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A Workflow to Create a High-Quality Protein-Ligand Binding Dataset for Training, Validation, and Prediction Tasks.

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This study reveals structural artifacts in the PDBbind dataset, impacting scoring function (SF) development. A new workflow, HiQBind-WF, and dataset, HiQBind, were created to improve protein-ligand binding energy prediction accuracy.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Scoring functions (SFs) are crucial for predicting protein-ligand binding energies.
  • High-quality structural and binding assay data are essential for developing accurate SFs.
  • Existing datasets like PDBbind contain structural artifacts that can compromise SF reliability.

Purpose of the Study:

  • To identify and address structural artifacts in protein-ligand datasets.
  • To develop a semi-automated workflow (HiQBind-WF) for curating these datasets.
  • To create a new, high-quality dataset (HiQBind) for SF development and validation.

Main Methods:

  • Developed a series of algorithms for a semi-automated curation workflow (HiQBind-WF).
  • Applied HiQBind-WF to curate non-covalent protein-ligand datasets.
  • Integrated binding free energy data from BioLiP, Binding MOAD, and BindingDB with PDB structures to create HiQBind.

Main Results:

  • Identified common structural artifacts in widely-used protein-ligand datasets.
  • Demonstrated the potential of these artifacts to reduce the accuracy and generalizability of SFs.
  • Successfully created the HiQBind dataset and workflow, ensuring reproducibility and minimizing human intervention.

Conclusions:

  • The HiQBind-WF and HiQBind dataset offer a more reliable resource for developing and testing SFs.
  • Addressing data quality issues is critical for advancing protein-ligand binding energy prediction.
  • The open-source nature of HiQBind promotes transparency and collaboration in drug discovery research.