Updated MS²PIP web server supports cutting-edge proteomics applications

Area of Science:

• Proteomics
• Computational Biology
• Bioinformatics

Background:

• Machine learning for peptide fragmentation spectrum prediction is increasingly vital for proteomics.
• The MS²PIP tool has been a valuable resource for various downstream applications due to its accuracy and ease of use.

Purpose of the Study:

• To present a significantly updated version of the MS²PIP web server.
• To enhance prediction models and introduce new functionalities for spectral library generation and data analysis.

Main Methods:

• Developed new, more performant prediction models for tryptic, non-tryptic, and immunopeptides, as well as CID-fragmented TMT-labeled peptides.
• Integrated retention time predictions using DeepLC for generated spectral libraries.
• Added functionality for proteome-wide spectral library generation from FASTA files and provided pre-built libraries for model organisms.

Main Results:

• The updated MS²PIP server features enhanced prediction models and expanded capabilities.
• New functionalities facilitate the creation of comprehensive spectral libraries, including retention time predictions.
• Pre-built spectral libraries for various organisms are now available in DIA-compatible formats.

Conclusions:

• The enhanced MS²PIP web server improves peptide spectrum prediction accuracy and applicability.
• The tool now supports challenging proteomics workflows like immunopeptidomics and TMT quantification.
• MS²PIP continues to be a freely accessible and valuable resource for the proteomics community.