KSP

KSP predicts the specific kinases that catalyze phosphorylation events at identified sites within human proteins to prioritize kinase–substrate relationships for phosphoproteomic analysis.

Key Features:

• Kinase prediction: Predicts specific kinases responsible for catalyzing phosphorylation events at identified sites within human proteins.
• Network model: Constructs a network model based on known protein-protein interactions and established kinase-substrate relationships.
• Affinity scoring: Calculates an affinity score between each phosphorylation site and potential kinases.
• Ranking and prioritization: Ranks candidate kinases by affinity score to prioritize the most likely catalyzing kinases.
• Data integration: Integrates network-based analyses with sequence data.
• Comparative evaluation: Demonstrates superior performance in predicting known kinase-substrate pairs versus NetworKIN, iGPS, and PKIS.

Scientific Applications:

• Protein function elucidation: Links phosphorylation sites to kinases to aid elucidation of protein function.
• Phosphoproteomic data interpretation: Assigns candidate kinases to sites identified by high-throughput phosphoproteomic technologies.
• Signal transduction and tumorigenesis research: Investigates roles of phosphorylation in signal transduction and tumorigenesis.
• Drug discovery: Prioritizes kinase targets for drug discovery efforts targeting aberrant phosphorylation pathways.
• Experimental validation prioritization: Provides ranked candidate kinases to guide experimental validation of kinase-substrate relationships.

Methodology:

Constructs a network from known protein-protein interactions and kinase-substrate relationships, calculates affinity scores between phosphorylation sites and potential kinases, ranks candidate kinases, and integrates network-based analyses with sequence data.