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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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Graph-based prediction of Protein-protein interactions with attributed signed graph embedding.

Fang Yang1, Kunjie Fan2, Dandan Song3

  • 1School of Computer Science and Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China.

BMC Bioinformatics
|July 23, 2020
PubMed
Summary

We developed a new graph representation learning method to predict protein-protein interactions (PPIs) by combining sequence and structural information. This advanced approach achieves high accuracy, outperforming existing methods for crucial biological insights.

Keywords:
Network embeddingProtein-protein interactionRepresentation learningVariational graph auto-encoder

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Network Science

Background:

  • Protein-protein interactions (PPIs) are fundamental to biological processes.
  • Experimental PPI identification is costly and time-consuming.
  • Computational methods, particularly sequence-based deep learning, show promise but neglect network structure.

Purpose of the Study:

  • To develop an improved computational method for PPI prediction.
  • To integrate both sequence and structural information of PPI networks.
  • To enhance the accuracy and efficiency of automated PPI prediction.

Main Methods:

  • Introduced a novel graph representation learning model.
  • Employed a graph-based deep learning approach.
  • Utilized signed variational graph auto-encoder (S-VGAE) for encoding graph structure.

Main Results:

  • Achieved state-of-the-art accuracy of 99.15% on the HPRD dataset.
  • Demonstrated superior performance over sequence-based methods.
  • Obtained optimal results on multiple datasets including DIP, HPRD, E. coli, C. elegans, and Drosophila.

Conclusions:

  • The S-VGAE method effectively integrates sequence and graph structure for PPI prediction.
  • The model shows robustness in sparse networks and generalization capabilities.
  • This approach offers a significant advancement in computational prediction of protein interactions.