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Related Concept Videos

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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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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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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The Equilibrium Binding Constant and Binding Strength02:18

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Cooperative Allosteric Transitions01:58

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Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
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Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
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Related Experiment Video

Updated: May 16, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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SAIAME: Semi-Parameter Adaptation Information-Assisted Multi-Objective Evolutionary for Protein-Ligand Docking.

Wei Xiao1, Haichuan Shu1, Chen Xu1

  • 1School of Electronic and Information, Shanghai Dianji University, Shanghai, China.

Chemical Biology & Drug Design
|April 3, 2025
PubMed
Summary

A new computational method, SAIAME, improves protein-ligand docking by optimizing conformational sampling. This approach enhances accuracy in predicting drug molecule binding poses and affinities for structure-based drug discovery.

Keywords:
gradient enhancementmulti‐objective evolutionpopulation size reductionprotein‐ligand dockingsemi‐parameter adaptation

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Molecular docking is crucial for structure-based drug discovery, simulating drug-protein interactions.
  • High ligand connectivity and dimensionality pose challenges to conformational sampling in docking.
  • Existing methods struggle with searchability and efficiency in complex molecular docking scenarios.

Purpose of the Study:

  • To introduce a novel semi-parameter adaptation information-assisted multi-objective evolution method (SAIAME) for protein-ligand docking optimization.
  • To enhance the search ability and efficiency of conformational sampling in molecular docking.
  • To improve the accuracy of predicting drug-target binding poses and affinities.

Main Methods:

  • SAIAME utilizes a staged, dynamic semi-parameter adaptive updating strategy for crossover rate and scaling factor.
  • Incorporates gradient enhancement using infinity norms to improve learning rate stability and outlier handling.
  • Employs a population size reduction strategy with linear and bilateral symmetric sawtooth functions for efficiency.

Main Results:

  • SAIAME achieved 87.02% accuracy for the best poses and 72.98% for top-score poses within a 2 Å RMSD.
  • Demonstrated significant advantages in execution efficiency compared to existing methods.
  • Successfully addressed challenges related to high ligand connectivity and dimensionality in docking.

Conclusions:

  • SAIAME represents a significant advancement in computational methods for protein-ligand docking.
  • The method offers improved accuracy and efficiency for structure-based drug discovery.
  • SAIAME's adaptive strategies and optimization techniques enhance the reliability of molecular docking simulations.