Robust inference of kinase activity using functional networks.
Nat Commun
; 12(1): 1177, 2021 02 19.
Article
in En
| MEDLINE
| ID: mdl-33608514
ABSTRACT
Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer's disease and Parkinson's disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http//rokai.io .
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Phosphotransferases
/
Signal Transduction
/
Computational Biology
/
Metabolic Networks and Pathways
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2021
Document type:
Article
Affiliation country:
United States