An Ultra-fast Quantum Random Number Generation Scheme Based on Laser Phase Noise

Based on the intrinsic random property of quantum mechanics, quantum random number generators allow for access of truly unpredictable random sequence and are now heading towards high performance and small miniaturization, among which a popular scheme is based on the laser phase noise. However, this scheme is generally limited in speed and implementation complexity, especially for chip integration. In this work, a general physical model based on wiener process for such schemes is introduced, which provides an approach to clearly explain the limitation on the generation rate and comprehensively optimize the system performance. We present an insight to exploit the potential bandwidth of the quantum entropy source that contains plentiful quantum randomness with a simple spectral filtering method and experimentally boost the bandwidth of the corresponding quantum entropy source to 20 GHz, based on which an ultra-fast generation rate of 218 Gbps is demonstrated, setting a new record for laser phase noise based schemes by one order of magnitude. Our proposal significantly enhances the ceiling speed of such schemes without requiring extra complex hardware, thus effectively benefits the corresponding chip integration with high performance and low implementation cost, which paves the way for its large-scale applications.Comment: 25 pages, 7 figure

Computational design of transmembrane pores.

Transmembrane channels and pores have key roles in fundamental biological processes1 and in biotechnological applications such as DNA nanopore sequencing2-4, resulting in considerable interest in the design of pore-containing proteins. Synthetic amphiphilic peptides have been found to form ion channels5,6, and there have been recent advances in de novo membrane protein design7,8 and in redesigning naturally occurring channel-containing proteins9,10. However, the de novo design of stable, well-defined transmembrane protein pores that are capable of conducting ions selectively or are large enough to enable the passage of small-molecule fluorophores remains an outstanding challenge11,12. Here we report the computational design of protein pores formed by two concentric rings of α-helices that are stable and monodisperse in both their water-soluble and their transmembrane forms. Crystal structures of the water-soluble forms of a 12-helical pore and a 16-helical pore closely match the computational design models. Patch-clamp electrophysiology experiments show that, when expressed in insect cells, the transmembrane form of the 12-helix pore enables the passage of ions across the membrane with high selectivity for potassium over sodium; ion passage is blocked by specific chemical modification at the pore entrance. When incorporated into liposomes using in vitro protein synthesis, the transmembrane form of the 16-helix pore-but not the 12-helix pore-enables the passage of biotinylated Alexa Fluor 488. A cryo-electron microscopy structure of the 16-helix transmembrane pore closely matches the design model. The ability to produce structurally and functionally well-defined transmembrane pores opens the door to the creation of designer channels and pores for a wide variety of applications

Exploring the Molecular Design of Ligand Binding Sites by Computational Protein Design

Thesis (Ph.D.)--University of Washington, 2017-06Ligand binding sites in natural proteins, with diverse structural details, provide the foundation for enzymatic activity, antibody-antigen recognition, ligand-induced pathway activation and drug discovery in general. The work presented in this dissertation seeks to understand the general design principles of the molecular details revealed in the ligand-protein complex structures. An engineering approach based on computational protein design was taken to expand the boundary of our current knowledge. By combining computational structural modeling and protein biochemical characterization, computational design of ligand binding proteins iterates between structure-based design hypotheses and experimental validation. This research scheme was applied to two related topics: 1) re-purposing natural ligand binding sites and 2) designing de novo ligand binding proteins. Representative small molecules, steroids (digoxigenin, 17-hydroxylprogesterone, cortisol) and an environmentally sensitive fluorophore (DFHBI), were chosen as design targets. High-resolution X-ray crystal structures of the engineered proteins were obtained and analyzed for modeling feedback. Binding affinity and specificity, protein stability and function, as well as modeling challenges were discussed in each case. The design methods developed and tested in this work represent a systematic way of engineering small molecule binding sites and can be expanded to broad applications. As a rigorous test of our current knowledge, computational design of ligand-binding proteins presented in this work emphasizes the high precision required for accurate ligand positioning and protein conformation modeling

Iron-Doped Nickel Molybdate with Enhanced Oxygen Evolution Kinetics

Electrochemical water splitting is one of the potential approaches for making renewable energy production and storage viable. The oxygen evolution reaction (OER), as a sluggish four-electron electrochemical reaction, has to overcome high overpotential to accomplish overall water splitting. Therefore, developing low-cost and highly active OER catalysts is the key for achieving efficient and economical water electrolysis. In this work, Fe-doped NiMoO4 was synthesized and evaluated as the OER catalyst in alkaline medium. Fe3+ doping helps to regulate the electronic structure of Ni centers in NiMoO4, which consequently promotes the catalytic activity of NiMoO4. The overpotential to reach a current density of 10 mA cm−2 is 299 mV in 1 m KOH for the optimal Ni0.9Fe0.1MoO4, which is 65 mV lower than that for NiMoO4. Further, the catalyst also shows exceptional performance stability during a 2 h chronopotentiometry testing. Moreover, the real catalytically active center of Ni0.9Fe0.1MoO4 is also unraveled based on the ex situ characterizations. These results provide new alternatives for precious-metal-free catalysts for alkaline OER and also expand the Fe-doping-induced synergistic effect towards performance enhancement to new catalyst systems

