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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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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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Updated: Aug 5, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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HeadTailTransfer: An efficient sampling method to improve the performance of graph neural network method in

Jinhang Wei1, Linlin Zhuo2, Shiyao Pan1

  • 1Wenzhou University of Technology, Wenzhou, 325000, China.

Computers in Biology and Medicine
|March 23, 2023
PubMed
Summary

We developed a novel graph neural network (GNN) framework to predict noncoding RNA (ncRNA)-protein interactions, improving accuracy and speed. A new sampling method, HeadTailTransfer, addresses data sparsity for better predictions.

Keywords:
Graph neural networkNon-coding RNASampling methodTail nodesThe long-tail distributionncRNA–protein interaction

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Noncoding RNAs (ncRNAs) play crucial roles in gene expression and biological processes, but experimental verification of their protein interactions is costly and time-consuming.
  • Machine learning, particularly graph neural networks (GNNs), shows promise for predicting ncRNA-protein interactions due to their ability to leverage network structures.

Purpose of the Study:

  • To propose a general GNN-based framework for predicting ncRNA-protein interactions.
  • To address challenges of data sparsity and long-tail distributions in prediction accuracy for both small and large datasets.

Main Methods:

  • Development of a GNN-based framework to predict ncRNA-protein interactions using topological information.
  • Introduction of a novel sampling method, HeadTailTransfer, to mitigate prediction accuracy issues caused by sparse nodes and long-tail distributions in datasets.

Main Results:

  • The proposed GNN framework demonstrated faster and more accurate predictions of ncRNA-protein interactions.
  • The HeadTailTransfer sampling method significantly improved prediction performance, especially on datasets with sparse nodes.
  • On the RPI369 dataset, AUC and ACC increased from 56.8% and 52.2% to 80.2% and 71.8% respectively, using a GraphSage framework.

Conclusions:

  • The GNN-based framework provides an efficient and accurate method for predicting ncRNA-protein interactions.
  • The HeadTailTransfer sampling strategy effectively enhances prediction accuracy by addressing data imbalances.
  • The developed methods offer valuable tools for studying ncRNA regulation of biological activities.