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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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein Networks02:26

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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Zi-Ang Shen1, Tao Luo1, Yuan-Ke Zhou1

  • 1College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.

Briefings in Bioinformatics
|April 6, 2021
PubMed
Summary
This summary is machine-generated.

We developed Noncoding RNA-Protein Interaction prediction using Graph Neural Networks (NPI-GNN), a novel computational method for predicting ncRNA-protein interactions. NPI-GNN shows robust performance, offering a cost-effective alternative to experimental methods.

Keywords:
graph neural networkncRNA–protein interactionnoncoding RNA

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Molecular biology

Background:

  • Noncoding RNAs (ncRNAs) are vital in biological processes.
  • Experimental identification of ncRNA-protein interactions (NPIs) is resource-intensive.
  • Computational methods offer efficient alternatives for NPI prediction.

Purpose of the Study:

  • To evaluate existing machine learning methods for NPI prediction.
  • To introduce a novel Graph Neural Network (GNN)-based method, NPI-GNN, for predicting NPIs.
  • To assess the performance and robustness of NPI-GNN.

Main Methods:

  • Collected and utilized five benchmarking datasets for NPI prediction.
  • Evaluated and compared existing machine learning-based NPI prediction methods.
  • Developed and applied an end-to-end GNN-based model (NPI-GNN) incorporating network and sequence information.

Main Results:

  • NPI-GNN achieved performance comparable to state-of-the-art methods via 5-fold cross-validation.
  • The method demonstrated capability in predicting novel NPIs using network and sequence data.
  • NPI-GNN exhibited robustness, with minimal performance impact from reduced sequence information.

Conclusions:

  • NPI-GNN represents the first end-to-end GNN predictor for NPIs.
  • The developed method provides an efficient and robust computational approach for NPI prediction.
  • Accessible datasets and source code facilitate further research and application.