NetPhosYeast

NetPhosYeast predicts serine and threonine phosphorylation sites in yeast proteins, including Saccharomyces cerevisiae, to identify yeast-specific post-translational modification sites.

Key Features:

• Residue specificity: Predicts phosphorylation on serine and threonine residues.
• Organism focus: Targets yeast proteins, including Saccharomyces cerevisiae.
• Neural network-based methodology: Employs a neural network approach for phosphorylation site prediction.
• Performance metrics: Demonstrates sensitivity of 0.84 and specificity of 0.90.
• Correlation coefficient: Achieves a correlation coefficient of 0.75.
• Comparative performance: Outperforms predictors trained on mammalian datasets when applied to yeast data.
• Training data: Trained on yeast phosphorylation data to capture yeast-specific sequence characteristics.

Scientific Applications:

• PTM mapping: Facilitates identification of phosphorylation sites as post-translational modifications in yeast proteins.
• Signal transduction and regulation: Supports analysis of phosphorylation-related signal transduction, cell cycle regulation, and metabolic control in yeast.
• Research and biotechnology: Enables yeast-focused fundamental research and biotechnological investigations involving phosphorylation.

Methodology:

Neural network trained on yeast phosphorylation data to predict serine and threonine phosphorylation sites.