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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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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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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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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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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Protein Organization01:24

Protein Organization

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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.
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Updated: Jul 5, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.

Jia Guo1,2

  • 1Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Beijing, P.R. China.

Plos One
|January 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces LGN, a graph neural network model that improves protein-ligand binding affinity prediction by extracting distinct ligand features. This approach enhances accuracy in drug discovery compared to methods treating all molecules uniformly.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning in biochemistry

Background:

  • Predicting protein-ligand binding affinity is crucial for accelerating drug discovery.
  • Existing graph neural network methods may overlook data heterogeneity between proteins and ligands.
  • Incomplete biochemical information from ligands can limit prediction accuracy.

Purpose of the Study:

  • To develop an advanced graph neural network model for enhanced protein-ligand binding affinity prediction.
  • To address the limitations of existing methods by incorporating specific ligand feature extraction.
  • To improve the exploration of biochemical information within protein-ligand complexes.

Main Methods:

  • Proposed LGN, a graph neural network-based fusion model incorporating extra ligand feature extraction.
  • Utilized interaction fingerprints alongside ligand-based features.
  • Employed ensemble learning to enhance model robustness against data similarity.

Main Results:

  • LGN achieved a Pearson correlation coefficient of 0.842 on the PDBbind 2016 core set, outperforming complex graph features alone (0.807).
  • Demonstrated superior performance compared to state-of-the-art baseline methods.
  • Verified model rationalization and generalization through comprehensive experiments.

Conclusions:

  • The proposed LGN model effectively captures local and global features of protein-ligand complexes.
  • Integrating ligand-specific features and interaction fingerprints significantly improves binding affinity prediction accuracy.
  • The model shows strong generalization and robustness, validating its superiority in the field.