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

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.