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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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DockOpt: A Tool for Automatic Optimization of Docking Models.

Ian S Knight1, Olivier Mailhot1, Khanh G Tang1

  • 1Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States.

Journal of Chemical Information and Modeling
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

DockOpt automates molecular docking model creation and optimization, making drug discovery more accessible. This new utility significantly improves performance, enabling broader use in screening for new therapeutics.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Molecular docking is crucial for ligand discovery but is limited by complexity and requires expert knowledge.
  • Existing automation tools for molecular docking have not fully addressed these accessibility challenges.

Purpose of the Study:

  • To develop an automated utility, DockOpt, for creating, evaluating, and optimizing molecular docking models.
  • To enhance the accessibility and efficiency of molecular docking for a wider range of researchers.

Main Methods:

  • Development of DockOpt, a novel automated pipeline for docking model generation.
  • Evaluation of DockOpt using the DUDE-Z benchmark dataset across 43 targets.
  • Assessment of model performance based on enrichment factors and normalized LogAUC values.

Main Results:

  • DockOpt demonstrated superior performance compared to previous automated pipelines across all 43 DUDE-Z targets.
  • Generated docking models for 84% of targets showed sufficient enrichment (normalized LogAUC ≥ 15%) for prospective screening.
  • The utility is integrated into the Pydock3 package within the UCSF DOCK 3.8 distribution.

Conclusions:

  • DockOpt significantly advances automated molecular docking, improving accessibility and performance.
  • The tool's effectiveness in generating high-quality docking models supports its use in large-scale drug discovery efforts.
  • DockOpt is freely available to academic researchers and registered users, promoting wider adoption in the scientific community.