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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 polypeptide...

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 polypeptide...

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,...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.

Protein Complex Assembly

Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types. Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...

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Related Experiment Video

Updated: May 30, 2026

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

In silico Protein-Protein Interaction prediction with sequence alignment and classifier stacking.

Simone Marini¹, Qian Xu, Qiang Yang

¹Bioengineering Program, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong. marini@ust.hk

Current Protein & Peptide Science

|August 11, 2011

Summary

This study introduces a novel approach for predicting protein-protein interactions (PPI) by integrating sequence alignment with machine learning. The combined method significantly enhances prediction accuracy, aiding biological research.

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Area of Science:

Bioinformatics
Computational Biology
Proteomics

Background:

Protein-Protein Interaction (PPI) prediction is crucial but current methods lack sufficient accuracy for experimental guidance.
Existing PPI prediction techniques often fail to integrate diverse information, such as sequence alignment data.

Purpose of the Study:

To develop an integrated approach for enhanced PPI prediction.
To combine sequence alignment information with machine learning classifiers for improved accuracy.

Main Methods:

A novel approach integrating a k-Nearest Neighbor classifier (SA-kNN) with a Support Vector Machine (SVM).
The SVM classifier utilizes Amino Acid compositions and protein signatures.
The SA-kNN classifier leverages protein pair similarity derived from sequence alignment.

Main Results:

The ensemble approach combining SA-kNN and SVM demonstrates complementary strengths.
Significant improvements observed in prediction accuracy (~5%), precision (~16%), and sensitivity (~10%).

Conclusions:

The proposed integrated method offers a more robust and accurate solution for PPI prediction.
This approach effectively overcomes limitations of individual prediction methods, providing better support for experimental biologists.