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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...

Protein-Protein Interfaces

Protein-Protein Interfaces

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins.

Barnali Das¹, Pralay Mitra¹

¹Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur 721302, India.

Journal of Molecular Biology

|July 16, 2021

Summary

This summary is machine-generated.

A new Graph Convolutional Network (GCN) model, PINDeL, accurately identifies disease-associated host proteins. This method aids in discovering novel therapeutic targets for various human diseases.

Keywords:

deep learning-based classification disease-associated proteins enrichment analysis graph convolutional networks topological features of protein locality graph

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

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Published on: March 3, 2015

Area of Science:

Computational biology
Bioinformatics
Network science

Background:

Infectious diseases pose significant public health challenges.
Identifying disease-associated proteins is crucial for developing novel therapeutic targets.

Purpose of the Study:

To develop a Graph Convolutional Network (GCN)-based model named PINDeL for identifying disease-associated host proteins.
To integrate human Protein Locality Graph and its topological features for enhanced prediction accuracy.

Main Methods:

Developed a Graph Convolutional Network (GCN)-based model, PINDeL.
Integrated human Protein Locality Graph and topological features.
Validated the model on an independent dataset and utilized experimental evidence for verification.

Main Results:

PINDeL achieved 83.45% accuracy, with AUROC of 0.90 and AUPRC of 0.88.
Outperformed existing machine and deep learning techniques in disease protein identification.
Predicted 6448 new disease-protein associations, verifying 3196 through experimental evidence.
Identified 748 proteins involved in pathogen-host interactions, with 22 linked to multiple diseases.

Conclusions:

PINDeL is a highly accurate model for large-scale disease-protein association prediction.
The model provides crucial insights into disease pathogenesis.
PINDeL aids in the development of novel therapeutics by identifying key disease proteins.