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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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Sensitivity analysis on protein-protein interaction networks through deep graph networks.

Alessandro Dipalma1, Michele Fontanesi2, Alessio Micheli2

  • 1Department of Computer Science, University of Pisa, Largo Bruno Pontecorvo, 3, 56125, Pisa, PI, Italy. alessandro.dipalma@phd.unipi.it.

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Summary

This study introduces a novel method to enrich static protein-protein interaction networks (PPINs) with dynamic properties derived from biochemical pathways (BP). The developed model effectively predicts sensitivity relationships within PPINs, enhancing their utility for biological insights.

Keywords:
Deep graph networksProtein-protein interactionSensitivity analysis

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Protein-protein interaction networks (PPINs) offer a static view of cellular processes.
  • Existing PPINs lack dynamic information crucial for understanding biological systems.
  • Biochemical pathways (BPs) capture dynamics but are limited in scope and computationally intensive.

Purpose of the Study:

  • To enrich static PPINs with dynamic properties computed from BPs.
  • To develop a computational model for predicting dynamic properties directly from PPINs.
  • To explore the potential of PPIN structure in inferring dynamic behaviors.

Main Methods:

  • ODE simulations were used to analyze BPs and compute sensitivity between chemical species.
  • Sensitivity data was integrated into PPINs to create the DyPPIN dataset.
  • A deep graph network (DGN) was trained on DyPPIN to predict protein sensitivity relationships.

Main Results:

  • The DyPPIN dataset successfully enabled a DGN to predict sensitivity relationships within PPINs.
  • PPIN structure was found to be crucial for inferring sensitivity.
  • Protein sequence embeddings further improved the predictive accuracy of the model.

Conclusions:

  • This work presents the first model for direct sensitivity analysis on PPINs.
  • PPIN structure alone contains sufficient information to infer dynamic properties.
  • The pipeline is adaptable for applications in drug design, repurposing, and personalized medicine.