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  3. Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors

Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors

  • Eur J Med Chem. 2023 Jan 15;246:114980. doi: 10.1016/j.ejmech.2022.114980.
Alba L Montoya 1,  Marta Glavatskikh 2,  Brayden J Halverson 3,  Lik Hang Yuen 4,  Herwig Schüler 5,  Dmitri Kireev 6,  Raphael M Franzini 7
Affiliations

Affiliations

  • 1 Department of Medicinal Chemistry, Skaggs College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84112, USA. Electronic address: alba.montoya@pharm.utah.edu.
  • 2 Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, University of North Carolina, Chapel Hill, NC, 27599, USA. Electronic address: mglv@email.unc.edu.
  • 3 Department of Medicinal Chemistry, Skaggs College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84112, USA. Electronic address: brayden.halverson@pharm.utah.edu.
  • 4 Department of Medicinal Chemistry, Skaggs College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84112, USA.
  • 5 Center for Molecular Protein Science, Department of Chemistry, Lund University, 22100, Lund, Sweden. Electronic address: herwig.schuler@biochemistry.lu.se.
  • 6 Department of Chemistry, 36 Schlundt Hall, University of Missouri, Columbia, MO, 65211, USA. Electronic address: dmitri.kireev@missouri.edu.
  • 7 Department of Medicinal Chemistry, Skaggs College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84112, USA; Huntsman Cancer Institute, University of Utah, 2000 Circle of Hope Dr., Salt Lake City, UT, 84112, USA. Electronic address: raphael.franzini@utah.edu.
PMID: 36495630 DOI: 10.1016/j.ejmech.2022.114980
Abstract

DNA-encoded chemical libraries (DECLs) interrogate the interactions of a target of interest with vast numbers of molecules. DECLs hence provide abundant information about the chemical ligand space for therapeutic targets, and there is considerable interest in methods for exploiting DECL screening data to predict novel ligands. Here we introduce one such approach and demonstrate its feasibility using the cancer-related poly-(ADP-ribose)transferase tankyrase 1 (TNKS1) as a model target. First, DECL affinity selections resulted in structurally diverse TNKS1 inhibitors with high potency including compound 2 with an IC50 value of 0.8 nM. Additionally, TNKS1 hits from four DECLs were translated into pharmacophore models, which were exploited in combination with docking-based screening to identify TNKS1 ligand candidates in databases of commercially available compounds. This computational strategy afforded TNKS1 inhibitors that are outside the chemical space covered by the DECLs and yielded the drug-like lead compound 12 with an IC50 value of 22 nM. The study further provided insights in the reliability of screening data and the effect of library design on hit compounds. In particular, the study revealed that while in general DECL screening data are in good agreement with off-DNA ligand binding, unpredictable interactions of the DNA-attachment linker with the target protein contribute to the noise in the affinity selection data.

Keywords

Computer-assisted drug discovery; DNA-encoded chemical libraries; Hit-to-lead development; Pharmacophores; TNKS1 inhibitors; Virtual screening.

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