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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...

Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

Ligand Binding and Linkage

Ligand Binding and Linkage

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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Related Experiment Video

Updated: May 1, 2026

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Exploring high-order functional interactions via structurally-weighted LASSO models.

Dajiang Zhu, Xiang Li, Xi Jiang

Information Processing in Medical Imaging : Proceedings of the ... Conference

|April 2, 2014

Summary

This summary is machine-generated.

This study introduces a new structurally-weighted LASSO model to analyze high-order brain network interactions using resting-state fMRI data. The model reveals distinct functional network patterns in Mild Cognitive Impairment patients compared to controls.

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Area of Science:

Neuroscience
Brain Imaging
Network Science

Background:

Modeling functional interactions in neural networks is crucial for understanding brain mechanisms.
Current neuroimaging methods often limit analysis to pair-wise connections, neglecting higher-order network interactions.

Purpose of the Study:

To propose a novel structurally-weighted LASSO (SW-LASSO) regression model for representing multi-region functional interactions.
To leverage diffusion tensor imaging (DTI) data to guide the SW-LASSO model for improved accuracy.

Main Methods:

Developed a structurally-weighted LASSO (SW-LASSO) regression model.
Integrated structural connectivity constraints from DTI data to inform the model's weighting.
Validated model robustness and accuracy through experimental evaluations.

Main Results:

The SW-LASSO model successfully represents high-order functional interactions in resting-state fMRI (rsfMRI) data.
Application example showed different assortative mixing patterns in functional networks between Mild Cognitive Impairment (MCI) patients and normal controls (NC).

Conclusions:

The proposed SW-LASSO model enables the construction of high-order functional brain networks.
This approach shows potential for clinical applications, particularly in differentiating patient groups based on network characteristics.