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Fast structure-based virtual ligand screening combining FRED, DOCK, and Surflex.

Maria A Miteva1, Wen H Lee, Matthieu O Montes

  • 1INSERM U648, University Paris 5, Paris 75006, France.

Journal of Medicinal Chemistry
|September 16, 2005
PubMed
Summary
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This study combined FRED, DOCK, and Surflex for virtual ligand screening (VLS) of protein targets. The protocol efficiently screens large compound libraries, demonstrating the value of consensus docking for drug discovery.

Area of Science:

  • Computational chemistry
  • Structural bioinformatics
  • Drug discovery

Background:

  • Virtual ligand screening (VLS) is crucial for identifying potential drug candidates.
  • Accurate docking and scoring are essential for effective VLS.
  • Integrating freely available computational tools can enhance VLS accessibility.

Purpose of the Study:

  • To develop and evaluate a multistep VLS protocol using FRED, DOCK, and Surflex.
  • To assess the impact of combining these tools on docking/scoring accuracy and computational time.
  • To determine the efficiency of this protocol for screening large compound libraries against protein targets.

Main Methods:

  • A multistep VLS protocol was designed, integrating FRED, DOCK, and Surflex.
  • A compound bank of 65,660 molecules, including 49 known actives, was screened.

Related Experiment Videos

  • Docking/scoring parameters were optimized for specific binding pockets.
  • Shape-based filtering was employed before flexible docking.

    • Main Results:

    • The VLS protocol demonstrated successful screening of four different protein targets.
    • Enrichment factors achieved were comparable to those reported in recent studies.
    • The protocol processed the entire compound bank for one receptor in less than a week on a single processor.
    • Combining freely available packages impacted docking/scoring accuracy and CPU time.

    Conclusions:

    • Consensus docking and scoring approaches can be beneficial for drug discovery projects.
    • The developed VLS protocol is computationally efficient, requiring minimal resources.
    • This approach enhances the feasibility of VLS for academic researchers without extensive computational infrastructure.