Biosynthetic approach to modeling and understanding metalloproteins using unnatural amino acids

Metalloproteins have inspired chemists for many years to synthesize artificial catalysts that mimic native enzymes.As a complementary approach to studying native enzymes or making synthetic models,biosynthetic approach using small and stable proteins to model native enzymes has offered advantages of incorporating non-covalent secondary sphere interactions under physiological conditions.However,most biosynthetic models are restricted to natural amino acids.To overcome this limitation,incorporating unnatural amino acids into the biosynthetic models has shown promises.In this review,we summarize first synthetic,semisynthetic and biological methods of incorporates unnatural amino acids(UAAs)into proteins,followed by progress made in incorporating UAAs into both native metalloproteins and their biosynthetic models to fine-tune functional properties beyond native enzymes or their variants containing natural amino acids,such as reduction potentials of azurin,O_2 reduction rates and percentages of product formation of HCO models in Mb,the rate of radical transport in ribonucleotide reductase(RNR)and the proton and electron transfer pathways in photosystemⅡ(PSⅡ).We also discuss how this endeavour has allowed systematic investigations of precise roles of conserved residues in metalloproteins,such as Metl21 in azurin,Tyr244 that is cross-linked to one of the three His ligands to CuB in HCO,Tyr122,356,730 and 731 in RNR and TyrZ in PSⅡ.These examples have demonstrated that incorporating UAAs has provided a new dimension in our efforts to mimic native enzymes and in providing deeper insights into structural features responsible high enzymatic activity and reaction mechanisms,making it possible to design highly efficient artificial catalysts with similar or even higher activity than native enzymes.     

One-pot fabrication of single-crystalline octahedral Pd-Pt nanocrystals with enhanced electrocatalytic activity for methanol oxidation

This study reports the synthesis of octahedral Pd-Pt bimetallic alloy nanocrystals through a facile, one-pot, templateless, and seedless hydrothermal method in the presence of glucose and hexadecyl trimethyl ammonium bromide. The morphologies, compositions, and structures of the Pd-Pt nanocrystals were fully characterized by various physical techniques, thereby demonstrating their highly alloying octahedral nanostructures. The formation or growth mechanism of the Pd-Pt bimetallic alloy nanocrystals was explored and is discussed here based on the experimental observations. In addition, the synthesized Pd-Pt nanocrystals were applied to the methanol oxidation reaction (MOR) in alkaline media, which proved that the as-prepared catalysts exhibit enhanced electrocatalytic activity for MOR. Pd₁Pt₃ exhibited the best stability and durability, and its mass activity was 3.4 and 5.2 times greater than those of Pt black and Pd black catalysts, respectively. The facile synthetic process and excellent catalytic performance of the as-prepared catalysts demonstrate that they have the potential to be used in direct methanol fuel cell techniques.     

Synthesis and supercapacitive performance of CuO/Cu2O nanosheet arrays modified by hydrothermal deposited NiOOH

Journal of Solid State Electrochemistry - Binder-free, high-performance electrode materials play a critical role for supercapacitors. In this paper, through the electrochemical anodization process,...     

Phase-Selective Syntheses of Cobalt Telluride Nanofleeces for Efficient Oxygen Evolution Catalysts

Cobalt-based nanomaterials have been intensively explored as promising noble-metal-free oxygen evolution reaction (OER) electrocatalysts. Herein, we report phase-selective syntheses of novel hierarchical CoTe₂ and CoTe nanofleeces for efficient OER catalysts. The CoTe₂ nanofleeces exhibited excellent electrocatalytic activity and stablity for OER in alkaline media. The CoTe₂ catalyst exhibited superior OER activity compared to the CoTe catalyst, which is comparable to the state-of-the-art RuO₂ catalyst. Density functional theory calculations showed that the binding strength and lateral interaction of the reaction intermediates on CoTe₂ and CoTe are essential for determining the overpotential required under different conditions. This study provides valuable insights for the rational design of noble-metal-free OER catalysts with high performance and low cost by use of Co-based chalcogenides.     

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogenous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school. Supplementary material for this article is available online.     

