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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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Multitask Matrix Completion for Learning Protein Interactions Across Diseases.

Meghana Kshirsagar1, Keerthiram Murugesan2, Jaime G Carbonell2

  • 11 Memorial Sloan Kettering Cancer Center , New York, New York.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 28, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a multitask learning method to predict virus-human protein interactions for Hepatitis C, Ebola, and Influenza A viruses. The model significantly improves prediction accuracy, aiding infectious disease research.

Keywords:
host–pathogen protein–protein interactionmatrix completionmultitask learningprotein interaction predictionviruses

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Area of Science:

  • Computational biology
  • Infectious disease research
  • Bioinformatics

Background:

  • Viruses interact with host proteins for replication, crucial for understanding infectious diseases.
  • Related viruses often share biological similarities, suggesting potential for joint modeling.
  • Predicting pathogen-host protein interactions is vital for developing antiviral strategies.

Purpose of the Study:

  • To develop a multitask learning method for jointly modeling human protein interactions with three related viruses: Hepatitis C, Ebola, and Influenza A.
  • To improve the accuracy of predicting these complex interactions.
  • To provide interpretable insights into virus-specific and general interaction characteristics.

Main Methods:

  • A multitask matrix completion-based model was employed.
  • The model incorporates a shared low-rank structure and task-specific sparse structures.
  • This approach jointly analyzes interactions across different viral tasks.

Main Results:

  • Achieved 7 to 39 percentage point improvement in predictive performance over existing state-of-the-art models.
  • Demonstrated the model's ability to reveal general and specific interaction-relevant characteristics of the viruses.
  • The developed code is publicly available for further research.

Conclusions:

  • Multitask learning effectively models interactions between human proteins and related viruses.
  • The proposed method offers significant performance gains in predicting these interactions.
  • The model provides valuable insights for understanding infectious disease mechanisms.