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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...

G Protein-coupled Receptors

G Protein-coupled Receptors

G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network

Fei Zhu¹, Feifei Li¹, Lei Deng¹

¹School of Computer Science and Technology, Soochow University, Suzhou 215 006, China.

Journal of Chemical Information and Modeling

|January 10, 2022

Summary

This summary is machine-generated.

A new deep learning model, Structural Gated Attention Deep (SGAD), enhances protein-protein interaction network reconstruction. SGAD improves prediction accuracy by integrating sequence, topological, and information flow features, outperforming existing methods.

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Protein-protein interactions (PPIs) are crucial for cellular functions and understanding disease.
Existing machine learning models for PPI prediction using only protein sequences lack robustness and accuracy.

Purpose of the Study:

To develop a novel deep learning framework, Structural Gated Attention Deep (SGAD), for improved protein-protein interaction network reconstruction (PINR).
To enhance the robustness and prediction accuracy of PPI network analysis.

Main Methods:

Developed the SGAD model, a deep learning framework for PINR.
Augmented protein sequence descriptors, PPI network topological features, and information flow.
Implemented a gating mechanism within the SGAD model to enhance noise robustness.

Main Results:

SGAD achieved high performance on 11 independent and 1 combined dataset, with Area Under the Curve (AUC) values ranging from 0.83 to 0.93.
The SGAD model outperformed other existing prediction models.
An SGAD ensemble effectively captured characteristics of protein pairs via a two-layer neural network.

Conclusions:

The SGAD model represents a significant advancement in protein-protein interaction network reconstruction.
SGAD offers a robust and accurate tool for exploring the complex biological space of PPIs.
This framework has the potential to deepen our understanding of biological events and disease pathogenesis.