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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

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Structural Protein Function

Structural Protein Function

Structural Protein Function

Structural Protein Function

Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity. In bones and teeth, it mineralizes to...

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Related Experiment Video

Updated: Nov 4, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Structure-based protein function prediction using graph convolutional networks.

Vladimir Gligorijević¹, P Douglas Renfrew², Tomasz Kosciolek^3,4

¹Center for Computational Biology, Flatiron Institute, New York, NY, USA. vgligorijevic@flatironinstitute.org.

Nature Communications

|May 27, 2021

Summary

This summary is machine-generated.

DeepFRI, a novel Graph Convolutional Network, accurately predicts protein functions using sequence and structure data. This computational tool enhances protein function annotation and aids in discovering new biological insights.

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Area of Science:

Computational biology
Structural bioinformatics
Machine learning

Background:

Databases of protein sequences are growing rapidly, making automated function prediction challenging.
Existing computational methods struggle with the diversity of protein functions.

Purpose of the Study:

To develop an advanced computational method for automated protein function prediction.
To leverage both protein sequence and structural information for improved accuracy.

Main Methods:

Introduced DeepFRI, a Graph Convolutional Network (GCN) model.
Integrated sequence features from protein language models with protein structural data.
Augmented training data with homology models to expand function prediction capabilities.

Main Results:

DeepFRI outperforms existing leading methods and sequence-based Convolutional Neural Networks.
The method demonstrates scalability for large sequence repositories.
Class activation mapping enables residue-level, site-specific function predictions.
DeepFRI shows robustness, with minimal performance loss when using homology models instead of experimental structures.

Conclusions:

DeepFRI provides a highly accurate and scalable solution for automated protein function prediction.
The method facilitates novel function predictions and residue-level annotations.
DeepFRI is accessible via a webserver for broader scientific use.