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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

Conserved Binding Sites

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

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Related Experiment Video

Updated: Apr 6, 2026

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

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

Published on: March 3, 2015

Protein function prediction using guilty by association from interaction networks.

Damiano Piovesan¹, Manuel Giollo^1,2, Carlo Ferrari²

¹Department of Biomedical Sciences, University of Padua, Viale G. Colombo 3, 35131, Padua, Italy.

|July 29, 2015

Summary

This summary is machine-generated.

Predicting protein function using protein-protein interactions (PPIs) is a powerful approach. The Guilty by Association on STRING (GAS) tool leverages interaction networks to identify protein functions, outperforming sequence similarity for certain Gene Ontology (GO) predictions.

Keywords:

CAFA Gene ontology Protein function Protein interaction network Protein sequence

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Published on: October 24, 2025

Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Protein function prediction is crucial for understanding biological systems.
The Gene Ontology (GO) classification provides a standardized framework for describing protein functions.
Recent challenges like CAFA have spurred interest in improving protein function prediction methods.

Purpose of the Study:

To introduce Guilty by Association on STRING (GAS), a novel tool for protein function prediction.
To evaluate the performance of GAS using protein-protein interaction (PPI) networks.
To compare GAS performance against traditional sequence similarity searches.

Main Methods:

GAS utilizes the STRING database to retrieve protein interaction partners for query proteins.
It measures the enrichment of functional annotations among interacting proteins to predict function.
Performance is assessed using Critical Assessment of Function Annotation (CAFA) metrics and compared with BLAST.

Main Results:

The protein-protein interaction (PPI) approach, implemented in GAS, outperforms sequence similarity searches for predicting biological process and cellular compartment GO terms.
Combining GAS and BLAST (consensus) improves overall prediction performance.
Analysis provides insights into best practices for leveraging PPI networks in function prediction.

Conclusions:

GAS offers an effective method for protein function prediction by exploiting PPI networks.
PPI-based approaches are valuable complements or alternatives to sequence similarity for specific GO categories.
The study highlights the utility of network-based methods in advancing functional genomics.