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Related Concept Videos

Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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 the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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 the...
Ligand Binding Sites02:40

Ligand Binding Sites

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...
Ligand Binding Sites02:40

Ligand Binding Sites

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...
Conserved Binding Sites01:49

Conserved Binding Sites

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 analyses the...
Nucleic Acid Structure01:25

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA has a double-helix structure. The...

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Related Experiment Video

Updated: Jun 10, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Exploring ligand flexibility in nucleic acid scaffolds using graph neural networks.

Chengwei Zeng1, Jiaming Gao1, Haoquan Liu1

  • 1Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.

Biophysical Journal
|June 9, 2026
PubMed
Summary

We developed ZHMolLigGraph, a deep learning framework that models ligand flexibility for nucleic acid interactions. This approach significantly improves the accuracy of docking poses compared to traditional methods.

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Last Updated: Jun 10, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Published on: June 20, 2025

Exploring Protein-Glycan Interactions: Advances in Nuclear Magnetic Resonance
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Exploring Protein-Glycan Interactions: Advances in Nuclear Magnetic Resonance

Published on: August 26, 2025

Area of Science:

  • Computational biology
  • Drug discovery
  • Molecular modeling

Background:

  • Nucleic acid-ligand interactions are crucial for gene regulation and therapeutic strategies.
  • Accurate modeling is challenging due to flexible binding sites and ligand conformational changes.
  • Current docking methods often treat ligands and nucleic acids as rigid, limiting the exploration of realistic binding poses.

Purpose of the Study:

  • To develop a novel computational framework, ZHMolLigGraph, for enhanced modeling of nucleic acid-ligand interactions.
  • To explicitly incorporate ligand flexibility into the docking process.
  • To improve the accuracy and efficiency of identifying near-native binding poses.

Main Methods:

  • A two-stage, graph-based deep learning framework named ZHMolLigGraph was designed.
  • Phase I (Flexibility Exploration) uses iterative atomic displacements to explore ligand conformational space.
  • Phase II (Feasibility Selection) filters poses based on geometric and interaction plausibility, prioritizing physically reasonable conformations.

Main Results:

  • ZHMolLigGraph demonstrated significant improvements in near-native hit rates, ranging from 10.30% to 30.91% across benchmarks.
  • The framework maintained computational efficiency compared to conventional docking algorithms.
  • The method effectively distinguishes correct poses from decoys by focusing on physically plausible conformations.

Conclusions:

  • ZHMolLigGraph offers a practical and scalable solution for modeling ligand flexibility in nucleic acid-ligand systems.
  • The framework enhances the accuracy of docking pose prediction for gene regulation and drug development.
  • Future extensions can incorporate receptor flexibility for more comprehensive modeling of biological systems.