NetPhos

NetPhos predicts phosphorylation sites on serine, threonine, and tyrosine residues in eukaryotic proteins to support analysis of protein regulation by phosphorylation.

Key Features:

• Phosphorylation site prediction: Predicts phosphorylation on serine, threonine, and tyrosine residues in eukaryotic proteins.
• Neural network implementation: Uses artificial neural network methodologies for prediction.
• Reported sensitivity: Demonstrates sensitivity ranging from 69% to 96% depending on context and dataset.
• O-GlcNAc glycosylation prediction: Identifies serine and threonine residues that may undergo O-linked glycosylation with N-acetylglucosamine (O-GlcNAc).
• Yin-yang regulation indication: Indicates potential reciprocal regulation where O-GlcNAc glycosylation can inhibit phosphorylation at specific sites.
• Application to specific proteins: Has been applied to predict novel phosphorylation sites in p300/CBP that may influence transcription factor interactions and histone acetyltransferase activity.
• Complementarity to experiments: Identifies candidate phosphorylation sites that may not be evident through experimental methods alone.

Scientific Applications:

• Signaling pathway analysis: Use predictions to investigate regulatory phosphorylation events in cellular signaling pathways.
• Functional hypothesis generation: Predict novel phosphorylation sites in proteins such as p300/CBP to generate hypotheses about effects on protein–protein interactions and enzymatic activity.
• Post-translational modification crosstalk: Enable analysis of yin-yang regulation between phosphorylation and O-GlcNAc glycosylation.
• Experimental prioritization: Prioritize candidate sites for experimental validation when direct evidence is lacking.

Methodology:

Predictions are generated using artificial neural network methodologies.