SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets

Area of Science:

• Biochemistry
• Molecular Biology
• Bioinformatics

Background:

• Phospho-proteomics studies offer insights into cellular signaling dynamics but often lack mechanistic detail on kinase/substrate relationships.
• Existing tools rely on prior knowledge, limiting discovery of novel or condition-specific signaling pathways.
• Understanding cell-specific signaling networks is crucial for deciphering complex biological processes.

Purpose of the Study:

• To introduce SELPHI, a user-friendly web-based tool for in-depth phospho-proteomics data analysis.
• To enable the extraction of kinase/phosphatase and phospho-peptide associations from phospho-proteomics data.
• To highlight potential signaling flow within biological systems, aiding in the understanding of network wiring.

Main Methods:

• SELPHI utilizes correlation analysis of phospho-sites to identify kinase/phosphatase and phospho-peptide associations.
• The tool provides an intuitive interface accessible to non-bioinformatics experts.
• Analysis is demonstrated using phospho-proteomics data from cancer cells treated with erlotinib, a tyrosine kinase inhibitor.

Main Results:

• SELPHI successfully identified kinase/substrate relationships and potential signaling pathways.
• Analysis of erlotinib-treated cancer cells revealed previously overlooked information, including the roles of MET and EPHA2 kinases in erlotinib resistance.
• The tool demonstrated its capability to uncover condition-specific signaling events.

Conclusions:

• SELPHI significantly enhances the analysis of phospho-proteomics data, facilitating a deeper understanding of sample-specific signaling networks.
• The tool aids in discovering novel kinase-substrate interactions and signaling pathways.
• SELPHI contributes to improved comprehension of cellular signaling mechanisms, particularly in the context of drug resistance.