2. Academic Validation
  3. Discovery of a Novel DCAF1 Ligand Using a Drug-Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets

Discovery of a Novel DCAF1 Ligand Using a Drug-Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets

  • J Chem Inf Model. 2023 Jul 10;63(13):4070-4078. doi: 10.1021/acs.jcim.3c00082.
Serah W Kimani 1 2,  Julie Owen 3,  Stuart R Green 1,  Fengling Li 1,  Yanjun Li 1,  Aiping Dong 1,  Peter J Brown 1,  Suzanne Ackloo 1,  David Kuter 3,  Cindy Yang 3,  Miranda MacAskill 3,  Stephen Scott MacKinnon 3,  Cheryl H Arrowsmith 1 2 4,  Matthieu Schapira 1 5,  Vijay Shahani 3,  Levon Halabelian 1 5
Affiliations

Affiliations

  • 1 Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
  • 2 Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2C4, Canada.
  • 3 Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada.
  • 4 Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada.
  • 5 Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A1, Canada.
PMID: 37350740 DOI: 10.1021/acs.jcim.3c00082
Abstract

DCAF1 functions as a substrate recruitment subunit for the RING-type CRL4DCAF1 and the HECT family EDVPDCAF1 E3 ubiquitin ligases. The WDR domain of DCAF1 serves as a binding platform for substrate proteins and is also targeted by HIV and SIV lentiviral adaptors to induce the ubiquitination and proteasomal degradation of Antiviral host factors. It is therefore attractive both as a potential therapeutic target for the development of chemical inhibitors and as an E3 ligase that could be recruited by novel PROTACs for targeted protein degradation. In this study, we used a proteome-scale drug-target interaction prediction model, MatchMaker, combined with cheminformatics filtering and docking to identify ligands for the DCAF1 WDR domain. Biophysical screening and X-ray crystallographic studies of the predicted binders confirmed a selective ligand occupying the central cavity of the WDR domain. This study shows that artificial intelligence-enabled virtual screening methods can successfully be applied in the absence of previously known ligands.

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