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Naringinase is a kind of glycosidase that catalyzes the biotransformation of naringin into naringenin. It is a multi-compound enzyme that provides the activity of α-L-rhamnosidase and β-glucosidase. In this study, an artificial naringinase system was constructed by co-immobilizing α-L-rhamnosidase (Aspergillus oryzae FJ0123, Ao-rha) and β-glucosidase (Thermotoga maritima MSB8, Tm-glu) on magnetic silica-based chitosan microspheres (MSC). The molar ratio of enzymes and the preparation conditions were optimized for improving synergistic effect and immobilization efficiency. Under optimized conditions (the molar ratio of Ao-rha and Tm-glu, 3:1; temperature, 25 °C; glutaraldehyde concentration, 2.0%; pH, 3.0; time, 9 h), the immobilization yield and activity recovery were 61.4% and 37.3% for Ao-rha and 90.1% and 56.3% for Tm-glu, respectively. Biochemical characterization indicated that MSC-naringinase had better adaptability and stability than free enzymes. The activity of MSC-naringinase was maintained at 58.7% after 10 times of recycling. The catalytic conditions were also investigated. The cascade reactions of naringin to prunin and prunin to naringenin by using MSC-naringinase resulted in a yield of 0.62 mg/mL and a conversion rate of 96.9%, respectively, without prunin accumulation. These results indicated the great potential of MSC-naringinase as an efficient artificial naringinase system for cooperative enzymatic synthesis of naringenin.

Quorum quenching (QQ) enzymes, which degrade signaling molecules so as to disrupt the quorum sensing signaling process, have drawn much attention as alternative antimicrobial agents. However, the screening methods for evolution of such enzymes through constructing genetic circuits remain a challenge for its relatively high false positive rates caused by the higher basal expression level of the naturally acquired promoter. Thus, we presented an improved genetic circuit by introducing an artificial hybrid promoter P

Phenylalanine dehydrogenase (PheDH) plays an important role in enzymatic synthesis of l-phenylalanine for aspartame (sweetener) and detection of phenylketonuria (PKU), suggesting that it is important to obtain a PheDH with excellent characteristics. Gene fusion of PheDH and formate dehydrogenase (FDH) was constructed to form bifunctional multi-enzymes for bioconversion of l-phenylalanine coupled with coenzyme regeneration. Comparing with the PheDH monomer from Microbacterium sp., the bifunctional PheDH–FDH showed noteworthy stability under weakly acidic and alkaline conditions (pH 6.5–9.0). The bifunctional enzyme can produce 153.9 mM l-phenylalanine with remarkable performance of enantiomers choice by enzymatic conversion with high molecular conversion rate (99.87 %) in catalyzing phenylpyruvic acid to l-phenylalanine being 1.50-fold higher than that of the separate expression system. The results indicated the potential application of the PheDH and PheDH–FDH with coenzyme regeneration for phenylpyruvic acid analysis and l-phenylalanine biosynthesis in medical diagnosis and pharmaceutical field.

Degenerate PCR primers were designed by multiple alignment of the protein sequences of known structural genes encoding the catalytic subunits of NiFe-hydrogenases obtained from Swiss-Prot Protein Sequence Database through CLUSTAL-W software and compared for conserved sequence motifs. A 1 kb amplified PCR product was obtained from the genomic DNA of Klebsiella pneumoniae using a set of degenerate primers, and then the inverse PCR technique was used to obtain the full hydrogenase coding region. A predicted secondary structure and a 3D structural model were constructed by homology modeling and docking. On the basis of these results, it was inferred that NiFe-hydrogenase from Klebsiella pneumoniae belongs to the membrane-bound H 2 evolving hydrogenase group (Ech hydrogenase group).

A shuttle promoter-probe vector pNW33N-mpd was constructed with the E. coli-B. subtilis shuttle vector pNW33N and the mature mpd gene without it' s signal peptide-encoding sequence. The promoter fragments of B. subtilis ytkA and ywoF gene were cloned from plasmid pMPDP3 and pMPDP29 then generated the shuttle expression vector pNYTM and pNYWM. Expression vectors pNYTM and pNYWM were transformed into B. subtilis 1A751 to construct the expression strain 1A751 (pNYTM) and 1A751 (pNYTM), in these strains, under the control of the promoters and signal peptides of ytkA and ywoF gene, mpd gene was expressed and secreted with its biological activity; the result showed that the promoter of ytkA gene is much stronger than that of ywoF gene. Then a new shuttle expression-secretion vector pYNMK was constructed using the ytkA gene promoter and the signal peptide-encoding sequence of B. subtilis nprB gene, the expression of mpd gene achieved a higher level using the B. subtilis WB800 as the host, the methyl parathion hydrolase activity accumulated to a maximum level of 10.40 u/mL after 84 h of cultivation at the late stationary phase, which was 10.8-fold higher than the expression level of the original Plesiomonas strain M6, about 91.4% of the recombinant expression production was secreted into the culture medium.

