Name: Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis (Video) | JoVE Journal
Uploaded: 2025-06-20T13:10:00.000+00:00
Duration: 8 min 49 s
Description: Discover ensemble-based docking analysis for computational drug discovery using molecular dynamics simulations. This method incorporates protein flexibility to improve binding prediction accuracy and enhance drug design outcomes.

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5 minutes

This work was supported by Universiti Malaya RMF Grant, Project Number RMF1392-2021 from Universiti Malaya.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ensemble-based application in computational drug discovery involves utilizing numerous conformational ensembles derived from crystal structures, nuclear magnetic resonance (NMR) studies, or molecular dynamics simulations26. The exploitation of multiple computational conformations would allow the analysis to incorporate the dynamics and flexibility of a protein structure, leading to the enhancement of accuracy and reliability in identifying potential drug candidates.
To ...

In computational drug discovery (CDD), techniques from computer science, chemistry, biology, and physics are integrated to explore vast chemical space, predict drug-target interactions, and optimize drug candidates with higher efficiency and lower costs compared to traditional experimental methods alone. It is a powerful approach that leverages computational methods and algorithms to accelerate the discovery and optimization of new therapeutic compounds1,2,3. CDD has revolutionized drug discovery. However, there are limitations associated with protein three-dimensional (3D) structure dynamics that can affect the accuracy and reliability of computational predictions4. Protein 3D structures serve as templates in CDD for the design or optimize drug candidates based on the target protein-drug interactions. While X-ray crystallography models of protein structures provide valuable structural information about protein, it is essential to recognize the dynamic nature of protein structures and the limitations of static models5,6,7. In addition, recent advancements in cryo-electron microscopy (cryo-EM) and computational predictions such as AlphaFold have also greatly expanded the availability of structural data in capturing the full spectrum of protein flexibility and dynamics8,9,10,11,12.
Molecular dynamics (MD) simulations simulate the movement and interactions of atoms and molecules over time, providing insights into the dynamic behavior and flexibility of protein 3D structure13,14. MD simulations are used to generate 3D structures of protein representing various conformational states, which serve as input for ensemble-based docking analysis. Through the sampling of diverse protein conformations, ensemble-based docking analysis accounts for the inherent flexibility and dynamics of biological targets, allowing for a more comprehensive exploration of ligand binding modes and interactions3.
Understanding protein flexibility is essential, as it influences how drugs exert their biological effects, determines the location and orientation of binding sites, and affects binding kinetics, metabolism, and transport15,16. Capturing this dynamic nature can significantly enhance the accuracy and reliability of docking predictions. In 1994, Kearsley et al. introduced a flexible docking technique, a framework that models the flexibility of both ligands and proteins. This approach allows the protein&#39;s conformation to adjust during docking, improving predictions of ligand-receptor interactions by accounting for structural flexibility17. Similarly, in 1999, Carlson et al. reported on ensemble docking, which applies flexible pharmacophore modeling to both static and dynamic models of HIV-1 integrase, further highlighting the importance of accounting for protein dynamics in docking studies18. Furthermore, Cavasotto et al. also reported the improvement of ligand docking accuracy by incorporating receptor flexibility into the docking process using normal mode analysis19. More recently, ensemble-based techniques20,21,22,23,24,25,26,27have expanded drug discovery by identifying potential new ligand binding sites and providing more accurate estimates of free ligand-receptor binding energy. These advancements have been applied to targets such as quadruplex-duplex DNA20, vascular endothelial growth factor 165 (VEGF-165)21, the SARS-CoV-2 target enzyme22, human liver cytochrome P450 enzymes23, and anticancer proteins24.
Flavokawain B (FB), classified as a flavonoid, has been documented to exhibit various pharmacological properties28,29,30. Based on experimental and computational analysis, FB has been reported to form a stable complex with lysozyme (LYZ)31, a protein widely recognized for its antimicrobial activity and has also been identified as a ligand transporter32,33,34. In this report, we further analyze the nature of the interaction of FB with LYZ using ensemble-based docking analysis to incorporate the impact of protein flexibility in FB-LYZ complex formation. The goal of this method is to provide researchers with a step-by-step, repeatable process for ensemble-based docking analysis. In addition, it is advisable for researchers to select the protein structures from the target organism for research.

