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Protein Networks

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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.
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Related Experiment Video

Updated: May 16, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Predicting protein-protein interaction with interpretable bilinear attention network.

Yong Han¹, Shao-Wu Zhang², Ming-Hui Shi²

¹MOE Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China; Henan Judicial Police Vocational College, Zhengzhou, 450046, China.

Computer Methods and Programs in Biomedicine

|April 2, 2025

Summary

This summary is machine-generated.

This study introduces PPI-BAN, a novel framework for predicting protein-protein interactions (PPIs) and their types by integrating sequence and 3D structure data. PPI-BAN effectively identifies key interaction sites, improving upon existing methods.

Keywords:

Bilinear attention network Protein-protein interaction Relational graph neural network

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Last Updated: May 16, 2025

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Published on: January 26, 2024

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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

Area of Science:

Computational Biology
Bioinformatics
Structural Biology

Background:

Protein-protein interactions (PPIs) are crucial for biological processes and disease.
Experimental identification of PPIs is costly and time-consuming.
Existing computational methods often neglect protein structure information or fail to learn joint representations.

Purpose of the Study:

To develop a novel end-to-end framework, PPI-BAN, for predicting PPIs and their interaction types.
To integrate both protein sequence and 3D structure information for enhanced prediction accuracy.
To improve the interpretability of PPI prediction by identifying significant interaction sites.

Main Methods:

PPI-BAN utilizes 1D convolution (Conv1D) for sequence feature extraction.
Geometry-Aware Relational Graph Neural Network (GearNet) is employed for 3D structure feature learning.
A deep Bilinear Attention Network (BAN) learns joint sequence-structure features, which are then concatenated for prediction.

Main Results:

PPI-BAN demonstrated superior performance compared to state-of-the-art methods in predicting PPIs and their types.
The framework effectively integrates sequence and structural information for robust predictions.

Conclusions:

PPI-BAN provides an effective computational approach for predicting protein-protein interactions and their types.
The method can identify critical interaction sites by analyzing attention weight maps, offering insights into molecular mechanisms.