Details

Autor(en) / Beteiligte

• Kimani, Serah W.
• Owen, Julie
• Green, Stuart R.
• Li, Fengling
• Li, Yanjun
• Dong, Aiping
• Brown, Peter J.
• Ackloo, Suzanne
• Kuter, David
• Yang, Cindy
• MacAskill, Miranda
• MacKinnon, Stephen Scott
• Arrowsmith, Cheryl H.
• Schapira, Matthieu
• Shahani, Vijay
• Halabelian, Levon

Titel

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

Ist Teil von

• Journal of chemical information and modeling, 2023-07, Vol.63 (13), p.4070-4078

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.