An Automated Workflow to Address Proteome Complexity and the Large Search Space Problem in Proteomics and HLA-I...

Area of Science:

• Proteogenomics
• Mass Spectrometry
• Immunopeptidomics

Background:

• Accurate proteogenomic search spaces are crucial for novel protein and antigenic peptide discovery via mass spectrometry.
• The size and characteristics of these search spaces, especially for noncanonical peptides, are not well-defined, impacting identification sensitivity.

Purpose of the Study:

• To develop an automated workflow for creating and refining sequence search spaces for mass spectrometry-based proteogenomic discovery.
• To characterize the impact of RNA sequencing, post-translational modifications, and noncanonical origins on search space size and peptide identification.

Main Methods:

• Developed an automated workflow integrating Sequoia for RNA-informed search space generation and SPIsnake for pre-filtering.
• Applied the workflow to analyze tryptic and nonspecific peptide search spaces, including effects of RNA expression and post-translational modifications.
• Evaluated the workflow's performance on HLA-I immunopeptidomes to assess peptide identification sensitivity.

Main Results:

• Quantified the exact sizes of various peptide sequence search spaces and their reduction with RNA expression data.
• Demonstrated how post-translational modifications inflate search spaces and analyzed peptide sequence multimapping frequencies.
• Showcased improved peptide identification sensitivity on HLA-I immunopeptidomes using the developed workflow.

Conclusions:

• The Sequoia and SPIsnake workflow enables robust characterization of proteogenomic search spaces, crucial for large-scale discovery.
• This approach addresses challenges posed by database size inflation and peptide/protein identification ambiguity.
• Facilitates the development of improved methods for discovering novel therapeutic targets and understanding antigen presentation.