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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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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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Related Experiment Video

Updated: Nov 6, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Proteo3Dnet: a web server for the integration of structural information with interactomics data.

Guillaume Postic1,2, Jessica Andreani3, Julien Marcoux4

  • 1Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France.

Nucleic Acids Research
|May 8, 2021
PubMed
Summary

Proteo3Dnet organizes protein interaction data from mass spectrometry. This web server integrates structural, motif-based, and validated interactions to visualize complex networks and predict new partners.

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Area of Science:

  • Proteomics
  • Structural Biology
  • Bioinformatics

Background:

  • Mass spectrometry interactomics generates large datasets of protein interactions.
  • Organizing and interpreting these interactions, especially structural and transient ones, remains challenging.
  • Understanding protein complex organization is crucial for deciphering cellular functions.

Purpose of the Study:

  • To present Proteo3Dnet, a web server for analyzing mass spectrometry interactomics data.
  • To organize protein lists into structural interaction networks.
  • To provide a clearer overview of complex biological data and suggest potential interactions.

Main Methods:

  • Integrating structural data from the Protein Data Bank (PDB) via interolog search, including remote homology.
  • Predicting weaker interactions using Short Linear Motifs (SLMs) via ELM database.
  • Incorporating physically validated protein-protein interactions from the BioGRID database.
  • Compiling and visualizing interaction data as an interactive graph.

Main Results:

  • Proteo3Dnet provides a comprehensive view of protein interactions by combining diverse data sources.
  • The generated interaction graphs allow interactive exploration of complex networks.
  • The server successfully identified interactions in known complex datasets (proteasome, pragmin) and applied to yeast ribosomal subunits and 14-3-3zeta interactome.
  • Proteo3Dnet can suggest potential undetected protein partners and binding specificities.

Conclusions:

  • Proteo3Dnet is a valuable tool for the analysis and visualization of mass spectrometry interactomics data.
  • The integration of structural, motif-based, and validated interaction data enhances the interpretation of complex biological networks.
  • The web server aids researchers in identifying protein complex organization, predicting novel interactions, and understanding protein binding dynamics.