The class A adenosine A receptor (AR) is a G-protein-coupled receptor that preferentially couples to inhibitory G heterotrimeric G proteins, has been implicated in numerous diseases, yet remains poorly targeted. Here we report the 3.6 Å structure of the human AR in complex with adenosine and heterotrimeric G protein determined by Volta phase plate cryo-electron microscopy. Compared to inactive AR, there is contraction at the extracellular surface in the orthosteric binding site mediated via movement of transmembrane domains 1 and 2. At the intracellular surface, the G protein engages the AR primarily via amino acids in the C terminus of the Gα α5-helix, concomitant with a 10.5 Å outward movement of the AR transmembrane domain 6. Comparison with the agonist-bound β adrenergic receptor-G-protein complex reveals distinct orientations for each G-protein subtype upon engagement with its receptor. This active AR structure provides molecular insights into receptor and G-protein selectivity.

The adenosine A receptor (A-AR) is a G-protein-coupled receptor that plays a vital role in cardiac, renal, and neuronal processes but remains poorly targeted by current drugs. We determined a 3.2 Å crystal structure of the A-AR bound to the selective covalent antagonist, DU172, and identified striking differences to the previously solved adenosine A receptor (A-AR) structure. Mutational and computational analysis of A-AR revealed a distinct conformation of the second extracellular loop and a wider extracellular cavity with a secondary binding pocket that can accommodate orthosteric and allosteric ligands. We propose that conformational differences in these regions, rather than amino-acid divergence, underlie drug selectivity between these adenosine receptor subtypes. Our findings provide a molecular basis for AR subtype selectivity with implications for understanding the mechanisms governing allosteric modulation of these receptors, allowing the design of more selective agents for the treatment of ischemia-reperfusion injury, renal pathologies, and neuropathic pain.

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Drug-resistant tuberculosis is a global health problem that hinders the progress of tuberculosis eradication programs. Accurate and early detection of drug-resistant tuberculosis is essential for effective patient care, for preventing tuberculosis spread, and for limiting the development of drug-resistant strains. Culture-based drug susceptibility tests are the gold standard method for the detection of drug-resistant tuberculosis, but they are time-consuming and technically challenging, especially in low- and middle-income countries. Nowadays, different nucleic acid-based assays that detect gene mutations associated with resistance to drugs used to treat tuberculosis are available. These tests vary in type and number of targets and in sensitivity and specificity. In this review, we will describe the available molecular tests for drug-resistant tuberculosis detection and discuss their advantages and limitations.

Despite intense interest in designing positive allosteric modulators (PAMs) as selective drugs of the adenosine A1 receptor (A1AR), structural binding modes of the receptor PAMs remain unknown. Using the first X-ray structure of the A1AR, we have performed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) technique to determine binding modes of the A1AR allosteric drug leads. Two prototypical PAMs, PD81723 and VCP171, were selected. Each PAM was initially placed at least 20 Å away from the receptor. Extensive GaMD simulations using the AMBER and NAMD simulation packages at different acceleration levels captured spontaneous binding of PAMs to the A1AR. The simulations allowed us to identify low-energy binding modes of the PAMs at an allosteric site formed by the receptor extracellular loop 2 (ECL2), which are highly consistent with mutagenesis experimental data. Furthermore, the PAMs stabilized agonist binding in the receptor. In the absence of PAMs at the ECL2 allosteric site, the agonist sampled a significantly larger conformational space and even dissociated from the A1AR alone. In summary, the GaMD simulations elucidated structural binding modes of the PAMs and provided important insights into allostery in the A1AR, which will greatly facilitate the receptor structure-based drug design.

The adenosine A receptor (AAR) is a potential novel therapeutic target for myocardial ischemia-reperfusion injury. However, to date, clinical translation of prototypical AAR agonists has been hindered due to dose limiting adverse effects. Recently, we demonstrated that the biased bitopic agonist 1, consisting of an adenosine pharmacophore linked to an allosteric moiety, could stimulate cardioprotective AAR signaling in the absence of unwanted bradycardia. Therefore, this study aimed to investigate the structure-activity relationship of compound 1 biased agonism. A series of novel derivatives of 1 were synthesized and pharmacologically profiled. Modifications were made to the orthosteric adenosine pharmacophore, linker, and allosteric 2-amino-3-benzoylthiophene pharmacophore to probe the structure-activity relationships, particularly in terms of biased signaling, as well as AAR activity and subtype selectivity. Collectively, our findings demonstrate that the allosteric moiety, particularly the 4-(trifluoromethyl)phenyl substituent of the thiophene scaffold, is important in conferring bitopic ligand bias at the AAR.

This article is part of a themed section on Molecular Pharmacology of GPCRs. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.21/issuetoc.

For the site-specific prescription of fertilizer topdressing in rice cultivation, a non-destructive diagnosis of the rice growth and nutrition status is necessary. Three experiments were done to develop and test a model using canopy reflectance for the nondestructive diagnosis of plant growth and N status in rice. Two experiments for model development were conducted, one in 2000 and another in 2003 in Suwon, Korea, including two rice varieties and four nitrogen (N) rates in 2000 and four rice varieties and 10 N treatments in 2003. Hyperspectral canopy reflectance (300-1,100 nm) data recorded at various growth stages before heading were used to develop a partial least squares regression (PLS) model to calculate plant biomass and N nutrition status. The 342 observations were split for model calibration (75%) and validation (25%). The PLS model was then tested to calculate within-field statistical variation of four crop variables: shoot dry weight (SDW), shoot N concentration (SN), shoot N density (SND) and N nutrition index (NNI) using measured canopy reflectance data from a field of 6,500 m 2 in 2004. Results showed that PLS regression using logarithm reflectance had better performance than both the PLS and multiple stepwise linear regression (MSLR) models using original reflectance data to calculate the four plant variables in year 2000 and 2003. It produced values with an acceptable model coefficient of determination (R 2 ) and relative error of calculation (REC). The model R 2 and REC ranged from .83 to .89 and 13.4% to 22.8% for calibration, and .76 to .87 and 14.0% to 24.4% for validation, respectively. The PLS regression model R 2 was reduced in the test data of year 2004 but the root mean square error of calculation (RMSEC) was smaller, suggesting that the PLS regression model using 123 canopy reflectance data could be a promising method to calculate within-field spatial variation of rice crop growth and N status.