Maximizing Proteome Coverage with Advanced Peak Determination Algorithm on Tribrid Mass Spectrometers

Significance of the Topic

The depth and speed of proteome profiling are central to modern biological and clinical research. Improved peak determination during real-time data acquisition allows more comprehensive sampling of peptide ions, thereby increasing peptide identifications and reproducibility.

Objectives and Study Overview

This study evaluates an advanced peak determination (APD) algorithm on two Tribrid platforms (Orbitrap Fusion and Orbitrap Fusion Lumos). Key goals:

• Compare APD versus legacy THRASH-based peak picking in data-dependent LC-MS/MS.
• Optimize MS2 acquisition parameters to maximize spectral quality and throughput.
• Assess gains in unique peptide identifications and reproducibility.
• Demonstrate APD benefits for top-down intact protein analysis.

Methodology

Complex HeLa peptide digests and intact protein standards were analyzed by data-dependent acquisition with dynamic exclusion and charge state filters. MS2 spectra were collected at high scan rates, with automatic mass range determination and varied maximum injection times. Resulting data were searched with Proteome Discoverer, controlling false discovery at 1%.

Used Instrumentation

• Orbitrap Fusion Lumos Tribrid mass spectrometer
• Orbitrap Fusion Tribrid mass spectrometer
• Easy-nLC 1200 ultra-high pressure liquid chromatograph
• UltiMate 3000 ultra-high pressure LC system
• Proteome Discoverer 2.2 software for database searching

Main Results and Discussion

APD improved precursor utilization from ~60% to ~95% of available MS2 cycle time, leading to over 250,000 MS2 spectra in a 2 h LC gradient. Key findings:

• Unique peptide identifications increased by >35%, from ~33,000 to >45,000 in 2 h runs.
• APD enabled half-length LC gradients (1 h) to match legacy 2 h coverage, enhancing throughput.
• Optimized ITMS2 injection time of 20 ms balanced spectral quality and rate for APD runs.
• Reproducibility improved: the limit of reproducible identification was 2–3× lower, and overlap among triplicate runs increased markedly.
• APD’s ability to annotate overlapping isotopic envelopes and correlate assignments across charge envelopes unlocked more precursors for MS2.
• In top-down analyses of protein standards, APD enhanced charge state assignment for large biomolecules, supporting advanced intact protein workflows.

Benefits and Practical Applications

The APD approach enables deeper proteome coverage without hardware changes by leveraging software improvements. Benefits include:

• Higher throughput in discovery proteomics through shorter gradients.
• Enhanced reproducibility and sensitivity for quantitative studies.
• Robust top-down performance for intact protein characterization.

Future Trends and Potential Applications

Further algorithmic refinements combined with emerging acquisition schemes (e.g., parallel accumulation and serial fragmentation) may push MS sampling rates beyond 40 Hz. Integration of APD with real-time database searching and intelligent acquisition promises targeted deep proteome and proteoform analysis in clinical and biopharmaceutical settings.

Conclusion

Implementing the APD algorithm on Tribrid Orbitrap platforms significantly increases peptide identifications and reproducibility by exploiting overlapping isotopic signals and improved charge deconvolution. This software-centric advancement enhances both bottom-up and top-down proteomics capabilities without sacrificing spectral quality.

Reference

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