[The sequence coverage in different methods of mass spectrometry data analysis obtained on model proteins]

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Shotgun proteomics is crucial for protein identification and characterization.
• Optimizing data analysis and hydrolysis methods is essential for maximizing sequence coverage.
• Accurate protein sequence analysis aids in understanding protein function and variations.

Purpose of the Study:

• To evaluate sequence coverage of five model proteins using different shotgun proteomic data analysis methods.
• To compare the performance of database-dependent search engines and de novo sequencing software.
• To assess the impact of multiprotease hydrolysis on protein sequence coverage.

Main Methods:

• Shotgun proteomic data analysis of five model proteins (CYB5A, SMAD4, RAB27B, FECH, CXXC1).
• Utilized database-dependent search engines (MASCOT, X!Tandem) and de novo sequencing software (PEAKS, Novor, PepNovo+).
• Performed multiprotease hydrolysis (trypsin, LYS-C, ASPN, GluC) via in-solution and FASP methods, followed by high-resolution mass spectrometry (Q EXACTIVE HF).

Main Results:

• Database-dependent and de novo sequencing methods yielded similar results when controlling for false peptide identification.
• Sequence coverage varied, reaching 80-90% in optimal cases, exceeding the 40% threshold.
• De novo sequencing software identified a Y12H amino acid substitution in the CYB5A protein.

Conclusions:

• Both database-dependent and de novo sequencing approaches are effective for shotgun proteomic analysis.
• Multiprotease hydrolysis and advanced mass spectrometry enhance protein sequence coverage.
• De novo sequencing offers potential for identifying novel sequence variations.