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Updated: Jul 13, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization

Garrett A Stevenson1, Dan Kirshner2, Brian J Bennion2

  • 1Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.

Journal of Chemical Information and Modeling
|October 17, 2023
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Summary

This study introduces a novel method to map protein pockets using ligand-based features, aiding drug discovery by predicting protein interactions and assessing safety. It offers a granular view of the human proteome for better therapeutic development.

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Bioinformatics

Background:

  • Protein-ligand interactions are crucial for drug discovery, influencing both therapeutic efficacy (on-target) and safety (off-target effects).
  • Current methods often characterize proteins at a broad family, function, or pathway level, lacking granular detail for precise interaction prediction.

Purpose of the Study:

  • To develop a novel ligand-based featurization and mapping approach for human protein pockets.
  • To enable identification of closely related protein targets and prediction of drug interactions within a hybrid protein-ligand feature space.

Main Methods:

  • Protein pockets are characterized using ligands that bind to their best co-complex template matches from the Protein Data Bank (PDB).
  • This structure-based template matching allows for a granular, protein-pocket level characterization of the human proteome.
  • The featurization method was applied to cluster a subset of the human proteome.

Main Results:

  • The novel featurization provides a granular characterization of protein pockets, moving beyond traditional protein-level classifications.
  • Clustering analysis demonstrated the method's ability to group related protein pockets.
  • The approach was validated by evaluating the predicted cluster associations for over 7000 compounds.

Conclusions:

  • This ligand-based featurization offers a simple, interpretable, and granular method for characterizing protein pockets.
  • The approach enhances the prediction of protein-ligand interactions, crucial for both identifying effective therapeutics and assessing potential safety concerns.
  • This method advances drug discovery by providing a more detailed understanding of the human proteome at the protein-pocket interaction level.