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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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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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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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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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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.
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.

Xiaozhe Wan1, Xiaolong Wu2, Dingyan Wang1

  • 1State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China.

Briefings in Bioinformatics
|March 11, 2022
PubMed
Summary

This study introduces CPI-IGAE, a novel framework for predicting compound-protein interactions (CPIs) by transforming complex heterogeneous graphs into simpler homogeneous ones. This approach enhances drug development efficiency and accuracy.

Keywords:
compound–protein interaction predictionend-to-end learninghomogeneous graphinductive graph neural network

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Area of Science:

  • Computational biology
  • Drug discovery
  • Graph representation learning

Background:

  • Compound-protein interactions (CPIs) are crucial for drug development.
  • Computational methods, particularly graph representation learning, accelerate CPI prediction.
  • Existing network-based methods face challenges with complex heterogeneous graphs.

Purpose of the Study:

  • To develop an efficient computational framework for predicting compound-protein interactions (CPIs).
  • To address the limitations of heterogeneous graphs in network-based CPI prediction methods.
  • To improve the accuracy and reduce the cost of drug development through enhanced CPI prediction.

Main Methods:

  • Transformed compound-protein heterogeneous graphs into homogeneous graphs.
  • Integrated ligand-based protein representations and similarity associations.
  • Proposed an Inductive Graph AggrEgator-based framework (CPI-IGAE) for end-to-end learning of low-dimensional compound and protein representations.

Main Results:

  • CPI-IGAE demonstrated superior performance compared to state-of-the-art methods.
  • Ablation studies and embedding visualization confirmed the model's architectural advantages and feature extraction capabilities.
  • Top-ranked CPIs predicted by CPI-IGAE were validated through literature review.

Conclusions:

  • The proposed CPI-IGAE framework effectively predicts compound-protein interactions using a simplified graph structure.
  • The method offers a promising approach to accelerate drug discovery and development.
  • The model's performance and validated predictions highlight its potential in bioinformatics and pharmaceutical research.