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Systematic identification and classification of three-dimensional activity cliffs.

Ye Hu1, Norbert Furtmann, Michael Gütschow

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Area of Science:

  • Structural Biology
  • Medicinal Chemistry
  • Computational Chemistry

Background:

  • Activity cliffs represent a critical challenge in drug discovery, where minor structural modifications lead to substantial potency changes.
  • Understanding the structural basis of these cliffs is essential for rational drug design and optimizing ligand-target interactions.

Purpose of the Study:

  • To systematically identify and characterize three-dimensional (3D) activity cliffs from public X-ray crystallographic data.
  • To analyze the ligand-target interactions underlying these cliffs and provide a dataset for further research.

Main Methods:

  • Extraction of activity cliffs from the Protein Data Bank (PDB) based on 3D similarity (≥80%) and potency difference (≥2 orders of magnitude).
  • Utilized a property density function-based method to calculate 3D similarity, considering conformational, positional, and chemical differences.
  • Detailed analysis of crystallographic interaction patterns for identified 3D cliffs.

Main Results:

  • A total of 216 well-defined 3D activity cliffs were identified, involving 269 ligands across 38 targets from 17 protein families.
  • Binding modes for 255 compounds were available at high crystallographic resolution.
  • Analysis revealed plausible origins for many cliffs based on differences in ligand-target interactions.

Conclusions:

  • The identified 3D activity cliffs provide valuable insights into structure-activity relationships at the molecular level.
  • This dataset advances the rationalization of activity cliffs and can aid in calibrating energy functions for structure-based design.
  • The provided pool of cliffs facilitates subsequent structure-based analyses and drug discovery efforts.