Structure of the human PKD1-PKD2 complex.

Mutations in two genes, PKD1 and PKD2, account for most cases of autosomal dominant polycystic kidney disease, one of the most common monogenetic disorders. Here we report the 3.6-angstrom cryo-electron microscopy structure of truncated human PKD1-PKD2 complex assembled in a 1:3 ratio. PKD1 contains a voltage-gated ion channel (VGIC) fold that interacts with PKD2 to form the domain-swapped, yet noncanonical, transient receptor potential (TRP) channel architecture. The S6 helix in PKD1 is broken in the middle, with the extracellular half, S6a, resembling pore helix 1 in a typical TRP channel. Three positively charged, cavity-facing residues on S6b may block cation permeation. In addition to the VGIC, a five-transmembrane helix domain and a cytosolic PLAT domain were resolved in PKD1. The PKD1-PKD2 complex structure establishes a framework for dissecting the function and disease mechanisms of the PKD proteins.

Cryo-EM structure of the polycystic kidney disease-like channel PKD2L1.

PKD2L1, also termed TRPP3 from the TRPP subfamily (polycystic TRP channels), is involved in the sour sensation and other pH-dependent processes. PKD2L1 is believed to be a nonselective cation channel that can be regulated by voltage, protons, and calcium. Despite its considerable importance, the molecular mechanisms underlying PKD2L1 regulations are largely unknown. Here, we determine the PKD2L1 atomic structure at 3.38 Å resolution by cryo-electron microscopy, whereby side chains of nearly all residues are assigned. Unlike its ortholog PKD2, the pore helix (PH) and transmembrane segment 6 (S6) of PKD2L1, which are involved in upper and lower-gate opening, adopt an open conformation. Structural comparisons of PKD2L1 with a PKD2-based homologous model indicate that the pore domain dilation is coupled to conformational changes of voltage-sensing domains (VSDs) via a series of π-π interactions, suggesting a potential PKD2L1 gating mechanism.

Insights into the Modulation of Dopamine Transporter Function by Amphetamine, Orphenadrine, and Cocaine Binding.

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.

Structure and mechanism of a type III secretion protease, NleC.

NleC is one of the virulence factors that is injected into infected host cells by enteropathogenic and enterohaemorrhagic Escherichia coli (EPEC and EHEC) via a needle-like protein complex called the type III secretion system (T3SS). The cytosolic NleC specifically cleaves the p65 subunit of NF-κB in the p65-p50 heterodimeric complex just after the Cys38 site in its N-terminal domain. The degradation of the remainder of the p65 C-terminal domain by the proteasome disrupts the NF-κB signalling pathway, thus dampening the host inflammatory response. Here, the crystal structure of NleC is reported at 1.55 Šresolution. In conjunction with biochemical analyses, the structure reveals that NleC is a member of the zincin zinc protease family and that the configuration of the NleC active site resembles that of the metzincin clan of metallopeptidases but without the canonical Met turn of astacin. The extended zinc-binding motif of NleC (HEXXHXXTXXXD) includes three metal ligands. The fifth zinc ligand, a conserved tyrosine (a bound water molecule is the fourth ligand), lies 45 residues downstream of the zincin motif. Furthermore, the electrostatic potential complementarity between NleC and p65 also contributes to the cleavage activity of the protease. These results not only provide important insights into the mechanism of how NleC recognizes its substrates, but also shed light on the design of new antibiotics for the food-borne diseases arising from EPEC and EHEC.

Predicting drug-target interactions using probabilistic matrix factorization.

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.