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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

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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,...

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

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Published on: January 26, 2024

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm.

Aijun Deng^1,2,3, Huan Zhang⁴, Wenyan Wang⁴

¹Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology), Ministry of Education, Ma'anshan 243002, China.

International Journal of Molecular Sciences

|March 29, 2020

Summary

This summary is machine-generated.

Predicting protein-protein interaction sites is crucial for understanding cell biology and drug development. This study introduces novel XGBoost-based strategies to address data imbalance, improving prediction accuracy for these vital interaction sites.

Keywords:

XGBoost overlapping regions protein interaction sites unbalanced data sets

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

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Published on: March 3, 2015

Area of Science:

Computational biology
Bioinformatics
Protein science

Background:

Protein-protein interactions (PPIs) are fundamental to cellular processes.
Predicting PPI sites aids in understanding biological activity and drug discovery.
Imbalanced datasets, due to limited experimentally confirmed interactions, challenge computational prediction methods.

Purpose of the Study:

To develop and evaluate novel computational strategies for predicting protein-protein interaction sites.
To address the challenge of imbalanced datasets in protein-protein interaction site prediction.
To improve the accuracy and reliability of computational methods for identifying interaction sites.

Main Methods:

Proposed two imbalanced data processing strategies utilizing the XGBoost algorithm.
Employed a feature extraction method based on evolutionary conservatism of proteins.
Incorporated the influence of overlapping positive and negative samples in prediction.

Main Results:

Achieved a prediction accuracy of 0.807 and a Matthews Correlation Coefficient (MCC) of 0.614.
Demonstrated effective performance on a dataset with a significant imbalance between interface and surface residues.
Validated the effectiveness of the proposed XGBoost-based method through experimental results.

Conclusions:

The developed XGBoost-based strategies effectively handle data imbalance in protein-protein interaction site prediction.
The method shows significant promise for advancing computational approaches in this field.
Improved prediction of protein-protein interaction sites can accelerate biological understanding and drug development.