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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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Related Experiment Video

Updated: Nov 20, 2025

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

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Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites.

Cristian Privat1, José M Granadino-Roldán2, Jordi Bonet1

  • 1Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain. jaime.rubio@ub.edu.

Physical Chemistry Chemical Physics : PCCP
|January 25, 2021
PubMed
Summary

Fragment dissolved Molecular Dynamics (fdMD) overcomes common simulation issues in fragment-based drug discovery (FBDD). This novel computational method prevents protein denaturation and ligand aggregation, improving FBDD accuracy.

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Area of Science:

  • Computational Chemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Fragment-based drug discovery (FBDD) relies on computational methods.
  • Existing methods face challenges like protein denaturation and ligand aggregation during biomolecular simulations.

Purpose of the Study:

  • Introduce a novel computational procedure, fragment dissolved Molecular Dynamics (fdMD), to address limitations in FBDD simulations.
  • Enhance the reliability and accuracy of FBDD by preventing common simulation artifacts.

Main Methods:

  • Developed a semi-automatic computational procedure utilizing simulation boxes of solvated small fragments.
  • Incorporated a repulsive Lennard-Jones potential term to prevent ligand aggregation.
  • Generated a database of ligand-solvated boxes and provided analysis scripts for binding site identification.

Main Results:

  • Successfully tested fdMD on four diverse protein systems with multiple molecular dynamics runs.
  • Demonstrated the method's ability to identify correct binding sites, even in complex cases with multiple or close-by sites.
  • Identified average MMGBSA and average KDEEP energies as robust descriptors for analyzing simulation results.

Conclusions:

  • fdMD effectively prevents protein denaturation and ligand aggregation in FBDD simulations.
  • The method reliably identifies correct binding sites, offering a significant improvement over existing computational approaches.
  • Proposed descriptors provide a trustworthy means to discard spurious binding sites, enhancing FBDD efficiency.