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Related Concept Videos

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...
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...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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...

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Related Experiment Video

Updated: Jul 16, 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

FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.

Jing Li1, Ruiqiang Lu1, Yi Tan1

  • 1Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

Acta Pharmaceutica Sinica. B
|July 15, 2026
PubMed
Summary

FlowDock, a new deep learning framework, accurately predicts protein-ligand complex structures and binding affinity, addressing limitations of traditional methods and enhancing drug design. It incorporates protein flexibility for more reliable results.

Keywords:
Bayesian flow networksBinding affinity predictionDeep equivariant generative modelsLatent space optimizationMachine learning in drug discoveryProtein–ligand dockingStructure-based drug design

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Related Experiment Videos

Last Updated: Jul 16, 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

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Accurate prediction of protein-ligand interactions is crucial for structure-based drug design.
  • Traditional docking methods are computationally expensive and may not fully capture protein flexibility.
  • Existing deep learning methods can lack physicochemical validity and ignore protein dynamics.

Purpose of the Study:

  • To develop a cost-effective and accurate deep learning framework for predicting protein-ligand complexes and binding affinity.
  • To address the limitations of traditional docking and current deep learning approaches, particularly regarding protein flexibility and physicochemical validity.
  • To accelerate hit identification and optimization in drug design.

Main Methods:

  • Proposed FlowDock, a multitask framework utilizing Bayesian Flow Networks.
  • Employed multimodal intramolecular representations and a deep equivariant generative model.
  • Iteratively refined protein-ligand complexes in latent space for rapid and stable generation.
  • Incorporated protein conformational flexibility into the generative process.

Main Results:

  • Achieved state-of-the-art performance in binding pose prediction, emphasizing physical plausibility.
  • Demonstrated superior virtual screening capabilities compared to existing methods.
  • Provided reliable predictions of binding affinity.
  • Showcased enhanced molecular insights into dynamic protein-ligand interactions.

Conclusions:

  • FlowDock offers a robust and efficient alternative to traditional docking for structure-based drug design.
  • The framework successfully integrates protein flexibility and improves the physicochemical validity of generated complexes.
  • FlowDock accelerates the rational development of therapeutics by providing accurate predictions and deeper molecular insights.