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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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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.

Yasmmin Côrtes Martins1, Artur Ziviani2, Maiana de Oliveira Cerqueira E Costa1

  • 1Bioinformatics Laboratory, National Laboratory for Scientific Computing, Petrópolis 25651-076, Brazil.

Bioinformatics Advances
|June 26, 2023
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Summary

PPIntegrator semantically integrates protein-protein interaction data, enabling new host-pathogen interaction discovery through transitivity analysis. This system enhances biological data analysis and interoperability.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Semantic Web Technologies

Background:

  • Semantic web standards are crucial for data formalization and interlinking knowledge graphs.
  • Biological data integration, particularly for protein-protein interactions (PPIs), faces challenges due to heterogeneous data formats.
  • Existing ontologies for PPIs promote interoperability but lack automated semantic integration guidelines.

Purpose of the Study:

  • To present PPIntegrator, a system for semantic description and integration of protein interaction data.
  • To introduce an enrichment pipeline for generating, predicting, and validating host-pathogen PPI datasets using transitivity analysis.
  • To demonstrate the application of PPIntegrator for analyzing and comparing host-pathogen PPI datasets.

Main Methods:

  • Developed PPIntegrator with data preparation, triplification, and data fusion modules.
  • Implemented a transitivity analysis pipeline for generating and validating new host-pathogen PPI datasets.
  • Integrated and compared host-pathogen PPI data from three reference databases for four bacterial species.

Main Results:

  • Successfully integrated and semantically described host-pathogen PPI datasets.
  • Generated and validated potential new host-pathogen interactions via transitivity analysis.
  • Demonstrated critical queries for analyzing semantic PPI data.

Conclusions:

  • PPIntegrator facilitates semantic data integration and analysis of PPIs.
  • The transitivity analysis pipeline aids in discovering and validating novel host-pathogen interactions.
  • Semantic data enhances the understanding and utility of biological interaction datasets.