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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...

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Protein Networks

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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,...

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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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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.
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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

Protein-protein interaction predictions using text mining methods.

Nikolas Papanikolaou¹, Georgios A Pavlopoulos¹, Theodosios Theodosiou¹

¹Division of Basic Sciences, School of Medicine, University of Crete, Heraklion 71003, Greece.

Methods (San Diego, Calif.)

|December 3, 2014

Summary

This summary is machine-generated.

This review explores text-mining computational methods for predicting protein-protein interactions. It highlights their strengths, weaknesses, and how they complement experimental techniques using biological databases and benchmark datasets.

Keywords:

Computational tools Protein–protein interaction prediction Text mining

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Published on: January 26, 2024

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Published on: March 3, 2015

Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Proteins and their interactions are crucial for biological processes.
Understanding protein function and complexes is vital.
High-throughput experimental methods exist for detecting protein-protein interactions.

Purpose of the Study:

To review text-mining based computational methodologies for predicting protein-protein interactions.
To discuss the strengths and weaknesses of these computational approaches.
To highlight how text-mining complements experimental techniques.

Main Methods:

Focus on text-mining to extract protein interaction information from literature and databases.
Analysis of public repositories and biological databases.
Evaluation using benchmark datasets.

Main Results:

Text-mining offers a computational approach to supplement experimental interaction detection.
Identified strengths and weaknesses of text-mining methodologies.
Discussion of relevant biological databases and benchmark datasets.

Conclusions:

Text-mining computational methods are valuable for predicting protein-protein interactions.
These methods complement experimental techniques by leveraging existing data.
Further development and evaluation using benchmark datasets are essential for advancing the field.