Optimizing Spectronaut Search Parameters to Improve Data Quality with Minimal Proteome Coverage Reductions in DIA...

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Data-independent acquisition (DIA) enables comprehensive proteome profiling.
• Increasing proteome coverage necessitates higher data quality and accurate identification.
• Minimizing false positives is crucial for reliable proteomic analysis.

Purpose of the Study:

• To optimize Spectronaut search parameters for minimizing false positives in DIA.
• To evaluate the impact of parameter optimization on data quality and true positive rates.
• To demonstrate the utility of optimized parameters in a complex biological system.

Main Methods:

• Utilized Spectronaut software for proteomic data analysis.
• Employed an immunological infection model with *Mus musculus* macrophages and *Candida albicans*.
• Compared proteome profiles before and after parameter optimization.

Main Results:

• Adjusting search parameters reduced false positive identifications by 89% at peptide and protein levels.
• Optimized parameters decreased true positive identifications by less than 6.7%.
• Demonstrated significant improvement in data quality for heterogeneous samples.

Conclusions:

• Optimized Spectronaut parameters enhance the reliability of DIA proteomic analyses.
• These findings are valuable for studies involving mixed cell populations or tissues.
• Parameter optimization is a critical step for high-quality proteome profiling.