DiagnoTop: A Computational Pipeline for Discriminating Bacterial Pathogens without Database Search

Area of Science:

• Microbiology
• Proteomics
• Computational Biology

Background:

• Accurate pathogen identification is vital for effective antimicrobial therapy.
• Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is a standard for microbial identification, but struggles with closely related species.
• Top-down proteomics (TDP) shows promise for differentiating difficult-to-identify bacteria, but current methods rely on extensive database searches of proteoforms, which are often incomplete.

Purpose of the Study:

• To develop a novel computational pipeline (DiagnoTop) for microbial diagnostics using TDP data.
• To enable pathogen identification without the need for database searches of proteoforms.
• To improve the diagnostic power of TDP for closely related bacterial species.

Main Methods:

• Development of the DiagnoTop computational pipeline.
• Application of DiagnoTop to top-down proteomics datasets of enterobacterial pathogens.
• Analysis of spectral clusters to identify discriminative patterns without database reliance.

Main Results:

• DiagnoTop successfully identified discriminative spectral clusters within TDP data.
• The pipeline enabled efficient shortlisting of high-quality spectral clusters for diagnostic purposes.
• Increased diagnostic power was achieved for enterobacterial pathogens like Escherichia coli, Shigella, and Salmonella.

Conclusions:

• DiagnoTop offers a robust, database-independent approach for microbial diagnostics using TDP.
• This computational tool enhances the clinical utility of TDP for identifying challenging bacterial pathogens.
• The pipeline holds potential for advancing clinical microbiology and biomarker discovery.