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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

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Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...

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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

Prediction of protein-protein interaction network using a multi-objective optimization approach.

Archana Chowdhury¹, Pratyusha Rakshit¹, Amit Konar¹

¹1 Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India.

Journal of Bioinformatics and Computational Biology

|February 6, 2016

Summary

This study introduces a novel multi-objective optimization framework for predicting protein-protein interactions (PPIs). The proposed Firefly Algorithm with Nondominated Sorting demonstrates superior performance in identifying functional similarities and interaction strengths.

Keywords:

Protein–protein interaction networks firefly algorithm gene ontology nondominated sorting

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Protein-protein interactions (PPIs) are crucial for nearly all cellular functions.
Accurate prediction of PPIs is essential for understanding cellular mechanisms.
Existing PPI prediction methods have limitations in comprehensively evaluating interaction evidence.

Purpose of the Study:

To develop a multi-objective optimization framework for predicting protein-protein interactions.
To enhance PPI prediction accuracy by simultaneously considering functional similarity, domain interaction strength, and common neighbors.
To evaluate the efficacy of a novel Firefly Algorithm with Nondominated Sorting for PPI prediction.

Main Methods:

Formulation of the PPI prediction problem as a multi-objective optimization task.
Development of scoring functions to maximize functional similarity, domain interaction profiles, and common neighbors.
Application of the proposed Firefly Algorithm with Nondominated Sorting to solve the optimization problem.

Main Results:

The proposed method significantly outperforms existing techniques in PPI prediction.
Experimental results show improvements in sensitivity, specificity, and F1 score compared to various baseline methods.
The Firefly Algorithm with Nondominated Sorting effectively handles the multi-objective nature of PPI prediction.

Conclusions:

The developed multi-objective optimization framework provides a robust approach for PPI prediction.
The proposed algorithm offers a significant advancement over current state-of-the-art methods.
This technique holds promise for advancing our understanding of cellular processes through accurate PPI identification.