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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

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 Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...

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Related Experiment Video

Updated: Jan 7, 2026

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

Component puzzle protein-protein interaction prediction.

SeyedMohsen Hosseini¹, G Brian Golding², Lucian Ilie¹

¹Department of Computer Science, University of Western Ontario, London, N6A 5B7 Ontario, Canada.

Briefings in Bioinformatics

|December 22, 2025

Summary

This summary is machine-generated.

C3PI, a new deep learning framework, accurately predicts protein-protein interactions (PPIs) by avoiding data leakage. This novel method significantly improves upon existing computational approaches for PPI prediction.

Keywords:

ProtT5 machine learning protein embedding protein interaction

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Last Updated: Jan 7, 2026

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

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

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

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

Published on: March 3, 2015

Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Protein-protein interactions (PPIs) are crucial for cellular functions.
Experimental PPI determination is time-consuming and costly.
Existing computational methods suffer from data leakage, leading to unreliable predictions.

Purpose of the Study:

To develop a novel, accurate computational method for predicting PPIs.
To address the data leakage issue in current PPI prediction models.
To provide a reliable tool for PPI prediction research.

Main Methods:

Developed C3PI, a sequence-based deep learning framework.
Utilized ProtT5 protein embeddings as input.
Incorporated novel 'puzzler' and 'entangler' components into the architecture.

Main Results:

C3PI consistently outperforms state-of-the-art methods on various datasets.
Achieved significant improvements in key metrics like AUPRC and AUROC.
Demonstrated superior performance on a leakage-free gold standard dataset, outperforming random predictions.

Conclusions:

C3PI represents a significant advancement in computational PPI prediction.
The novel architecture effectively mitigates data leakage issues.
C3PI offers a reliable and accurate tool for biological research, available via web server and source code.