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G protein coupled receptors (GPCRs) exhibit varying degrees of selectivity for different G protein isoforms. Despite the abundant structures of GPCR-G protein complexes, little is known about the mechanism of G protein coupling specificity. The β2-adrenergic receptor is an example of GPCR with high selectivity for Gαs, the stimulatory G protein for adenylyl cyclase, and much weaker for the Gαi family of G proteins inhibiting adenylyl cyclase. By developing a new Gαi-biased agonist (LM189), we provide structural and biophysical evidence supporting that distinct conformations at ICL2 and TM6 are required for coupling of the different G protein subtypes Gαs and Gαi. These results deepen our understanding of G protein specificity and bias and can accelerate the design of ligands that select for preferred signaling pathways. 

A radio frequency (RF) reflectometry technique is presented to measure device capacitances using a probe station. This technique is used to characterize micro-electromechanical system (MEMS) variable capacitor devices that can be connected to create pull-up and pull-down networks used in digital gates for reversible computing. Adiabatic reversible computing is a promising approach to energy-efficient computing that can dramatically reduce heat dissipation by switching circuits at speeds below their RC time constants, introducing a trade-off between energy and speed. The variable capacitors in this study will be measured using single port RF reflectometry achieved with a custom-made RF probe. The RF probe consists of a micromanipulator with an on-board matching network and is calibrated by measuring a capacitive bank that shows a clearly visible frequency shift with the increase in capacitance. The RF probe worked well when measuring static capacitors with no parasitic resistance; however, the frequency shift is masked when measuring the MEMS variable capacitors due to their high in-series parasitic resistance (around 80 kΩ). Therefore, RF reflectometry has the potential to measure MEMS variable capacitors in the range of 0–30 fF when not masked by a high in-series parasitic resistance, creating a fast and versatile method for characterizing variable capacitors that can be used in energy-efficient computing. 

G protein-coupled receptors (GPCRs) represent the largest group of membrane receptors for transmembrane signal transduction. Ligand-induced activation of GPCRs triggers G protein activation followed by various signaling cascades. Understanding the structural and energetic determinants of ligand binding to GPCRs and GPCRs to G proteins is crucial to the design of pharmacological treatments targeting specific conformations of these proteins to precisely control their signaling properties. In this study, we focused on interactions of a prototypical GPCR, beta-2 adrenergic receptor (β 2 AR), with its endogenous agonist, norepinephrine (NE), and the stimulatory G protein (G s ). Using molecular dynamics (MD) simulations, we demonstrated the stabilization of cationic NE, NE(+), binding to β 2 AR by G s protein recruitment, in line with experimental observations. We also captured the partial dissociation of the ligand from β 2 AR and the conformational interconversions of G s between closed and open conformations in the NE(+)–β 2 AR–G s ternary complex while it is still bound to the receptor. The variation of NE(+) binding poses was found to alter G s α subunit (G s α) conformational transitions. Our simulations showed that the interdomain movement and the stacking of G s α α1 and α5 helices are significant for increasing the distance between the G s α and β 2 AR, which may indicate a partial dissociation of G s α The distance increase commences when G s α is predominantly in an open state and can be triggered by the intracellular loop 3 (ICL3) of β 2 AR interacting with G s α, causing conformational changes of the α5 helix. Our results help explain molecular mechanisms of ligand and GPCR-mediated modulation of G protein activation. 

The downward flux of sinking particles is a prominent Hg removal and redistribution process in the ocean; however, it is not well-constrained. Using data from three U.S. GEOTRACES cruises including the Pacific, Atlantic, and Arctic Oceans, we examined the mercury partitioning coefficient, K d , in the water column. The data suggest that the K d varies widely over three ocean basins. We also investigated the effect of particle concentration and composition on K d by comparing the concentration of small-sized (1–51 μm) suspended particulate mass (SPM) as well as its compositional fractions in six different phases to the partitioning coefficient. We observed an inverse relationship between K d and suspended particulate mass, as has been observed for other metals and known as the “particle concentration effect,” that explains much of the variation in K d . Particulate organic matter (POM) and calcium carbonate (CaCO 3 ) dominated the Hg partitioning in all three ocean basins while Fe and Mn could make a difference in some places where their concentrations are elevated, such as in hydrothermal plumes. Finally, our estimated Hg residence time has a strong negative correlation with average log bulk K d , indicating that K d has significant effect on Hg residence time.