Estimated conditional score function for missing mechanism model with nonignorable nonresponse

Missing data mechanism often depends on the values of the responses, which leads to nonignorable nonresponses. In such a situation, inference based on approaches that ignore the missing data mechanism could not be valid. A crucial step is to model the nature of missingness. We specify a parametric model for missingness mechanism, and then propose a conditional score function approach for estimation. This approach imputes the score function by taking the conditional expectation of the score function for the missing data given the available information. Inference procedure is then followed by replacing unknown terms with the related nonparametric estimators based on the observed data. The proposed score function does not suffer from the non-identifiability problem, and the proposed estimator is shown to be consistent and asymptotically normal. We also construct a confidence region for the parameter of interest using empirical likelihood method. Simulation studies demonstrate that the proposed inference procedure performs well in many settings. We apply the proposed method to a data set from research in a growth hormone and exercise intervention study.     

Enhanced electrochemical performance of LiNi0.83Co0.12Mn0.05O2 cathodes with a fast-ion conductor Li0.33La0.56TiO3 coating layer

Journal of Solid State Electrochemistry - Ni-rich layered cathode materials LiNixCoyMn(1-x–y)O2 (x ≥ 0.8) suffer from capacity decay due to structural deterioration during...     

Charge Density Overcomes Steric Hindrance of Ferrocene Surfactant in Switchable Oil-in-Dispersion Emulsions

A ferrocene surfactant can be switched between single and double head form (FcN⁺C₁₂/Fc⁺N⁺C₁₂) triggered by redox reaction. FcN⁺C₁₂ can neither stabilize an O/W emulsion alone nor an oil-in-dispersion emulsion in combination with alumina nanoparticles due to the steric hindrance of the ferrocene group. However, such steric hindrance can be overcome by increasing the charge density in Fc⁺N⁺C₁₂, so that oil-in-dispersion emulsions can be co-stabilized by Fc⁺N⁺C₁₂ and alumina nanoparticles at very low concentrations (1×10⁻⁷ M (≈50 ppb) and 0.001 wt %, respectively). Not only can reversible formation/destabilization of oil-in-dispersion emulsions be achieved by redox reaction, but also reversible transformation between oil-in-dispersion emulsions and Pickering emulsions can be obtained through reversing the charge of alumina particles by adjusting the pH. The results provide a new protocol for the design of surfactants for stabilization of smart oil-in-dispersion emulsions.     

A Robust Metal-Organic Framework with Scalable Synthesis and Optimal Adsorption and Desorption for Energy-Efficient Ethylene Purification

Adsorptive separation is an energy-efficient alternative, but its advancement has been hindered by the challenge of industrially potential adsorbents development. Herein, a novel ultra-microporous metal-organic framework ZU-901 is designed that satisfies the basic criteria raised by ethylene/ethane (C₂H₄/C₂H₆) pressure swing adsorption (PSA). ZU-901 exhibits an “S” shaped C₂H₄ curve with high sorbent selection parameter (65) and could be mildly regenerated. Through green aqueous-phase synthesis, ZU-901 is easily scalable with 99 % yield, and it is stable in water, acid, basic solutions and cycling breakthrough experiments. Polymer-grade C₂H₄ (99.51 %) could be obtained via a simulating two-bed PSA process, and the corresponding energy consumption is only 1/10 of that of simulating cryogenic distillation. Our work has demonstrated the great potential of pore engineering in designing porous materials with desired adsorption and desorption behavior to implement an efficient PSA process.     

Photoenzymatic Hydrosulfonylation for the Stereoselective Synthesis of Chiral Sulfones

Chiral sulfones are recurrent motifs in pharmaceuticals and bioactive molecules. Although chemical methods have been developed to afford α- or β- chiral sulfones, these protocols rely heavily on the pre-synthesis of structurally complicated starting materials and chiral metal complexes. Herein, we described a photoenzymatic approach for the radical-mediated stereoselective hydrosulfonylation. Engineered variants of ene reductases provide efficient biocatalysts for this transformation, enabling to achieve a series of β-chiral sulfonyl compounds with high yields (up to 92 %) and excellent e.r. values (up to 99 : 1).