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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Kinetic cross-linking and analysis of cDNA is a method that allows investigation of the dynamics of protein-RNA interactions in living cells at high temporal resolution. Here the protocol is described in detail, including the growth of yeast cells, UV cross-linking, harvesting, protein purification, and next generation sequencing library preparation...
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A biochemical approach is described to identify in vivo protein-protein interactions (PPI) of membrane proteins. The method combines protein cross-linking, affinity purification and mass spectrometry, and is adaptable to almost any cell type or organism. With this approach, even the identification of transient PPIs becomes...
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks.

Jinxiong Zhang1,2, Cheng Zhong2, Hai Xiang Lin3

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.

Biomed Research International
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying protein complexes using dynamic temporal protein-protein interaction networks (TI-PINs). The approach enhances accuracy by preserving continuous interactions and integrating multiple data sources for robust complex identification.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Protein complex identification is crucial for understanding biological mechanisms.
  • Static protein-protein interaction (PPI) networks have limitations in capturing cellular dynamics.
  • Dynamic PPI networks offer a more realistic representation of cellular processes.

Purpose of the Study:

  • To develop a novel method for accurately identifying protein complexes from dynamic temporal PPI networks.
  • To enhance protein complex identification by preserving continuous interactions within temporal intervals.
  • To improve the accuracy and quantity of identified protein complexes compared to existing methods.

Main Methods:

  • Constructing dynamic temporal PPI networks using undulating gene expression degrees.
  • Converting temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs).
  • Proposing a novel identification method integrating multisource biological data (colocalization, coexpression, cluster expansion).

Main Results:

  • The constructed TI-PINs preserve crucial dynamical information for protein complex identification.
  • The proposed method effectively ensures identified complexes exhibit colocalization, coexpression, and functional homogeneity.
  • Experimental results on yeast datasets show superior performance over existing dynamic PPI network methods and other identification approaches.

Conclusions:

  • Dynamic temporal PPI networks, specifically TI-PINs, provide a more informative framework for protein complex identification.
  • The novel multisource data integration method accurately identifies more protein complexes with biological relevance.
  • This approach advances the field of computational protein complex identification by incorporating temporal dynamics.