inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Rescoring mass spectrometry (MS) search results with spectral predictors improves peptide spectrum match (PSM) identification rates.
• This is crucial for large-scale MS data analysis, including immunopeptidomics and noncanonical peptide identification.

Purpose of the Study:

• To introduce inSPIRE (in silico Spectral Predictor Informed REscoring), an open-source rescoring pipeline.
• To enhance PSM identification rates and sensitivity in MS data analysis, particularly for challenging datasets.

Main Methods:

• Developed inSPIRE, a flexible and performant rescoring pipeline based on Prosit MS spectral prediction.
• Ensured compatibility with common database search engines for large-scale rescoring.
• Applied inSPIRE to various MS sample types, including immunopeptidomes and tryptic proteome digestions.

Main Results:

• inSPIRE significantly boosts PSM identification rates in immunopeptidomics.
• Demonstrated superior performance compared to the original Prosit rescoring pipeline via benchmarking on ground truth datasets.
• Showcased increased sensitivity to minor amino acid residue positional differences.

Conclusions:

• inSPIRE offers a powerful and flexible solution for rescoring MS data, improving identification rates.
• The pipeline enhances immunopeptidomics and has potential for noncanonical peptide identification.
• inSPIRE's integration of features further boosts PSM identification, especially in complex MS samples.