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Related Concept Videos

Protein Networks02:26

Protein Networks

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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,...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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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...
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Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Related Experiment Video

Updated: Jun 7, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D

Gaili Li1, Yongna Yuan2, Ruisheng Zhang3

  • 1School of Information science and Engineering, Lanzhou University, lanzhou, 730000, China.

Interdisciplinary Sciences, Computational Life Sciences
|November 14, 2024
PubMed
Summary

We developed PLA-STGCNnet, a novel deep learning model using 3D structural data for accurate protein-ligand interaction prediction. This fusion model enhances binding affinity prediction and shows promise for drug screening applications.

Keywords:
Attention mechanismFusion modelProtein–ligand binding affinitySpatio-temporal graph convolutions networksThree-dimensional (3D) descriptors

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Area of Science:

  • Computational Biology
  • Drug Discovery
  • Machine Learning

Background:

  • Accurate prediction of protein-ligand interactions is crucial for drug discovery.
  • Advancements in protein structure data necessitate sophisticated computational methods.
  • Existing methods often rely on simplified representations like 1D sequences or 2D graphs.

Purpose of the Study:

  • To introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network for protein-ligand interaction studies.
  • To leverage 3D structural data for a more precise representation of protein-ligand complexes.
  • To improve the accuracy and generalization of binding affinity prediction.

Main Methods:

  • Utilized a deep fusion spatial-temporal graph neural network (PLA-STGCNnet).
  • Employed 3D graph representations of protein-ligand complexes.
  • Integrated features and outputs from multiple models within a fusion framework.

Main Results:

  • PLA-STGCNnet demonstrated superior performance compared to individual algorithms in binding affinity prediction.
  • The fusion model exhibited satisfactory performance across different datasets, indicating strong generalization ability and stability.
  • Successfully applied the model to drug screening tasks.

Conclusions:

  • Deep fusion spatial-temporal graph neural networks offer a promising approach for complex protein-ligand affinity prediction.
  • PLA-STGCNnet provides a robust and accurate tool for computational drug discovery.
  • The model's performance highlights the benefits of utilizing 3D structural data and fusion methodologies.