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Predicting ligand binding to proteins by affinity fingerprinting

L M Kauvar1, D L Higgins, H O Villar

  • 1Terrapin Technologies, Inc., South San Francisco, CA 94080, USA.

Chemistry & Biology
|February 1, 1995
PubMed
Summary

This study introduces affinity fingerprints, a novel molecular representation based on protein binding potency, to predict compound activity. This method enables efficient drug discovery by identifying promising compounds through computational analysis and limited empirical testing.

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Traditional methods for predicting molecular properties rely on physical representations like chemical structures or spectra.
  • Predicting biological activity often depends on these established molecular representations.
  • A novel approach uses compound binding potency against diverse proteins to create 'affinity fingerprints'.

Purpose of the Study:

  • To introduce and validate the concept of affinity fingerprints for molecular representation.
  • To demonstrate the utility of affinity fingerprints in predicting compound binding properties against new proteins.
  • To explore shared binding interaction patterns across diverse proteins.

Main Methods:

  • Generated affinity fingerprints for 122 diverse compounds using a reference panel of eight proteins.

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  • Applied multivariate regression to create computational surrogates for new proteins.
  • Validated the predictive power of these surrogates on various enzymes and a large compound library.

    • Main Results:

    • Affinity fingerprints uniquely identified about 75% of tested small organic compounds.
    • Computational surrogates effectively predicted binding potencies for new proteins, even those structurally dissimilar to the reference panel.
    • The method successfully predicted several sub-micromolar hits in a library of 5000 compounds.

    Conclusions:

    • Affinity fingerprint databases offer insights into protein similarities and can challenge existing structural homology theories.
    • This approach facilitates efficient pre-screening of large compound libraries for drug design.
    • Combining fingerprint similarities with minimal empirical testing aids in selecting promising drug candidates.