Less missing values-evaluation of proteomics workflows for the quantification of (small) proteins

Area of Science:

• Proteomics
• Molecular Biology
• Pathogen Research

Background:

• Quantitative proteomic data quality relies on minimizing missing values.
• Small proteins (≤100 amino acids) present unique challenges in identification and quantification.
• Understanding Clostridioides difficile proteome is vital for pathogen research.

Purpose of the Study:

• To systematically evaluate sample preparation and MS-data processing methods for small protein analysis.
• To improve the identification and quantification of small proteins in Clostridioides difficile.
• To establish a comprehensive proteomic dataset for C. difficile.

Main Methods:

• Small protein enrichment techniques were applied.
• Mass spectrometry (MS) data processing strategies were optimized.
• Spectral libraries were utilized for MS spectra identification.

Main Results:

• Small protein enrichment significantly increased identified and quantified proteins, including low-abundance ones.
• Spectral library application enhanced robust quantification and lowered detection limits for small proteins.
• The study generated a comprehensive C. difficile proteome dataset, covering 84.7% of predicted proteins and 61.4% of small proteins.

Conclusions:

• Optimized sample preparation and MS data processing are effective for small protein quantification.
• This work provides a valuable resource for C. difficile research and understanding pathogen biology.
• The developed methods can be applied to improve small protein analysis in other complex proteomes.