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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,...

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: Jan 1, 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

Using discriminative vector machine model with 2DPCA to predict interactions among proteins.

Zhengwei Li^1,2,3,4, Ru Nie^5,6, Zhuhong You⁷

¹School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.

BMC Bioinformatics

|December 26, 2019

Summary

This summary is machine-generated.

This study introduces a novel computational model for predicting protein-protein interactions (PPIs) using protein sequences. The Discriminative Vector Machine (DVM) model achieves high accuracy, offering a cost-effective alternative to experimental methods in proteomics research.

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Last Updated: Jan 1, 2026

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

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Published on: January 26, 2024

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

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Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Area of Science:

Computational Biology
Bioinformatics
Proteomics

Background:

Protein-protein interactions (PPIs) are vital for cellular processes.
Experimental PPI identification methods are costly and inefficient.
In silico methods offer a promising alternative for PPI prediction.

Purpose of the Study:

To develop a novel computational model for predicting PPIs based solely on protein sequences.
To improve the efficiency and accuracy of PPI identification compared to existing methods.

Main Methods:

Utilized a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to extract features from Position-Specific Scoring Matrix (PSSM) data.
Employed a Discriminative Vector Machine (DVM) classifier for PPI inference.
Validated the model on benchmark datasets from Yeast, H. pylori, and Human.

Main Results:

Achieved high prediction accuracies of 97.06% for Yeast and 92.89% for H. pylori.
Demonstrated significant improvements over state-of-the-art methods.
Showcased the model's effectiveness in cross-species PPI prediction.

Conclusions:

The proposed computational method is highly effective for identifying potential PPIs.
This approach can serve as a practical tool to support experimental proteomics research.