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.

Synthesis of Carvone Derivatives and In Silico and In Vitro Screening of Anti-Inflammatory Activity in Murine Macrophages.

The chemical modification of natural compounds is a promising strategy to improve their frequently poor bioavailability and low potency. This study aimed at synthesizing chemical derivatives of carvone, a natural monoterpene with anti-inflammatory properties, which we recently identified, and evaluating their potential anti-inflammatory activity. Fourteen chemical derivatives of carvone were synthesized, purified and their chemical structures confirmed. Noncytotoxic concentrations of the test compounds were selected based on the resazurin reduction assay. Among the tested compounds, four significantly reduced the lipopolysaccharides-induced protein levels of the inducible isoform of the nitric oxide synthase and nitric oxide production and showed a dual effect on pro-IL-1 protein levels in the Raw 264.7 cell line. The Ligand Express drug discovery platform was used to predict the targets of the test compounds, and an enrichment analysis was performed to group the different biological processes and molecular and cellular functions of the tested compounds. Moreover, Ligand Express also predicted that all chemicals evaluated have intestinal and blood-brain barrier permeability, do not inhibit P-gp and do not interact with major receptors. Although presenting anti-inflammatory and some advantageous ADME properties, the tested compounds still have low potency and specificity but may provide novel structures the further chemical modification of which may yield more promising drugs.

Proteome-Scale Drug-Target Interaction Predictions: Approaches and Applications.

Drug-Target interaction predictions are an important cornerstone of computer-aided drug discovery. While predictive methods around individual targets have a long history, the application of proteome-scale models is relatively recent. In this overview, we will provide the context required to understand advances in this emerging field within computational drug discovery, evaluate emerging technologies for suitability to given tasks, and provide guidelines for the design and implementation of new drug-target interaction prediction models. We will discuss the validation approaches used, and propose a set of key criteria that should be applied to evaluate their validity. We note that we find widespread deficiencies in the existing literature, making it difficult to judge the practical effectiveness of some of the techniques proposed from their publications alone. We hope that this review may help remedy this situation and increase awareness of several sources of bias that may enter into commonly used cross-validation methods. © 2021 Cyclica Inc. Current Protocols published by Wiley Periodicals LLC.