Sherenga

Sherenga performs de novo interpretation of tandem mass spectrometry (MS/MS) spectra to infer peptide sequences for proteomics.

Key Features:

• Automatic learning: Learns fragment ion types and intensity thresholds from collections of test spectra to adapt to data from different mass spectrometers.
• Graph-based interpretation: Constructs graph representations of MS/MS spectra and uses optimal path scoring to identify candidate peptide sequences.
• Ranked candidate generation: Produces ranked lists of high-scoring paths that correspond to possible peptide sequences derived from the spectra.
• Unknown-protein sequencing: Interprets peptide sequences originating from unknown or uncharacterized proteins without reliance on sequence databases.
• Validation of database results: Validates results from database search algorithms to support automated, high-throughput peptide sequencing workflows.

Scientific Applications:

• Novel protein discovery: Enables identification of new protein sequences by de novo peptide sequencing from MS/MS data.
• Analysis of uncharacterized proteins: Supports proteomic analysis when database entries are absent or incomplete.
• Validation of identifications: Provides independent verification of peptide identifications obtained from database search algorithms.
• High-throughput proteomics: Integrates into automated, high-throughput sequencing workflows for large-scale proteomic studies.

Methodology:

Learns fragment ion types and intensity thresholds from test spectra, constructs graph representations of MS/MS spectra, applies optimal path scoring to generate ranked lists of high-scoring paths as candidate peptide sequences, and performs validation against database search results.