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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-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: Sep 29, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Fast and effective protein model refinement using deep graph neural networks.

Xiaoyang Jing¹, Jinbo Xu¹

¹Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.

Nature Computational Science

|March 24, 2022

Summary

This summary is machine-generated.

This study introduces a fast protein model refinement method using graph neural networks (GNNs). The GNN approach significantly accelerates the process while maintaining high accuracy, outperforming existing techniques.

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Area of Science:

Computational biology
Structural bioinformatics
Artificial intelligence in biology

Background:

Protein model refinement is crucial for improving predicted protein structures.
Current methods, relying on extensive sampling, are computationally expensive and time-consuming.

Purpose of the Study:

To develop a fast and effective protein model refinement method.
To leverage graph neural networks (GNNs) for predicting inter-atom distance distributions.

Main Methods:

Utilized graph neural networks (GNNs) to predict refined inter-atom distance probability distributions.
Rebuilt 3D protein models from the predicted distance distributions.
Evaluated performance on Critical Assessment of Structure Prediction (CASP) refinement targets.

Main Results:

The GNN-based method achieved accuracy comparable to leading human groups (Feig and Baker).
Refinement time was drastically reduced: ~11 minutes per model on 1 CPU, compared to hours for Baker and Feig.
GNNs outperformed ResNet (convolutional residual neural networks) in refinement with limited conformational sampling.

Conclusions:

Graph neural networks offer a significantly faster and effective approach to protein model refinement.
This method presents a viable alternative to traditional, time-intensive refinement techniques.
The study highlights the potential of GNNs in accelerating structural bioinformatics workflows.