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Updated: May 10, 2026
07:57
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Published on: August 21, 2019
Predicting protein interactions via parsimonious network history inference
1Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. robp@cs.cmu.edu
Bioinformatics (Oxford, England)
|July 2, 2013
Summary
This study introduces a new computational framework to reconstruct the evolutionary history of protein-protein interactions. The method accurately infers ancestral interactions and protein duplication orders, improving upon existing approaches.
Area of Science:
- Computational Biology
- Evolutionary Biology
- Bioinformatics
Background:
- Reconstructing evolutionary histories of protein-protein interactions (PPIs) is crucial for understanding present-day networks.
- Existing methods have limitations in accurately inferring ancestral PPIs and evolutionary events.
Purpose of the Study:
- To present a general framework for inferring the evolutionary history of PPI networks.
- To determine ancestral interactions, predict present-day interactions, and infer protein duplication orders.
Main Methods:
- Developed a framework based on directed hypergraph formulation and dynamic programming.
- Extended the framework to enumerate optimal and near-optimal solutions.
- Applied the algorithm to bZIP transcription factors and herpes viruses.
Main Results:
- The framework accurately reconstructs ancestral interactions, outperforming existing methods.
- Achieved high accuracy in imputing missing present-day interactions for bZIP and herpes virus PPI networks.
- Successfully estimated relative protein duplication orders, showing significant correlation with sequence-based estimates.
Conclusions:
- The proposed framework provides a principled approach to network-level evolutionary history reconstruction.
- The method is effective for inferring ancestral PPIs, imputing missing interactions, and determining protein duplication orders.
- The open-source implementation facilitates broader application in evolutionary and systems biology research.
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