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Protein-protein Interfaces

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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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Updated: May 5, 2026

In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces

In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces

Published on: March 19, 2010

Nature-Inspired Meta-Heuristic Algorithms for Detecting Protein Complexes in Protein-Protein Interaction Networks: A

Wei Zheng, Jianyong Sun, Haotian Zhang

IEEE Transactions on Computational Biology and Bioinformatics

|August 25, 2025

Summary

This summary is machine-generated.

This study surveys meta-heuristic algorithms for identifying protein complexes in protein-protein interaction networks. It analyzes 34 methods from 2004-2024, highlighting limitations and future research directions for computational biology.

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Last Updated: May 5, 2026

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Published on: April 1, 2017

Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Protein complexes are crucial for cellular functions.
Experimental detection of protein complexes is resource-intensive.
Computational methods are increasingly used for protein complex identification.

Purpose of the Study:

To systematically survey meta-heuristic optimization algorithms for protein complex detection.
To analyze the evolution and application of these algorithms from 2004 to 2024.
To identify limitations and suggest future research directions in the field.

Main Methods:

Comprehensive literature review of meta-heuristic algorithms for protein complex identification.
Analysis of 34 relevant studies published between 2004 and 2024.
Focus on problem modeling and optimization algorithm design within the surveyed methods.

Main Results:

Detailed summary of 34 meta-heuristic algorithms applied to protein complex identification.
Categorization of methods based on problem modeling and algorithm design.
Identification of common challenges and successful strategies in computational approaches.

Conclusions:

Meta-heuristic algorithms offer a powerful computational approach to protein complex identification.
Existing methods exhibit limitations in problem representation and algorithm optimization.
Future research should focus on refining these algorithms and exploring novel computational strategies.