Voltage-sensor activation with a tarantula toxin as cargo

The opening and closing of voltage-activated Na+, Ca2+ and K+ (Kv) channels underlies electrical and chemical signalling throughout biology, yet the structural basis of voltage sensing is unknown. Hanatoxin is a tarantula toxin that inhibits Kv channels by binding to voltage-sensor paddles, crucial helix-turn-helix motifs within the voltage-sensing domains that are composed of S3b and S4 helices. The active surface of the toxin is amphipathic, and related toxins have been shown to partition into membranes, raising the possibility that the toxin is concentrated in the membrane and interacts only weakly and transiently with the voltage sensors. Here we examine the kinetics and state dependence of the toxin-channel interaction and the physical location of the toxin in the membrane. We find that hanatoxin forms a strong and stable complex with the voltage sensors, far outlasting fluctuations of the voltage sensors between resting (closed) conformations at negative voltages and activated (open) conformations at positive voltages. Toxin affinity is reduced by voltage-sensor activation, explaining why the toxin stabilizes the resting conformation. We also find that when hanatoxin partitions into membranes it is localized to an interfacial region, with Trp 30 positioned about 8.5 A from the centre of the bilayer. These results demonstrate that voltage-sensor paddles activate with a toxin as cargo, and suggest that the paddles traverse no more than the outer half of the bilayer during activation.     

Genome sequencing in microfabricated high-density picolitre reactors

The proliferation of large-scale DNA-sequencing projects in recent years has driven a search for alternative methods to reduce time and cost. Here we describe a scalable, highly parallel sequencing system with raw throughput significantly greater than that of state-of-the-art capillary electrophoresis instruments. The apparatus uses a novel fibre-optic slide of individual wells and is able to sequence 25 million bases, at 99% or better accuracy, in one four-hour run. To achieve an approximately 100-fold increase in throughput over current Sanger sequencing technology, we have developed an emulsion method for DNA amplification and an instrument for sequencing by synthesis using a pyrosequencing protocol optimized for solid support and picolitre-scale volumes. Here we show the utility, throughput, accuracy and robustness of this system by shotgun sequencing and de novo assembly of the Mycoplasma genitalium genome with 96% coverage at 99.96% accuracy in one run of the machine.     

Portability of paddle motif function and pharmacology in voltage sensors

Voltage-sensing domains enable membrane proteins to sense and react to changes in membrane voltage. Although identifiable S1-S4 voltage-sensing domains are found in an array of conventional ion channels and in other membrane proteins that lack pore domains, the extent to which their voltage-sensing mechanisms are conserved is unknown. Here we show that the voltage-sensor paddle, a motif composed of S3b and S4 helices, can drive channel opening with membrane depolarization when transplanted from an archaebacterial voltage-activated potassium channel (KvAP) or voltage-sensing domain proteins (Hv1 and Ci-VSP) into eukaryotic voltage-activated potassium channels. Tarantula toxins that partition into membranes can interact with these paddle motifs at the protein-lipid interface and similarly perturb voltage-sensor activation in both ion channels and proteins with a voltage-sensing domain. Our results show that paddle motifs are modular, that their functions are conserved in voltage sensors, and that they move in the relatively unconstrained environment of the lipid membrane. The widespread targeting of voltage-sensor paddles by toxins demonstrates that this modular structural motif is an important pharmacological target.     

Market timing using combined forecasts and machine learning

Successful market timing strategies depend on superior forecasting ability. We use a sentiment index model, a kitchen sink logistic regression model, and a machine learning model (least absolute shrinkage and selection operator, LASSO) to forecast 1‐month‐ahead S&P 500 Index returns. In order to determine how successful each strategy is at forecasting the market direction, a “beta optimization” strategy is implemented. We find that the LASSO model outperforms the other models with consistently higher annual returns and lower monthly drawdowns.