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Related Experiment Videos

Computational identification of proteins for selectivity assays.

Sukjoon Yoon1, Andrew Smellie, David Hartsough

  • 1ArQule, Inc., Woburn, Massachusetts 01801, USA. afilikov@arqule.com

Proteins
|March 17, 2005
PubMed
Summary

This study introduces a computational method to identify potential off-target proteins for drug development. It uses molecular probes and docking scores to find proteins with similar binding sites, improving selectivity panel construction.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Drug optimization requires assessing compound binding to target proteins and a selectivity panel.
  • Current methods for selecting selectivity panel proteins rely heavily on sequence homology, potentially missing nonhomologous targets.
  • Experimental selectivity data is often limited or unavailable during early drug development.

Purpose of the Study:

  • To develop a computational method for identifying potential off-target proteins for selectivity panels.
  • To overcome limitations of homology-based selection by focusing on binding site similarity.
  • To enable the construction of more comprehensive selectivity panels early in drug discovery.

Main Methods:

  • Developed a computational approach using docking scores of target-selected molecular probes to evaluate binding site similarity.

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  • Generated molecular probes by docking a diverse library of drug-like compounds to the target protein.
  • Applied the method to proteins with known 3D structures, independent of sequence homology or known inhibitor data.

    • Main Results:

    • The method effectively identifies proteins with similar binding sites to the target, even those lacking sequence homology.
    • Successfully rediscovered known nonhomologous protein binders for common ligands like estradiol, tamoxifen, and riboflavin.
    • Demonstrated the ability to discriminate proteins with similar binding sites from random proteins based on 3D structure.

    Conclusions:

    • The developed computational method provides an effective strategy for identifying selectivity panel proteins.
    • This approach enhances the identification of potential off-target interactions early in the drug discovery process.
    • The method is broadly applicable to any protein with a known 3D structure, facilitating more robust drug development.