Evaluation of search engines for phosphopeptide identification and quantitation

Importance of Phosphoproteome Analysis

Phosphorylation is a key regulatory mechanism in cellular signaling and disease pathways. High‐coverage, accurate identification and quantitation of phosphopeptides by mass spectrometry enables deeper insights into dynamic phosphorylation events.

Objectives and Study Overview

This work evaluates multiple MS fragmentation strategies (HCD, CID, EThcD, CID‐MSA) and search engines (Sequest HT, Byonic, Mascot, Andromeda) for phosphopeptide identification and TMT‐based quantitation. The goal is to optimize workflows for maximal peptide coverage, confident site localization, and robust quantitation in multiplexed experiments.

Methods and Instrumentation

• Sample Preparation: HeLa and yeast digests enriched with Fe‐NTA, labeled with TMT10plex reagents and mixed in defined ratios for interference and quantitation studies.
• LC‐MS: Thermo Scientific Orbitrap Fusion Lumos coupled to EASY‐nLC 1000 with a 50 cm EASY‐Spray column.
• Fragmentation Modes: HCD on Orbitrap or ion trap, conventional CID, electron‐transfer/higher‐energy collision dissociation (EThcD), and multistage activation CID (MSA).
• Data Analysis: Proteome Discoverer 2.1 with Sequest HT, Byonic, Mascot nodes; MaxQuant 1.5.3.51 using Andromeda. Fixed mods: carbamidomethylation, TMT; variable: oxidation, deamidation, phosphorylation. MS1 tolerance 10 ppm; MS2 0.02 Da (Orbitrap) or 0.6 Da (ion trap). FDR 1% at peptide/protein level; ptmRS>90% for confident site localization. Results compared in ProteinCenter.

Main Results and Discussion

HCD produced the highest phosphopeptide and site identifications across all engines. Byonic yielded the largest number of confident sites, followed by Sequest HT and MaxQuant; Mascot showed comparable performance. CID‐MSA on the Orbitrap enhanced overlap with HCD identifications compared to standard CID. EThcD improved site localization confidence but delivered fewer IDs due to slower acquisition. TMT quantitation accuracy was equivalent across engines, and SPS‐MS3 effectively removed yeast interference, restoring the expected 8:1 ratio (log₂≈3).

Benefits and Practical Applications

• Improved mapping of signaling networks through expanded phosphosite coverage.
• Optimized fragmentation and search strategies boost throughput in multiplexed workflows.
• SPS‐MS3 and CID‐MSA approaches yield reliable quantitation in complex biological samples.

Future Trends and Potential Uses

• Development of real‐time, neutral‐loss triggered MS3 and enhanced search algorithms for greater depth.
• Hybrid fragmentation schemes and machine‐learning–driven scoring to refine PTM discovery.
• Extension of these strategies to other post‐translational modifications and network analyses.

Conclusion

The comparative assessment highlights the influence of fragmentation mode and search engine on phosphoproteomic depth and quantitation fidelity. Combining HCD and advanced CID‐MSA with tailored search workflows provides balanced identification and quantitation performance in TMT‐based studies.

Reference

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