Recognition of heteropolysaccharide alginate by periplasmic solute-binding proteins of a bacterial ABC transporter.

Alginate is a heteropolysaccharide that consists of ß-D-mannuronate (M) and α-L-guluronate (G). The Gram-negative bacterium Sphingomonas sp. A1 directly incorporates alginate into the cytoplasm through the periplasmic solute-binding protein (AlgQ1 and AlgQ2)-dependent ABC transporter (AlgM1-AlgM2/AlgS-AlgS). Two binding proteins with at least four subsites strongly recognize the nonreducing terminal residue of alginate at subsite 1. Here, we show the broad substrate preference of strain A1 solute-binding proteins for M and G present in alginate and demonstrate the structural determinants in binding proteins for heteropolysaccharide recognition through X-ray crystallography of four AlgQ1 structures in complex with saturated and unsaturated alginate oligosaccharides. Alginates with different M/G ratios were assimilated by strain A1 cells and bound to AlgQ1 and AlgQ2. Crystal structures of oligosaccharide-bound forms revealed that in addition to interaction between AlgQ1 and unsaturated oligosaccharides, the binding protein binds through hydrogen bonds to the C4 hydroxyl group of the saturated nonreducing terminal residue at subsite 1. The M residue of saturated oligosaccharides is predominantly accommodated at subsite 1 because of the strict binding of Ser-273 to the carboxyl group of the residue. In unsaturated trisaccharide (ΔGGG or ΔMMM)-bound AlgQ1, the protein interacts appropriately with substrate hydroxyl groups at subsites 2 and 3 to accommodate M or G, while substrate carboxyl groups are strictly recognized by the specific residues Tyr-129 at subsite 2 and Lys-22 at subsite 3. Because of this substrate recognition mechanism, strain A1 solute-binding proteins can bind heteropolysaccharide alginate with different M/G ratios.

Crystallization and preliminary X-ray analysis of alginate importer from Sphingomonas sp. A1.

Sphingomonas sp. A1 directly incorporates alginate polysaccharides through a 'superchannel' comprising a pit on the cell surface, alginate-binding proteins in the periplasm and an ABC transporter (alginate importer) in the inner membrane. Alginate importer, consisting of four subunits, AlgM1, AlgM2 and two molecules of AlgS, was crystallized in the presence of the binding protein AlgQ2. Preliminary X-ray analysis showed that the crystal diffracted to 3.3 Å resolution and belonged to space group P2(1)2(1)2(1), with unit-cell parameters a = 72.5, b = 136.8, c = 273.3 Å, suggesting the presence of one complex in the asymmetric unit.

Improving the Open-Circuit Voltage of Sn-Based Perovskite Solar Cells by Band Alignment at the Electron Transport Layer/Perovskite Layer Interface.

Organic-inorganic lead halide perovskites are promising materials for realization of low-cost and high-efficiency solar cells. Because of the toxicity of lead, Sn-based perovskite materials have been developed as alternatives to enable fabrication of Pb-free perovskite solar cells. However, the solar cell performance of Sn-based perovskite solar cells (Sn-PSCs) remains poor because of their large open-circuit voltage (VOC) loss. Sn-based perovskite materials have lower electron affinities than Pb-based perovskite materials, which result in larger conduction band offset (CBO) values at the interface between the Sn-based perovskite and a conventional electron transport layer (ETL) material such as TiO2. Herein, the relationship between the VOC and the CBO in these devices was studied to improve the solar cell performances of Sn-PSCs. It was found that the band offset at the ETL/perovskite layer interface affects the VOC of the Sn-PSCs significantly but does not affect that of the Pb-PSCs because the Sn-based perovskite material is a p-type semiconductor, unlike the Pb-based perovskite. It was also found that Nb2O5 has the CBO that is closest to zero for Sn-based perovskite materials, and the VOC values of Sn-PSCs that use Nb2O5 as their ETL are higher than those of Sn-PSCs using TiO2 or SnO2 ETLs. This study indicates that control of the energy alignment at the ETL/perovskite layer interface is an important factor in improving the VOC values of Sn-PSCs.

Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

Structure of a Bacterial ABC Transporter Involved in the Import of an Acidic Polysaccharide Alginate.

The acidic polysaccharide alginate represents a promising marine biomass for the microbial production of biofuels, although the molecular and structural characteristics of alginate transporters remain to be clarified. In Sphingomonas sp. A1, the ATP-binding cassette transporter AlgM1M2SS is responsible for the import of alginate across the cytoplasmic membrane. Here, we present the substrate-transport characteristics and quaternary structure of AlgM1M2SS. The addition of poly- or oligoalginate enhanced the ATPase activity of reconstituted AlgM1M2SS coupled with one of the periplasmic solute-binding proteins, AlgQ1 or AlgQ2. External fluorescence-labeled oligoalginates were specifically imported into AlgM1M2SS-containing proteoliposomes in the presence of AlgQ2, ATP, and Mg(2+). The crystal structure of AlgQ2-bound AlgM1M2SS adopts an inward-facing conformation. The interaction between AlgQ2 and AlgM1M2SS induces the formation of an alginate-binding tunnel-like structure accessible to the solvent. The translocation route inside the transmembrane domains contains charged residues suitable for the import of acidic saccharides.

Back-propagation operation for analog neural network hardware with synapse components having hysteresis characteristics.

To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware.