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Related Experiment Videos

Ligand solvation in molecular docking.

B K Shoichet1, A R Leach, I D Kuntz

  • 1Department of Molecular Pharmacology and Biological Chemistry, Northwestern University Medical School, Chicago, Illinois 60611-3008, USA. b-shoichet@nwu.edu

Proteins
|May 21, 1999
PubMed
Summary
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Accounting for ligand solvation improves drug discovery by refining binding energy calculations. This method helps identify suitable drug candidates by correcting for charge and size, leading to more accurate inhibitor screening.

Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Drug discovery

Background:

  • Ligand solvation significantly impacts ligand-protein binding energy calculations.
  • Ignoring solvation can lead to the selection of unsuitable drug candidates (e.g., overly charged or large molecules) in structure-based inhibitor discovery.
  • Accurate solvation energy calculations are crucial for reliable virtual screening.

Purpose of the Study:

  • To develop and apply corrections for ligand solvation free energies in virtual screening.
  • To improve the accuracy of ligand-receptor interaction energy calculations.
  • To enhance the identification of complementary ligands for proteins of known structure.

Main Methods:

  • Calculated electrostatic solvation free energy using a modified Born equation.

Related Experiment Videos

  • Calculated non-polar solvation free energy based on ligand surface area and hydration parameters.
  • Subtracted solvation energies from ligand-receptor interaction energies.
  • Screened the Available Chemicals Directory (ACD) against three protein targets using molecular docking.

    • Main Results:

    • The inclusion of solvation corrections improved the ranking of known ligands.
    • The method effectively discriminated against molecules with inappropriate charge states and sizes.
    • Virtual screening accuracy was enhanced by considering both electrostatic and non-polar solvation effects.

    Conclusions:

    • Ligand solvation corrections are essential for accurate structure-based inhibitor discovery.
    • This approach refines virtual screening by accounting for molecular properties like charge and size.
    • The developed method offers a more reliable way to identify potential drug candidates.