Optimized Search Strategy to Maximize PTM Characterization and Protein Coverage in Proteome Discoverer Software

Significance of the Topic

Comprehensive and accurate interpretation of tandem mass spectrometry data underpins advanced proteomics studies. The choice and configuration of database search algorithms directly affects peptide and protein identifications, post-translational modification (PTM) mapping, and ultimately biological conclusions. Optimizing search strategies is essential for maximizing coverage, minimizing false assignments, and reducing processing time in high-resolution proteome analyses.

Objectives and Study Overview

This work systematically compares four search engines—SEQUEST, Mascot, Byonic, MS Amanda (all in Proteome Discoverer 2.0) and the Andromeda engine in MaxQuant—using two representative datasets: a simple HeLa digest and a complex histone mixture. Search parameters and filters were standardized to assess each engine’s peptide/protein identifications, PTM localization capability, and overall processing time. The goal was to define an optimal strategy for diverse sample types and PTM analyses.

Methodology and Instrumentation

• Sample Preparation: Thermo Scientific Pierce HeLa Protein Digest Standard (200 ng) for global proteome profiling; extracted histones (1 µg) subjected to propionylation and multiple PTM enrichment schemes.
• Chromatography and Acquisition: Q Exactive Plus coupled to EASY-nLC 1000 with 50 cm EASY-Spray column in top-10 data-dependent mode (70 K MS1, 17.5 K MS2); Orbitrap Fusion Tribrid with 120 K resolving power MS1 and rapid ion-trap MS2 (150 ms max injection).
• Data Processing: Uniform FASTA database; 1 % peptide false discovery rate (PSM FDR in MaxQuant) and 2 % protein FDR for select engines; PTM site localization via ptmRS; all searches on a 2.9 GHz PC with 16 GB RAM (Mascot server with 24 GB RAM).

Used Instrumentation

• Thermo Scientific Q Exactive Plus mass spectrometer
• Thermo Scientific EASY-nLC 1000 UHPLC with EASY-Spray column
• Thermo Scientific Orbitrap Fusion Tribrid mass spectrometer
• Proteome Discoverer 2.0 software
• MaxQuant 1.5.2.8 with Andromeda engine
• Thermo Scientific ProteinCenter for result integration

Key Results and Discussion

• HeLa Digest: Byonic with protein-aware (2D) FDR outperformed other engines by identifying ~7 000 more peptides and yielding higher protein coverage; ~65 % of Byonic’s unique peptides were confirmed in a fractionated reference dataset.
• FDR Filtering: Applying peptide 1D FDR reduced sensitivity, whereas protein-aware filtering maintained a favorable balance between identification breadth and mass-error accuracy.
• Search Speed: SEQUEST HT (multi-threaded) was fastest (~21 min), followed by Byonic (~34 min), Mascot (~36 min), and MS Amanda/MaxQuant (73–88 min) for a 2 h gradient HeLa run.
• Histone PTM Analysis: Byonic led in modified peptide identifications, especially methylated forms; Mascot and MaxQuant provided complementary coverage. Combined engine workflows within Proteome Discoverer boosted PTM discovery.
• Parallel Searching: Deploying SEQUEST HT, Mascot, and Byonic in parallel reduced total PTM search time from 17 h to 14 h for seven modification schemes.

Benefits and Practical Applications

• Enhanced sensitivity and confidence in PTM mapping accelerate biomarker discovery and mechanistic studies in disease and epigenetics.
• Parallel engine integration in Proteome Discoverer enables comprehensive coverage without linear increases in processing time.
• Optimized FDR strategies improve identification accuracy while preserving depth of coverage in high-resolution datasets.

Future Trends and Opportunities

• Development of AI-driven scoring algorithms to further refine PTM localization and reduce false positives.
• Cloud-based parallelization and GPU acceleration for faster large-scale proteome searches.
• Integration of multi-omic databases and machine learning for predictive PTM discovery.
• Standardized benchmarking frameworks to ensure reproducibility across platforms and laboratories.

Conclusion

Search engine selection and FDR strategy profoundly influence proteome identification and PTM characterization. SEQUEST HT offers rapid overview analyses, while Byonic—with protein-aware FDR—yields maximal sensitivity, particularly for complex PTM schemes. Combining multiple search platforms within Proteome Discoverer 2.0 achieves comprehensive coverage with efficient processing times, supporting advanced proteomic applications.

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