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  3. @tome 3.0: Interfacing Protein Structure Modeling And Ligand Docking.
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  3. @tome 3.0: Interfacing Protein Structure Modeling And Ligand Docking.

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@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking.

Jean-Luc Pons1, Victor Reys1, François Grand1

  • 1A.B.C.I.S, CNRS UMR5048 - INSERM U1054 - Université de Montpellier 29, Rue de Navacelles, 34090 Montpellier Cedex, France.

Journal of Molecular Biology
|September 5, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

The @TOME-3 pipeline enhances drug design by integrating protein structure modeling with flexible ligand docking. This allows for rapid and efficient virtual screening across multiple receptor conformations, improving the accuracy of protein-ligand complex prediction.

Keywords:
chemoinformaticscomparative dockingdrug designhomology modellingligand docking

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Area of Science:

  • Computational chemistry and structural biology.
  • Drug discovery and molecular modeling.

Background:

  • Accurate prediction of protein-ligand complexes is crucial for effective drug design.
  • Existing virtual docking methods struggle with balancing speed and accuracy due to protein flexibility.
  • Simulating protein flexibility using conformation ensembles is computationally intensive and often disconnected from ligand screening.

Purpose of the Study:

  • To present @TOME-3, an updated pipeline integrating protein structure modeling with flexible ligand docking.
  • To enable efficient and straightforward ligand screening across multiple receptor conformations.
  • To improve the speed and accuracy of virtual docking for drug design.

Main Methods:

  • Utilizing sequence-profile comparisons to identify PDB templates for protein structure modeling.
  • Deducing binding sites from template ligands for targeted screening.
  • Employing PLANTS for virtual docking with optional pharmacophoric restraints from bound ligands.
  • Analyzing docking poses using diverse chemoinformatics functions.
  • Running parallel docking on multiple receptor conformations.

    • Main Results:

    • The @TOME-3 pipeline directly interfaces protein structure modeling with flexible ligand docking.
    • It enables the use of bound ligands as pharmacophoric restraints during docking.
    • The system facilitates rapid and efficient parallel ligand docking on multiple receptor conformations.
    • Sequence-profile comparisons effectively identify suitable templates and deduce binding sites.

    Conclusions:

    • @TOME-3 offers a unique combination of tools for efficient and rapid virtual ligand docking.
    • The pipeline addresses the challenge of protein flexibility in docking by enabling screening on multiple conformations.
    • This integrated approach facilitates faster and more accurate prediction of protein-ligand complexes for drug design.