Predictors for Predicting Temperature Optimum in Beta-Glucosidases

This is the continuation of our studies on beta-glucosidase, which plays an important role in biological processes and recently strong interests focus on their potential role in biofeul production. In order to develop simple methods to predict the optimal working condition for beta-glucosidase, we used a 20-1 feedforward backpropagation neural network to screen possible predictors to predict the temperature optimum of beta-glucosidase from 25 amino-acid properties related to the primary structure of beta-glucosidases. The results show that the normalized polarizability index and amino-acid distribution probability can predict the temperature optimum of beta-glucosidase, which highlights a cost-effective way to predict various enzymatic parameters of beta-glucosidase.

Predicting pH Optimum for Activity of Beta-Glucosidases

The working conditions for enzymatic reaction are elegant, but not many optimal conditions are documented in literatures. For newly mutated and newly found enzymes, the optimal working conditions can only be extrapolated from our previous experience. Therefore a question raised here is whether we can use the knowledge on enzyme structure to predict the optimal working conditions. Although working conditions for enzymes can be easily measured in experiments, the predictions of working conditions for enzymes are still important because they can minimize the experimental cost and time. In this study, we develop a 20-1 feedforward backpropagation neural network with information on amino acid sequence to predict the pH optimum for the activity of beta-glucosidase, because this enzyme has drawn much attention for its role in bio-fuel industries. Among 25 features of amino acids being screened, the results show that 11 features can be used as predictors in this model and the amino-acid distribution probability is the best in predicting the pH optimum for the activity of beta-glucosidases. Our study paves the way for predicting the optimal working conditions of enzymes based on the amino-acid features.

Identification of proteins involved in lignocellulose degradation using in gel zymogram analysis combined with mass spectroscopy-based peptide analysis of gut proteins from larval Asian Ionghorned beetles, Anoplophora glabripennis

Enzyme activities toward lignocellulose substrates were analyzed in the gut of larval Asian longhorned beetle （Anoplophora glabripennis）. Total protein was extracted from gut contents of wild collected larvae from an invasive population in Worchester, MA, USA. From these protein extracts, lignocellulolytic activities were measured （β-1,4- endoglucanase,β-1,4-glucosidase and birch wood xylanase）.β-1,4-glucosidase activity was 0.075 μmol glucose/mg protein per min, endoglucanase activity was measured at 0.41/zmol glucose/mg protein per min and xylanase activity was 0.058μmol xylose/mg protein per min. To identify specific enzymes that may provide these activities, zymogram analysis was performed to detect enzymes active toward carboxymethyl cellulose （CMC）, 4-methylumbelliferyl-β-D-glueopyranoside and birch wood xylan. Three protein bands were found to be active toward CMC, three displayed β-1,4-glucosidase, and one displayed xylanase activity. Proteins from active bands from these zymograms were then identified by in-gel trypsin digestions followed by peptide identification by matrix-assisted laser desorption ionization - time of flight - time of flight mass spectrometry （MS）. A custom A. glabripennis transcriptome database was used for peptide identification, giving highly significant matches in all MS analyses. These matches were then searched against the National Center for Biotechnology Information database to provide annotation to the transcripts and provide possible classification. From these analyses, we were able to detect enzymes active toward cellulose and xylan, and proteins putatively involved in lignocellulose degradation in the gut of this wood-feeding insect. Future research will be focused on characterizing these enzymes through cloning and expression experiments and understanding how the lignocellulose degradation system functions in the gut of this insect.

Phytochemical analysis and inhibitory effects of Calligonum polygonoides on pancreatic α-amylase and β-glucosidase enzymes

OBJECTIVES:To estimate the existence of phytochemicals and then to determine the antidiabetic activity againstα-amylase andβ-glucosidase inhibition in vitro.METHODS:The study was carried out by following standard procedures.RESULTS:Phytochemicals analysis indicated the presence of different phytochemicals.The total phenolic content was 6.055 mg GAE/g and the total flavonoid content was 5.706 mg RU/g in the plant extract.The total saponins,alkaloids,and tannins contents were(0.044%),(2.88%)and(2.862 nm)respectively.α-amylase inhibition activity of Calligonum polygonoides(CP)extract was 70%with IC50 of 610μg/mL and that ofβ-glucosidase inhibition activity was 65%with IC50 of 640μg/mL.CONCLUSION:The findings reported for the first time the antidiabetes-promoting effects of an extract of CP in vitro,thus validating their promising anti-diabetes potential.

Ethanol production by a filamentous fungal strain Byssochlamys fulva AM130 under alternating aerobic and oxygen-limited conditions

Byssochlamys fulva AM130,a novel strain of filamentous fungus,could produce ethanol from glucose,xylose,and alkali pretreated rice straw(PRS),while the efficiencies were very low with PRS.Ethanol production of 11.84 g/L was attained by the fungus when grown in glucose,indicating that the limitations while growing on PRS were related to low hydrolytic efficiency.Enzyme profiling of the fungus showed 365 IU/ml of beta-glucosidase and 89 IU/ml of xylanase activity,while endoglucanase and filter paper activity were negligible,which accounts for the low hydrolytic efficiency.The fungus could survive for extended periods under oxygen-limited conditions and produce ethanol.The fungal mycelia could also be used for repeated cycles of anaerobic fermentation,wherein the ethanol yield improved with each consecutive cycle.