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Related Concept Videos

Protein Networks02:26

Protein Networks

3.7K
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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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells
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Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

Suyu Mei1

  • 1Software College, Shenyang Normal University, Shenyang, China.

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|November 22, 2013
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This study introduces a novel computational model to predict HIV-human protein interactions, addressing data limitations for better understanding microbial pathogenesis. The model enhances accuracy and robustness, aiding future biological research.

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SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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Area of Science:

  • Computational biology
  • Network biology
  • Infectious disease research

Background:

  • Host-pathogen protein interaction networks are crucial for understanding microbial pathogenesis but are often incomplete.
  • Computational methods are needed to augment experimental data for less-biased biological inference.
  • Challenges in computational modeling include data scarcity, unavailability, and negative data sampling.

Purpose of the Study:

  • To address data scarcity, unavailability, and negative data sampling in host-pathogen protein interaction network reconstruction.
  • To propose a probability weighted ensemble transfer learning model (PWEN-TLM) for HIV-human protein interaction prediction.
  • To improve the robustness and reduce data constraints for predicting host-pathogen interactions.

Main Methods:

  • Developed a probability weighted ensemble transfer learning model (PWEN-TLM) using support vector machine (SVM) classifiers.
  • Employed homolog knowledge transfer to address data scarcity and unavailability.
  • Utilized ROC-AUC metric to weigh the importance of homolog knowledge.
  • Investigated the reliability of exclusiveness of subcellular co-localized proteins for negative data construction.

Main Results:

  • The PWEN-TLM model effectively tackles data scarcity and unavailability through homolog knowledge transfer.
  • Homolog knowledge transfer proved sufficient for training a satisfactory host-pathogen protein interaction prediction model.
  • Exclusiveness of subcellular co-localized proteins was identified as a more reliable method for negative data sampling than random sampling.
  • The model demonstrated robustness against data unavailability and less demanding data constraints.

Conclusions:

  • The proposed PWEN-TLM offers a robust computational approach for reconstructing host-pathogen protein interaction networks.
  • The findings suggest that homolog knowledge transfer and specific negative data sampling strategies can overcome key challenges in the field.
  • The model's predictions can be further analyzed and applied to novel host-pathogen PPI recognition for biological research.