Virtual polymorphism: finding divergent peptide matches in mass spectrometry data.

Area of Science:

• Proteomics
• Bioinformatics
• Mycology

Background:

• Tandem mass spectrometry (MS/MS) is crucial for protein identification.
• Standard methods struggle with divergent or unsequenced species, like the fungal pathogen Alternaria alternata.
• Existing methods for divergent species, such as de novo sequencing, are often laborious.

Purpose of the Study:

• To develop an improved method for identifying proteins from species with significant sequence divergence and absent genomes.
• To overcome limitations of current protein identification techniques in unsequenced fungal species.

Main Methods:

• Developed a novel database engineering approach using biologically rational polymorphism prediction.
• Utilized statistically weighted amino acid substitution data (BLOSUM62) for sequence comparison.
• Enabled consideration of multiple amino acid changes per peptide, improving sequence matching.

Main Results:

• Demonstrated considerable improvement in routine protein identification for divergent species.
• Showcased the potential to rescue unconvincing identifications that would otherwise be dismissed.
• Successfully addressed challenges posed by Alternaria alternata protein identification.

Conclusions:

• The developed database engineering strategy enhances protein identification in evolutionarily distant or unsequenced organisms.
• This method offers a more controlled and flexible alternative to existing techniques for challenging proteomic analyses.
• Facilitates more accurate and comprehensive proteomic characterization of fungal pathogens and other under-represented species.