Quantitative analysis of known drug–target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large—which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug–target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.

Human dopamine (DA) transporter (hDAT) regulates dopaminergic signaling in the central nervous system by maintaining the synaptic concentration of DA at physiological levels, upon reuptake of DA into presynaptic terminals. DA translocation involves the co-transport of two sodium ions and the channeling of a chloride ion, and it is achieved via alternating access between outward-facing (OF) and inward-facing states of DAT. hDAT is a target for addictive drugs, such as cocaine, amphetamine (AMPH), and therapeutic antidepressants. Our recent quantitative systems pharmacology study suggested that orphenadrine (ORPH), an anticholinergic agent and anti-Parkinson drug, might be repurposable as a DAT drug. Previous studies have shown that DAT-substrates like AMPH or -blockers like cocaine modulate the function of DAT in different ways. However, the molecular mechanisms of modulation remained elusive due to the lack of structural data on DAT. The newly resolved DAT structure from Drosophila melanogaster opens the way to a deeper understanding of the mechanism and time evolution of DAT–drug/ligand interactions. Using a combination of homology modeling, docking analysis, molecular dynamics simulations, and molecular biology experiments, we performed a comparative study of the binding properties of DA, AMPH, ORPH, and cocaine and their modulation of hDAT function. Simulations demonstrate that binding DA or AMPH drives a structural transition toward a functional form predisposed to translocate the ligand. In contrast, ORPH appears to inhibit DAT function by arresting it in the OF open conformation. The analysis shows that cocaine and ORPH competitively bind DAT, with the binding pose and affinity dependent on the conformational state of DAT. Further assays show that the effect of ORPH on DAT uptake and endocytosis is comparable to that of cocaine.

α1-Antitrypsin deficiency (ATD) is a common genetic disorder that can lead to end-stage liver and lung disease. Although liver transplantation remains the only therapy currently available, manipulation of the proteostasis network (PN) by small molecule therapeutics offers great promise. To accelerate the drug-discovery process for this disease, we first developed a semi-automated high-throughput/content-genome-wide RNAi screen to identify PN modifiers affecting the accumulation of the α1-antitrypsin Z mutant (ATZ) in a Caenorhabditis elegans model of ATD. We identified 104 PN modifiers, and these genes were used in a computational strategy to identify human ortholog-ligand pairs. Based on rigorous selection criteria, we identified four FDA-approved drugs directed against four different PN targets that decreased the accumulation of ATZ in C. elegans. We also tested one of the compounds in a mammalian cell line with similar results. This methodology also proved useful in confirming drug targets in vivo, and predicting the success of combination therapy. We propose that small animal models of genetic disorders combined with genome-wide RNAi screening and computational methods can be used to rapidly, economically and strategically prime the preclinical discovery pipeline for rare and neglected diseases with limited therapeutic options.