ProtGraph: a tool for the quick and comprehensive exploration and exploitation of the peptide search space derived...

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry-based proteomics often uses limited peptide sequences due to computational constraints.
• Existing methods struggle to incorporate diverse protein variations and annotations into spectral matching.

Purpose of the Study:

• To develop a novel graph-based approach for comprehensive peptide search space characterization in proteomics.
• To enable the inclusion of canonical sequences, isoforms, and annotated amino acid variations.

Main Methods:

• Representing proteins as graphs to encode sequence information.
• Implementing a C++ and Python tool to manage and query an expanded peptide search space.
• Utilizing Uniprot KB and other protein databases for annotation integration.

Main Results:

• The approach characterizes a vastly expanded peptide search space, exceeding $10^{200}$ possibilities.
• Efficient extraction of relevant peptide subsets for targeted analysis, such as filtering by peptide mass.
• Demonstrated advantages and novel findings through re-analysis of public datasets.

Conclusions:

• This graph-based method significantly enhances the comprehensiveness of peptide search spaces in proteomics.
• The implementation offers a powerful tool for overcoming computational limitations and improving spectral matching accuracy.
• Enables deeper insights into proteomic data by incorporating a wider range of biological variations.