bottom-up and top-down proteomics data.
• Implementation of a proteoform-level classification scheme for top-down tandem mass spectrometry (MS/MS) identifications.
• Application of the software to analyze proteoforms from the human Jurkat T lymphocyte cell line.

Main Results:

• Demonstrated instances where top-down identifications aid bottom-up protein inference.
• Showcased how bottom-up peptide identifications assist in proteoform PTM localization.
• Utilized bottom-up data to infer potential proteoform candidates, confirmed by MS1 spectra analysis.

Conclusions:

• Proteoform Suite enables large-scale integration of top-down and bottom-up data, leveraging their synergies.
• The software improves and extends proteomic analysis by enhancing proteoform identification and characterization.
• This integrated approach offers a more comprehensive understanding of the proteome.