Improvements to ProSightPD nodes in the Thermo Scientific Proteome Discoverer Software Framework

Importance of the Topic

Top-down proteomics delivers detailed insights into proteoforms, encompassing sequence variants and post-translational modifications. This level of characterization is vital for understanding functional protein diversity in biological systems and for identifying potential disease biomarkers.

Objectives and Overview of the Study

This work aims to demonstrate the enhanced ProSightPD nodes within the Thermo Scientific Proteome Discoverer framework for complex top-down proteomics analyses. It further integrates sliding window deconvolution results from Biopharma Finder to identify differentially expressed proteoforms in WHIM2 and WHIM16 tumor xenograft samples.

Methodology and Instrumentation

Data from High/High and Low/High GELFrEE fractions (Ntai et al.) were processed using:

• Proteome Discoverer with ProSightPD Base and High Mass nodes
• Five-step search workflow for High/High data; three-step search for Low/High data
• Xtract algorithm for MS1 and MS/MS deconvolution
• ReSpect deconvolution for Low/High MS1 spectra
• Sliding window deconvolution in Biopharma Finder 3.1
• Perl scripting for integration of PrSMs and deconvolution outputs
• LOESS normalization and p-value calculation via InfernoRDN and Microsoft Excel

Instrumentation included Orbitrap-based MS with HCD fragmentation and ion trap acquisition for MS1.

Main Results and Discussion

High/High analyses yielded 2,374 proteoforms (735 with C-score ≥3), while Low/High runs identified 254 proteoforms (38 with C-score ≥3), including many heavier than 40 kDa. Integration of sliding window deconvolution revealed over 400 proteoforms with significant abundance differences between WHIM2 and WHIM16. WHIM2 displayed a higher overall number of proteoform spectrum matches (PrSMs), reflecting greater sample load. Mirror plot comparisons highlighted sample-specific proteoforms, and C-score filtering provided a robust confidence measure for proteoform identification.

Benefits and Practical Applications

• Comprehensive multi-step search strategies enhance proteoform coverage
• C-score metrics offer reliable confidence assessment
• Label-free quantitation via sliding window deconvolution supports differential expression analysis
• Identification of high-mass proteoforms expands detectable proteome space

Future Trends and Possibilities

• Adoption of medium/high MS1 resolution to narrow precursor mass tolerances
• Integration of alternative fragmentation methods (e.g., CID) for improved sequence coverage
• Advancements in deconvolution algorithms for enhanced accuracy
• Scalability to larger clinical or biomarker discovery studies

Conclusion

The improved ProSightPD nodes, combined with sliding window deconvolution workflows, provide a powerful and quantitative platform for top-down proteomics. This integrated approach enables confident detection and differential analysis of proteoforms in complex biological samples.

Reference

1. Ntai I, et al. Mol Cell Proteomics. 2016;15:45–56.
2. LeDuc RD, et al. J Proteome Res. 2014;13:3231–3240.
3. Fellers RT, et al. Proteomics. 2015;15:1235–1238.