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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,...

Protein Networks

Protein Networks

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 Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

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...

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

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Related Experiment Video

Updated: Apr 12, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

A graph modification approach for finding core-periphery structures in protein interaction networks.

Sharon Bruckner¹, Falk Hüffner², Christian Komusiewicz²

¹International Max Planck Research School for Computational Biology and Scientific Computing, Ihnestr. 63-73, Berlin, 14195 Germany.

Algorithms for Molecular Biology : AMB

|May 23, 2015

Summary

This summary is machine-generated.

This study introduces two graph-theoretic models to identify global core-periphery structures in protein-protein interaction (PPI) networks. Algorithms were developed and evaluated on yeast PPI networks.

Keywords:

Graph classes NP-hard problems Protein complexes

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Last Updated: Apr 12, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

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Area of Science:

Computational Biology
Network Science
Bioinformatics

Background:

Protein-protein interaction (PPI) networks are crucial for understanding cellular mechanisms.
The core-periphery model describes protein complexes as having a dense core and a surrounding periphery.
Identifying global core-periphery structures in PPI networks remains a challenge.

Purpose of the Study:

To propose and analyze exact graph-theoretic formulations for uncovering global core-periphery structures in PPI networks.
To develop efficient exact and heuristic algorithms for identifying these structures.
To evaluate the proposed models and algorithms on real biological data.

Main Methods:

Formulated two exact graph-theoretic models for global core-periphery structure identification.
Analyzed the theoretical complexity, proving NP-hardness for both models.
Developed and implemented efficient exact and heuristic algorithms.
Evaluated performance on subnetworks of the *Saccharomyces cerevisiae* PPI network.

Main Results:

Demonstrated the NP-hardness of finding global core-periphery structures.
Developed algorithms that efficiently approximate or find exact solutions.
Showcased the applicability of the models on a biological PPI network.

Conclusions:

The proposed graph-theoretic models provide a robust framework for analyzing global core-periphery structures in PPI networks.
The developed algorithms offer efficient solutions for identifying these structures.
This work contributes to a deeper understanding of network organization in biological systems.