What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets?

Zhonghua Xia, Pavel Karpov, Grzegorz Popowicz, Michael Sattler, Igor V. Tetko

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo-apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins.

Original languageEnglish
Article number8
JournalInformatics
Volume9
Issue number1
DOIs
StatePublished - Mar 2022
Externally publishedYes

Keywords

  • Inducer
  • Ligand
  • Machine learning
  • Pocket change
  • Pocket volume

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