An AI-Ready Phosphorylation Meta-Analysis for Saccharomyces cerevisiae.

Area of Science:

• Proteomics
• Yeast Biology
• Post-Translational Modifications

Background:

• Phosphorylation is a key post-translational modification (PTM) regulating cellular processes.
• Accurate mapping of phosphosites is crucial for understanding kinase signaling networks.
• High-quality, accessible data is essential for advancing artificial intelligence (AI) in proteomics.

Purpose of the Study:

• To create a comprehensive map of phosphosites in Saccharomyces cerevisiae.
• To analyze the structural context and sequence motifs of identified phosphosites.
• To ensure data quality and accessibility for AI-driven proteomics research.

Main Methods:

• Meta-analysis of eight high-quality datasets, identifying 56,694 phosphosites with strict false discovery rate control.
• Classification of phosphosites into Gold, Silver, and Bronze confidence categories.
• Utilized disorder region predictions and AlphaFold 3 for structural context analysis of phosphosites.

Main Results:

• Identified 56,694 phosphosites in Saccharomyces cerevisiae, classified by confidence.
• Discovered 55 significant phosphorylation motifs, grouped into kinase classes for pathway enrichment.
• Phosphorylation predominantly occurs on disordered serine and threonine residues, potentially inducing short alpha helices.

Conclusions:

• The study provides a high-confidence, comprehensive phosphosite map of Saccharomyces cerevisiae.
• Phosphorylation sites are frequently located in disordered regions and influence protein structure.
• The FAIR and AI-ready dataset (PRIDE: PXD071918) supports downstream analyses and AI model training in proteomics.