Four 2-benzthiazolethiol (BzTa)-linked porphyrins (1)–(4), and their complexes with CoII and MnIII, (5)–(12), were prepared and characterized by elemental analysis, 1H-n.m.r., i.r., u.v.–vis. and mass spectra. The hydroxylation of cyclohexane in the presence of these complexes and PhIO under mild conditions was investigated. The catalytic activities of these complexes were higher than that of corresponding TPPMnIIICl and TPPCoII species respectively, which indicated that the terminal group, BzTa, played an important role in the catalysis. A possible mechanism is proposed.

The conductive polymer, poly(3,4-ethylenedioxythiophene) (PEDOT), possesses attractive properties that show the potential applications in many fields. In this study, we have proposed a two-stage enzymatic synthesis of conductive PEDOT. Horseradish peroxidase (HRP) acts as the catalyst to promote the generation of EDOT free radicals followed by the polymerization under the room temperature in the presence of poly(sodium 4-styrenesulfonate) (PSS), then a mild heating process is employed for further chain extension. The final PEDOT:PSS is purified with n-butanol and subjected to various characterizations, which indicate that PEDOT with enzymatic approach exhibits a similar molecular structure to that with chemical method. However, the enzymatically synthesized PEDOT:PSS demonstrates advantages, such as stable integration of PEDOT with PSS and better electrochemical properties, suggesting its future prospective applications.

In this paper, the Boosting-based decision tree ensemble classifiers were applied to discriminate thermophilic and mesophilic proteins. Three methods, namely, self-consistency test, 5-fold cross-validation and independent testing with other dataset, were used to evaluate the performance and robust of the models. Logitboost, as a novel classifier in Boosting algorithm, performed better than Adaboost. The overall accuracy of the three methods was 100%, 88.4% and 89.5%, respectively. It was demonstrated that LogitBoost performed comparably or even better than that of neural network, a very powerful classifier widely used in biological literatures. The influence of protein size on discrimination was addressed. It is anticipated that the power in predicting many bio-macromolecular attributes will be further strengthened if the Boosting and some other existing algorithms can be effectively complemented with each other.

In this work, the dipeptide composition of 3216 thermophilic and 4007 mesophilic protein sequences was systematically analyzed. We found that the thermophilic proteins contained more dipeptides such as EE, EK, KE, VE, EI, KI, EV, KK, VK and IE, whereas less dipeptides such as AA, LL, LA, AL, QA, QL, AQ, LT, TL and EQ. Based on this information, a statistical method for discriminating thermophilic and mesophilic proteins was developed. Our approach correctly picked up the thermophilic proteins with the accuracy of 94.0% and 89%, respectively, for the testing sets of 382 and 73 thermophilic proteins. And for the testing 325 and 73 mesophilic proteins, the accuracy was 85.2% and 89%, respectively. The influence of specific dipeptides on discrimination was also discussed.

Pattern recognition of thermophilic and mesophilic proteins were studied through principle component analysis, partial least-square regression and BP neural network. The results showed that the fitting accuracy of the three methods was 92%, 95% and 98%, respectively. And the forecasting accuracy was 60%, 72.5% and 72.5%, respectively. The best forecasting accuracy for thermophilic proteins was 75%, and for mesophilic proteins was 85%. A mathematical model was established and the biological meaning of it was expatiated on, a new method to discriminate the thermophilic and mesophilic proteins based on their sequences was established here.

In this paper, a prediction model for amino acid composition and optimum pH of xylanase in G/11 family was established in terms of an artificial neural networks based on uniform design. Results showed that the calculated and predicted pHs fitted the optimum pHs of xylanase very well and the MAPEs (Mean mean Absolute Percent Error) were 3.02% and 4.06%, the MSEs (Mean Square Error) were 0.19 and 0.19 pH unit, the MAE (Mean Absolute Error) were 0.11 and 0.19 pH unit, respectively. It was better in fittings and predictions compared with the reported model based on stepwise regression.