Monitoring Functional Posttranslational Modifications Using a Data-Driven Proteome Informatic Pipeline

Area of Science:

• Molecular Biomedicine
• Proteomics
• Bioinformatics

Background:

• Posttranslational modifications (PTMs) are critical for cellular signaling but challenging to characterize.
• Current methods for PTM analysis are often labor-intensive and require specific experimental designs.
• Understanding PTMs, their functions, and crosstalk is vital in molecular biology.

Purpose of the Study:

• To develop a PTM-centric computational pipeline for predicting relevant PTMs directly from mass spectrometry proteomics data.
• To enable the identification and quantification of PTMs without prior experimental information.
• To demonstrate the pipeline's utility in glycoproteomics and drug-treated cancer cells.

Main Methods:

• Developed a PTM-centric informatic pipeline for in silico PTM prediction.
• Applied the pipeline to mass spectrometry-based proteomics data from oral squamous cell carcinoma.
• Utilized computational enrichment analysis to identify potential PTMs in differentially expressed proteins.
• Performed refined database searches incorporating predicted PTMs.

Main Results:

• The pipeline successfully predicted and analyzed enriched PTMs in proteomics data.
• Identified proteins not detected in initial searches, demonstrating enhanced discovery capabilities.
• Showcased the pipeline's application in glycoproteomics and analysis of drug-induced protein changes.
• Validated the effectiveness of PTM-centric searching with computational enrichment.

Conclusions:

• PTM-centric searching of mass spectrometry data, aided by computational enrichment analysis, is effective.
• The proposed pipeline significantly improves the identification of PTMs and associated proteins.
• Integrating this computational approach into future proteomics search engines is recommended.