PhoScan
PhoScan predicts kinase-specific phosphorylation sites in protein sequences using a log-odds ratio scoring approach to identify likely kinase–substrate relationships.
Key Features:
- Log-odds ratio scoring: Uses a log-odds ratio framework to score the likelihood that a peptide is phosphorylated by a specific protein kinase at a given site.
- Feature extraction: Extracts common and kinase-specific sequence features from substrate sequences associated with different protein kinases.
- Experimental-data-derived features: Derives features from analysis of published experimental phosphorylation data.
- Predictive performance: Reports specificity and sensitivity rates above 90% at the kinase-family level based on experimental data.
- Input data source: Applied to predict phosphorylation sites in human proteins sourced from the Swiss-Prot database.
- Kinase families supported: Includes predictions for protein kinase A, cyclin-dependent kinases, and casein kinase 2.
Scientific Applications:
- Phosphorylation site prediction: Predicts putative phosphorylation sites in human proteins from Swiss-Prot.
- Kinase–substrate assignment: Assigns candidate kinases to phosphorylation sites for protein kinase A, cyclin-dependent kinases, and casein kinase 2.
- Phosphorylation dynamics and function studies: Supports investigation of protein function and phosphorylation-mediated signaling events.
Methodology:
Extracts common and kinase-specific features from substrate sequences associated with different protein kinases using published experimental data, and implements a log-odds ratio scoring system to evaluate the likelihood that a peptide is phosphorylated by a specific protein kinase at a particular site within its sequence context.
Topics
Details
- Tool Type:
- web application
- Operating Systems:
- Linux, Windows, Mac
- Added:
- 8/3/2017
- Last Updated:
- 11/25/2024
Operations
Publications
Li T, Li F, Zhang X. Prediction of kinase‐specific phosphorylation sites with sequence features by a log‐odds ratio approach. Proteins: Structure, Function, and Bioinformatics. 2007;70(2):404-414. doi:10.1002/prot.21563. PMID:17680694.