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Updated: Apr 5, 2026
Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.
Pengyi Yang1, Xiaofeng Zheng2, Vivek Jayaswal3
1Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, North Carolina, United States of America; Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, North Carolina, United States of America.
This study introduces a novel CLUE method to analyze temporal phosphoproteomics data. It identifies key kinases driving cell signaling pathways by clustering phosphorylation sites using prior knowledge.
Area of Science:
- Molecular Biology
- Systems Biology
- Bioinformatics
Background:
- Cell signaling orchestrates cell-fate decisions through transcription and epigenetic control.
- Identifying kinases involved in signaling pathways is crucial for understanding cellular processes.
- Phosphoproteomics quantifies phosphorylation sites (substrates) to reveal kinase activity dynamics.
Purpose of the Study:
- To develop a knowledge-based approach for identifying informative partitioning of temporal phosphoproteomics data.
- To facilitate the identification of key kinases regulating signaling cascades.
- To characterize signaling networks from phosphoproteomics data.
Main Methods:
- Developed the CLUster Evaluation (CLUE) approach, a knowledge-based method for data partitioning.
- Utilized prior knowledge of kinase-substrate relationships from literature and databases.
- Applied the CLUE approach to two time-series phosphoproteomics datasets.
Main Results:
- Generated biologically meaningful partitioning of phosphorylation sites based on temporal kinetics.
- Identified key kinases associated with specific clusters of phosphorylation sites.
- Successfully applied to human embryonic stem cell differentiation and insulin signaling pathways.
Conclusions:
- The CLUE approach effectively identifies key kinases from temporal phosphoproteomics data.
- This method provides a valuable resource for characterizing complex signaling networks.
- Enables deeper understanding of cell signaling in biological processes like differentiation and metabolic regulation.
