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A Data-Driven Signaling Network Inference Approach for Phosphoproteomics.

Imani Madison1, Fin Amin2, Kuncheng Song3

  • 1Department of Plant and Microbial Biology and NC Plant Sciences Initiative, North Carolina State University, Raleigh, NC, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a computational pipeline to analyze dynamic phosphoproteomic data, enabling the prediction of protein regulatory networks over time. It helps understand cell signaling cascades by quantifying kinase and phosphatase interactions.

Keywords:
Bayesian modelingKinase regulatory networksLabel-free phosphoproteomicsPost-translational modifications

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Area of Science:

  • Biochemistry and Molecular Biology
  • Systems Biology
  • Computational Biology

Background:

  • Cellular responses to environmental changes involve rapid post-translational protein modifications.
  • Protein phosphorylation and dephosphorylation, mediated by kinases and phosphatases, are key regulatory mechanisms in cell signaling.

Purpose of the Study:

  • To develop and present methods for statistically analyzing label-free phosphoproteomic data over time.
  • To infer dynamic post-transcriptional regulatory networks, focusing on kinase-phosphatase-target protein interactions.

Main Methods:

  • Utilized an R-based method for normalizing label-free phosphoproteomic data using variance stabilizing normalization.
  • Employed a linear mixed model for analyzing data across multiple time points and conditions.
  • Applied a discretization scheme followed by dynamic Bayesian modeling to infer regulator-target interactions.

Main Results:

  • The developed pipeline enables robust statistical analysis of time-resolved phosphoproteomic datasets.
  • Successfully inferred dynamic regulatory interactions within signaling cascades.
  • Validated the computational predictions through dynamic Bayesian modeling.

Conclusions:

  • The presented pipeline offers a comprehensive approach for functional analysis and prediction of phosphoproteomic signaling cascades.
  • Facilitates a deeper understanding of dynamic cellular responses and regulatory networks.
  • A valuable tool for researchers studying cell signaling and post-translational modifications.