Increased Dynamic Range of DDA-based label-free quantification using the CHIMERYS algorithm

Importance of the topic

Label-free quantification in proteomics enables accurate measurement of protein abundance across complex biological samples. Conventional DDA engines often fail to resolve chimeric MS/MS spectra and detect low-abundance peptides, limiting dynamic range and proteome coverage. Advanced algorithms such as CHIMERYS deconvolute mixed spectra, increasing peptide discovery and improving sensitivity, which is critical for deep profiling and biomarker identification.

Objectives and overview

The primary goals of this study are to:

• Evaluate the performance of the CHIMERYS intelligent search algorithm in label-free quantification workflows.
• Compare CHIMERYS processing against standard Sequest HT and a combined multi-engine strategy within Proteome Discoverer 3.0.
• Assess improvements in identification rates, dynamic range, and quantification precision using mixed human and E. coli proteome samples.

Methodology

Proteome mixtures containing varying amounts of E. coli digest (0–200 ng) spiked into 1 µg human digest were analyzed in triplicate. Data were acquired on a DDA method with ion trap HCD MS/MS. Feature detection was performed with the Minora node, followed by three processing workflows: Sequest HT, CHIMERYS, and a branched three-node strategy combining CHIMERYS, INFERYS rescoring, and MSPepSearch. Quantification used default consensus workflow settings with 50% channel occupancy, background-based p values, and pairwise ratio calculation at 1% FDR.

Instrumentation

• Thermo Scientific Orbitrap Eclipse Tribrid mass spectrometer
• FAIMS Pro interface for ion mobility separation
• HCD fragmentation in the ion trap (0.7 m/z isolation window; 1 s cycle time)
• MS1 resolution set to 240 000
• Proteome Discoverer 3.0 software with Sequest HT, CHIMERYS, INFERYS Rescoring, and MSPepSearch nodes

Main results and discussion

• CHIMERYS increased unique peptide identifications by 19% and protein identifications by 7% compared to Sequest HT.
• Quantified peptide counts rose by up to 83% at low sample loads, with CHIMERYS resolving up to six PSMs per spectrum versus one PSM for Sequest HT.
• The combined three-node workflow yielded 10 045 proteins and 144 709 unique peptides, enhancing both depth and dynamic range.
• Low-abundance peptides (Log10 intensity < 6) were more frequently detected, boosting sensitivity for minor species.
• Quantification precision remained high, accurately distinguishing expected human:E. coli ratios across concentration series.

Benefits and practical applications

• Improved proteome coverage and dynamic range support deeper exploration of complex biological samples.
• Enhanced quantification reliability aids biomarkers discovery, QA/QC, and comparative proteomic studies.
• Seamless integration in Proteome Discoverer 3.0 facilitates adoption in research, clinical, and industrial laboratories.

Future trends and possibilities

• Application of CHIMERYS to high-resolution MS/MS and wider isolation windows to further expand peptide identification capabilities.
• Use in multiplexed quantification and post-translational modification analyses to enhance proteome coverage.
• Real-time database searching and adaptive acquisition strategies leveraging CHIMERYS intelligence.
• Integration with machine learning–based rescoring for further gains in identification confidence and throughput.

Conclusion

CHIMERYS significantly outperforms conventional Sequest HT in label-free quantification workflows, delivering higher identification rates, expanded dynamic range, and robust quantification precision. When combined with INFERYS rescoring and MSPepSearch in Proteome Discoverer 3.0, CHIMERYS enables comprehensive proteome profiling, especially for low-abundance peptides.

Reference

• INFERYS rescoring: Boosting peptide identifications and scoring confidence for database search results Rapid Communications in Mass Spectrometry 2021 DOI 10.1002/rcm.9128