DDASSQ: An open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS...

Area of Science:

• Proteomics
• Computational Biology
• Biotechnology

Background:

• Accurate protein identification and quantification are crucial for understanding biological processes.
• Label-free quantification (LFQ) using LC-MS/MS data is a common method in proteomics.
• Evaluating computational pipelines is essential for reliable proteomic data analysis.

Purpose of the Study:

• To assess the performance of an open-source computational pipeline for protein identification and LFQ.
• To investigate the impact of different peptide search strategies on proteomic data analysis.
• To compare the pipeline's LFQ results with established software like MaxQuant and Proteome Discoverer.

Main Methods:

• Utilized the OpenMS software within the KNIME analytics platform.
• Integrated various peptide search engines including X!Tandem, MS-GF+, Novor, and SpectraST.
• Processed LC-MS/MS data from mouse ex vivo liver samples for proteomic analysis.

Main Results:

• The study compared different in silico digestion, database search, and de novo sequencing approaches.
• Performance of the pipeline was evaluated against MaxQuant and Proteome Discoverer for protein inference and LFQ.
• The combinatorial approach's impact on LFQ results was analyzed.

Conclusions:

• The developed computational pipeline offers a flexible framework for proteomic data analysis.
• Understanding the influence of peptide search strategies is key to optimizing LFQ accuracy.
• The findings provide insights into selecting appropriate tools for shotgun proteomics workflows.