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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 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....

Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Updated: Nov 9, 2025

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

A review on compound-protein interaction prediction methods: Data, format, representation and model.

Sangsoo Lim¹, Yijingxiu Lu², Chang Yun Cho³

¹Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.

Computational and Structural Biotechnology Journal

|April 12, 2021

Summary

Computational methods for predicting compound-protein interactions (CPI) have advanced rapidly due to better data and sophisticated techniques. This review covers data, formats, and models to aid researchers in developing improved CPI prediction tools.

Keywords:

Chemical descriptors Compound-protein interaction Data representation Deep learning Interpretable learning Machine learning Pharmacophore discovery Protein descriptors

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Area of Science:

Computational chemistry
Bioinformatics
Drug discovery

Background:

Compound-protein interactions (CPI) are crucial for drug development.
Traditional methods for CPI prediction are being enhanced by computational approaches.
Significant advancements in data quality and quantity have occurred.

Purpose of the Study:

To comprehensively review computational methods for CPI prediction.
To discuss data resources, formats, and encoding schemes for CPI.
To guide researchers in developing novel and accurate CPI prediction models.

Main Methods:

Review of existing literature on computational CPI prediction.
Categorization of CPI prediction methods into five groups, from traditional machine learning to deep learning.
Discussion of data formats and encoding schemes for chemical compounds and protein data.

Main Results:

Recent successes in CPI prediction are attributed to sophisticated computational techniques and high-quality database information.
A wide range of computational methods, including machine learning and deep learning, are available for CPI prediction.
Emerging machine learning topics are highlighted to foster further development.

Conclusions:

The integration of advanced computational techniques with high-quality data significantly improves CPI prediction accuracy.
This review provides a foundational understanding of CPI data and methods for researchers.
Further research into emerging machine learning approaches will enhance the development of more powerful CPI prediction tools.