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

Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spec-trum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identifi-cation. Here, we present inSPIRE (in silica Spectral Pre-dictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spec-tral prediction, which is compatible with common data-base search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, in-creases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immu-nopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.

Automated summary

This article introduces a method of rescoring mass spectrometry search results using spectral predictors, which can increase the identification rate of peptide spectrum matches. This method is particularly effective in immunopeptidomics and has potential benefits for the identification of noncanonical peptides.