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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-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 Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Protein-Protein Interfaces

Protein-Protein Interfaces

Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

Structural Protein Function

Structural Protein Function

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Related Experiment Video

Updated: Aug 13, 2025

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

Towards a structurally resolved human protein interaction network.

David F Burke¹, Patrick Bryant^2,3, Inigo Barrio-Hernandez¹

¹European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.

Nature Structural & Molecular Biology

|January 23, 2023

Summary

This summary is machine-generated.

Deep learning accurately predicts human protein interactions, revealing novel structures and disease insights. This advance aids understanding of cellular machinery and disease mechanisms.

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Area of Science:

Structural biology
Computational biology
Genomics

Background:

Cellular functions rely on protein-protein interactions (PPIs) for molecular machine assembly.
Understanding PPI atomic details is crucial for elucidating molecular mechanisms.
Currently, structural data exists for less than 5% of known human PPIs.

Purpose of the Study:

To assess the capabilities and constraints of deep learning, specifically AlphaFold2, in predicting human PPI structures.
To generate a structural interactome for a large number of human PPIs.
To explore the utility of predicted structures in identifying disease-relevant interfaces and regulatory patterns.

Main Methods:

Utilized AlphaFold2 to predict structures for 65,484 human protein interactions.
Employed experimental validation to confirm high-confidence predicted models.
Analyzed predicted structures to identify disease mutations and phosphorylation sites at interaction interfaces.

Main Results:

Generated 3,137 high-confidence structural models for human protein interactions.
Identified 1,371 models with no known structural homology, representing novel structural insights.
Located disease mutations within interaction interfaces and observed co-regulation patterns of phosphorylation sites.

Conclusions:

Deep learning methods like AlphaFold2 show significant potential for expanding the structural interactome.
Predicted structures offer valuable insights into disease mechanisms and signaling pathway regulation.
The generated structural models can serve as a foundation for understanding larger protein assemblies and cellular processes.