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Protein-protein Interfaces

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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...

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Related Experiment Video

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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

Deep Learning for Protein-Protein Interaction Site Prediction.

Arian R Jamasb^1,2, Ben Day¹, Cătălina Cangea¹

¹Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.

Methods in Molecular Biology (Clifton, N.J.)

|July 8, 2021

Summary

This study introduces a deep learning method for predicting protein-protein interaction (PPI) sites. This computational approach aims to improve accuracy and efficiency over experimental methods for identifying drug targets.

Keywords:

Deep learning Geometric deep learning Graph Machine learning Protein Protein–protein interaction Structural biology Structure

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Published on: November 3, 2011

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Last Updated: Oct 29, 2025

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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
Machine learning

Background:

Protein-protein interactions (PPIs) are crucial for cellular functions.
Experimental PPI prediction is costly and error-prone at scale.
Computational prediction offers mechanistic insights and drug target identification.

Purpose of the Study:

To outline the development of a deep learning approach for PPI site prediction.
To highlight key decisions in supervised machine learning for this task.
To discuss alternative training regimes for deep learning models.

Main Methods:

Development of a deep learning model for PPI site prediction.
Application of supervised machine learning principles.
Exploration of geometric deep learning on protein structure graphs.

Main Results:

Details of a deep learning approach for predicting protein residues involved in PPIs are provided.
Key considerations for supervised machine learning projects in this domain are highlighted.
Alternative training strategies are discussed to improve existing methods.

Conclusions:

The developed deep learning approach offers a more efficient and accurate method for PPI site prediction.
This work serves as a guide for developing deep learning models for PPIs.
It provides a foundation for geometric deep learning in protein structure analysis.