Has_circ_0071803 promotes colorectal cancer progression by regulating miR-330-5p/MAPK signaling pathway

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. A lack of effective targeted therapies against CRC makes the treatment challenging. Here, we report a circular RNA (circRNA), has_circ_0071803, functioning as an oncogene in CRC. Circ_0071803 was upregulated in CRC tissues and cell lines, and its expression levels were inversely correlated with the prognosis and survival rate of patients with CRC. Circ_0071803 knockdown suppressed cell proliferation, migration, and invasion in CRC. Moreover, we found that circ_0071803 sponged miR-330-5p, thereby upregulating mitogen-activated protein kinase 1 (MAPK1) in CRC cells. The suppression of cell activities by circ_0071803 knockdown were rescued by miR-330-5p inhibition or MAPK1 overexpression. Collectively, our findings elucidate that circ_0071803 promotes CRC progression by regulating the miR-330-5p/MAPK1 pathway, providing potential therapeutic targets for designing effective targeted treatment

Hybrid 2D Dual-Metal-Organic Frameworks for Enhanced Water Oxidation Catalysis

Metal-organic frameworks (MOFs) and MOF-derived nanostructures are recently emerging as promising catalysts for electrocatalysis applications. Herein, 2D MOFs nanosheets decorated with Fe-MOF nanoparticles are synthesized and evaluated as the catalysts for water oxidation catalysis in alkaline medium. A dramatic enhancement of the catalytic activity is demonstrated by introduction of electrochemically inert Fe-MOF nanoparticles onto active 2D MOFs nanosheets. In the case of active Ni-MOF nanosheets (Ni-MOF at Fe-MOF), the overpotential is 265 mV to reach a current density of 10 mA cm -2 in 1 m KOH, which is lowered by ≈100 mV after hybridization due to the 2D nanosheet morphology and the synergistic effect between Ni active centers and Fe species. Similar performance improvement is also successfully demonstrated in the active NiCo-MOF nanosheets. More importantly, the real catalytic active species in the hybrid Ni-MOF at Fe-MOF catalyst are unraveled. It is found that, NiO nanograins (≈5 nm) are formed in situ during oxygen evolution reaction (OER) process and act as OER active centers as well as building blocks of the porous nanosheet catalysts. These findings provide new insights into understanding MOF-based catalysts for water oxidation catalysis, and also shed light on designing highly efficient MOF-derived nanostructures for electrocatalysis

Low-Coordinate Iridium Oxide Confined on Graphitic Carbon Nitride for Highly Efficient Oxygen Evolution

Highly active and durable electrocatalysts for the oxygen evolution reaction (OER) is greatly desired. Iridium oxide/graphitic carbon nitride (IrO2/GCN) heterostructures are designed with low-coordinate IrO2 nanoparticles (NPs) confined on superhydrophilic highly stable GCN nanosheets for efficient acidic OER. The GCN nanosheets not only ensure the homogeneous distribution and confinement of IrO2 NPs but also endows the heterostructured catalyst system with a superhydrophilic surface, which can maximize the exposure of active sites and promotes mass diffusion. The coordination number of Ir atoms is decreased owing to the strong interaction between IrO2 and GCN, leading to lattice strain and increment of electron density around Ir sites and hence modulating the attachment between the catalyst and reaction intermediates. The optimized IrO2/GCN heterostructure delivers not only by far the highest mass activity among the reported IrO2-based catalysts but also decent durability

Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations.

Custom-designed ligand-binding proteins represent a promising class of macromolecules with exciting applications toward the design of new enzymes or the engineering of antibodies and small-molecule recruited proteins for therapeutic interventions. However, several challenges remain in designing a protein sequence such that the binding site organization results in high affinity interaction with a bound ligand. Here, we study the dynamics of explicitly solvated designed proteins through all-atom molecular dynamics (MD) simulations to gain insight into the causes that lead to the low affinity or instability of most of these designs, despite the prediction of their success by the computational design methodology. Simulations ranging from 500 to 1000 ns per replicate were conducted on 37 designed protein variants encompassing two distinct folds and a range of ligand affinities, resulting in more than 180 μs of combined sampling. The simulations provide retrospective insights into the properties affecting ligand affinity that can prove useful in guiding further steps of design optimization. Features indicate that entropic components are particularly important for affinity, which are not easily incorporated in the empirical models often used in design protocols. Additionally, we demonstrate that the application of machine learning approaches built upon the output from the simulations can help discriminate between successful and failed binders, such that MD could act as a screening step in protein design, resulting in a more efficient process