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Protein Networks

Protein Networks

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,...

Protein Networks

Protein Networks

Protein-protein Interfaces

Protein-protein Interfaces

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...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

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Related Experiment Video

Updated: Nov 10, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

HPOFiller: identifying missing protein-phenotype associations by graph convolutional network.

Lizhi Liu¹, Hiroshi Mamitsuka^2,3, Shanfeng Zhu^4,5,6,7,8

¹School of Computer Science, Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)

|April 6, 2021

Summary

This summary is machine-generated.

HPOFiller, a novel graph convolutional network approach, accurately predicts missing human protein-phenotype associations. This method enhances disease gene discovery by improving Human Phenotype Ontology annotations.

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Last Updated: Nov 10, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Understanding human protein-phenotype relationships is crucial for disease research.
The Human Phenotype Ontology (HPO) standardizes phenotype abnormalities but has incomplete protein annotations.
Identifying missing protein-phenotype associations is vital for advancing disease diagnosis and treatment.

Purpose of the Study:

To develop an accurate computational method for predicting missing protein-phenotype associations.
To leverage network structures and graph convolutional networks for improved Human Phenotype Ontology annotation.
To identify novel disease-gene associations through enhanced protein-phenotype data.

Main Methods:

Proposed HPOFiller, a graph convolutional network (GCN)-based approach.
Utilized S-GCN on protein-protein interaction and HPO semantic similarity networks.
Employed Bi-GCN on the protein-phenotype bipartite graph for message passing.
Repeatedly applied GCN modules to refine protein and phenotype embeddings.

Main Results:

HPOFiller significantly outperformed existing state-of-the-art methods in predicting missing HPO annotations.
Stringent cross-validation and temporal validation confirmed the model's robustness.
Ablation studies indicated batch normalization as a key contributor to performance.
High-ranking predictions were supported by existing literature evidence.

Conclusions:

HPOFiller effectively predicts missing protein-phenotype associations, enhancing the Human Phenotype Ontology.
The method successfully identifies potential novel disease-gene associations, offering insights into genetic disorders.
This approach provides a valuable tool for disease gene discovery and understanding genetic causes of human disorders.