Thermo Scientific Proteome Discoverer software

Significance of the topic

The rapid expansion of high-resolution mass spectrometry in proteomics demands intelligent data-processing platforms capable of handling diverse acquisition methods, complex study designs, and deep biological annotation. Thermo Scientific Proteome Discoverer software meets this need by integrating artificial-intelligence-driven search algorithms, flexible workflows, and comprehensive visualization tools to transform raw MS data into biologically meaningful results.

Objectives and overview of the study

This white paper presents the architecture, features, and performance of Proteome Discoverer software, focusing on its ability to:

• Support MS2 and MSn data from Orbitrap mass spectrometers including multiple fragmentation modes
• Leverage AI algorithms (CHIMERYS, INFERYS) to boost identification rates
• Manage studies end-to-end—from raw data import and factor mapping to statistical analysis
• Provide quantitative workflows for label-free, TMT/TMTpro, crosslinking, and top-down proteomics
• Offer built-in biological annotation and enrichment analysis

Methodology and instrumentation

Proteome Discoverer employs a node-based workflow system in which spectra pass through filters for precursor detection, search engines (Sequest HT, Comet, CHIMERYS), rescoring nodes (INFERYS), PTM localization (IMP-ptmRS), quantifiers (Reporter Ions, Minora Feature Detector), and FDR estimation (Percolator). Workflows can run search engines in series or parallel, integrate custom scripts via a Scripting node, and support hybrid acquisition strategies (CID, HCD, ETD, EThcD, UVPD, PTCR). Study management links raw files to experimental variables and channels for statistical comparison.

Used Instrumentation

• Thermo Scientific Orbitrap Exploris and Fusion series mass spectrometers
• Vanquish Neo UHPLC system
• FAIMS Pro Duo interface
• TMT, TMTpro and TurboTMT isobaric tagging reagents

Main results and discussion

Integration of CHIMERYS intelligent search yields up to 70% more protein IDs and 5× more PSMs in typical DDA runs compared to legacy workflows. INFERYS Rescoring adds intensity-based features to Percolator, boosting unique peptide identifications by over 15–20% and improving confidence in low-abundance and immunopeptidomics datasets. Label-free workflows employing Minora and CHIMERYS achieve deeper proteome coverage and robust quantification down to sub-nanogram loads. TMT/TMTpro SPS-MS3 workflows, augmented by real-time Comet searches, deliver high accuracy and interference-free quantification. XlinkX nodes facilitate crosslinked peptide ID and quantification, and ProSightPD software handles top-down proteoform discovery across multiple fragmentation methods.

Benefits and practical applications of the method

• Enhanced depth and confidence in peptide/protein identification through AI-driven deconvolution and rescoring
• Unified framework for multiple quantitative approaches (LFQ, TMT, crosslinking, top-down)
• Integrated study design, statistical analysis (pairwise ratios, background-based p-values), and dynamic visualization (volcano plots, PCA, heat maps)
• Built-in biological annotation and pathway enrichment to expedite functional interpretation
• Extensibility via third-party nodes and custom scripting for tailored workflows

Future trends and applications

Advances in machine-learning models for spectrum prediction and chimeric deconvolution will further deepen proteome mining. Emerging single-cell, metaproteomics, and clinical immunopeptidomics applications will benefit from the platform’s sensitivity and AI-based confidence measures. Integration with cloud computing and real-time instrument control promises automated, adaptive acquisition strategies. Expansion of top-down and structural proteomics through advanced crosslinking and proteoform mapping will drive new biological insights.

Conclusion

Thermo Scientific Proteome Discoverer software represents a comprehensive, AI-empowered platform for modern proteomics. Its flexible, extensible workflows, coupled with powerful visualization and annotation tools, enable researchers to extract maximum value from complex mass spectrometry datasets and accelerate biological discovery.

References

1. Schweppe et al. Anal. Chem. 2019;91(6):4010–4016.
2. Hughes et al. Chem. Sci. 2021;12:7612–7629.
3. Li et al. Nat. Methods 2022; doi:10.1038/s41592-020-0781-4.
4. Taus et al. J. Proteome Res. 2011;10:5354–5362.