KEA

KEA identifies kinases whose substrates are enriched in input lists of proteins or genes to infer kinase involvement in signaling pathways in mammalian cells.

Key Features:

• Integration with kinase-substrate databases: Leverages multiple existing kinase-substrate databases to map input proteins or genes to known kinase interactions.
• Kinase enrichment probability calculation: Computes enrichment probability by comparing the distribution of kinase-substrate proportions within a background database to those associated with an input list.
• Ranking and hypothesis generation: Ranks kinases and kinase families based on deviations from expected distributions to prioritize candidates for functional relevance.

Scientific Applications:

• Kinase identification: Identify kinases potentially responsible for observed changes in protein or gene lists derived from experiments.
• Therapeutic target exploration: Link altered gene or protein expression profiles to candidate kinase targets for downstream experimental validation.
• Regulatory network investigation: Infer kinase-mediated regulatory relationships that may underlie cellular homeostasis or disease-associated phenotypes.

Methodology:

Maps input protein or gene lists to entries in multiple kinase-substrate databases; calculates enrichment probabilities by comparing kinase-substrate proportion distributions between the background database and the input list; ranks kinases and kinase families by deviation from expected distributions.