pepgrep: A tool for peptide MS/MS pattern matching

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Protein identification in tandem mass spectrometry (MS2) typically relies on protein database searching (PDS).
• Manual spectrum evaluation is often needed to confirm peptide sequences, even with PDS.
• Existing methods can struggle with identifying peptides in complex or low-abundance samples.

Purpose of the Study:

• To develop a database-free tool for identifying specific peptide sequences in collision-induced dissociation (CID) tandem MS2 datasets.
• To enhance the detection of peptide fingerprints, including those with post-translational modifications (PTMs).
• To provide a more efficient method for analyzing complex proteomic samples.

Main Methods:

• Developed 'pepgrep', a command-line utility for pattern-matching peptide sequences against modelled MS2 data.
• Utilized a Peptide-to-MS2 scoring algorithm incorporating mass offsets for various PTMs.
• Employed decoy peptide sequences to minimize false-positive results.
• Enabled high-throughput screening using cluster computing environments.

Main Results:

• Successfully identified additional peptide sequences in CID spectra that were missed by traditional PDS.
• Demonstrated pepgrep's utility in analyzing CID spectra of small peptide fractions from techniques like affinity purification.
• Showcased the ability to incorporate numerous PTMs into the search algorithm.

Conclusions:

• pepgrep offers a powerful, database-free alternative for targeted peptide identification in MS2 data.
• The tool is particularly effective for complex samples where PDS may fail.
• pepgrep facilitates the discovery of previously unidentified peptide sequences and PTMs.