<table><tbody><tr><td>AutoDock</td><td>The Scripps Research Institute, US</td><td>Version 4.2.6</td><td></td></tr><tr><td>AutoDock Tools</td><td>The Scripps Research Institute, US</td><td>Version 1.5.6</td><td></td></tr><tr><td>Avogadro</td><td>Geoffrey R Hutchison, Department of Chemistry, University of Pittsburgh, Pittsburgh, USA</td><td>Version 1.95</td><td></td></tr><tr><td>Discovery Studio</td><td>Dassault Syst&amp;egrave;mes, Massachusetts, US</td><td>Version 2021</td><td></td></tr><tr><td>GROMACs</td><td>University of Groningen&lt;br/&gt; Royal Institute of Technology&lt;br/&gt; Uppsala University, Sweden</td><td>Version 2023</td><td></td></tr><tr><td>UCSF Chimera</td><td>Resource for Biocomputing, Visualization, and Informatics&lt;br/&gt; University of California</td><td>Version 1.16</td><td></td></tr></tbody></table>

incorporating target protein structure flexibility and dynamics in computational drug discovery using ensemble-based docking analysis

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

The drug discovery process is a rigorous, time-consuming, and expensive operation. The computational approach in drug discovery allows researchers to prioritize the most promising compounds for further testing, which would greatly reduce the required resources, leading to an increment of the overall efficiency in the drug discovery pipelines. Structure-based drug discovery is a common approach that requires the structural information of the target protein in a three-dimensional format. However, the current limitation of most computer-aided drug discovery strategies is their inability to introduce the flexibility and dynamics of the target protein structure during the ligand-protein docking simulation. While both induced fit docking and ensemble-based docking aim to address protein flexibility in the docking procedure, the latter can provide a more comprehensive view of dynamic protein behavior by incorporating multiple conformations throughout the simulation. In this report, we demonstrate and discuss the application of a technique called ensemble-based docking analysis that indirectly introduces the target protein structure's flexibility and dynamics in the molecular docking process. The protein and ligand selected for ensemble-based docking studies were lysozyme and Flovokawain B (FB), respectively. FB has been previously reported to have binding activity with lysozyme. A molecular dynamics (MD) simulation was performed on lysozyme in the presence of water, and the total energy, root-mean-square deviation (RMSD), and root-mean-square fluctuation (RMSF) were examined. Conformation clustering was generated based on several clustering cutoff values and was chosen for additional docking analysis with FB. Cluster no 2 gives the lowest binding energy at -29.37 kJ/mol. Molecular docking images were generated to anticipate the presence of binding forces. By incorporating the structural dynamics of the protein, the ensemble-based docking approach can better capture the range of possible binding scenarios, leading to more reliable predictions of binding outcomes.

The chemical structure and the 3D structural representation of FB after optimization are shown in Figure 8A. Figure 8B shows the 3D structure of lysozyme with pdb code 1LYZ at the initial state prior to MD simulation. In order to study the dynamics and flexibility of lysozyme 3D structure, an MD simulation for 100 ns was conducted. The total energy of the protein structure was stable during the simulation, as shown in <strong cla...

Computational methods hold promises for expediting drug discovery, yet they frequently overlook the dynamic nature of protein structures. Here, we discuss ensemble-based docking analysis to indirectly incorporate protein flexibility, potentially improving the accuracy and reliability of drug discovery efforts.

The drug discovery process is a rigorous, time-consuming, and expensive operation. The computational approach in drug discovery allows researchers to ...

incorporating-target-protein-structure-flexibility-dynamics

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Discover ensemble-based docking analysis for computational drug discovery using molecular dynamics simulations. This method incorporates protein flexibility to improve binding prediction accuracy and enhance drug design outcomes.

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis (Video) | JoVE Journal

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

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