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 extrapola...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.展开更多
The understanding of enzymatic saccharification of pretreated lignocellulosic material is of great importance. There are several important commercially available enzymes in the market that are used for this purpose. T...The understanding of enzymatic saccharification of pretreated lignocellulosic material is of great importance. There are several important commercially available enzymes in the market that are used for this purpose. The conditions of pH and temperature performance of any particular enzyme are very well defined and it is clearly indicated by its manufacturer and it depends on the type of enzyme or enzymes in the complex pool. It is well know that commercial cellulases work best at pH around 4.8-5.0 and as a consequence this is widely used in the industry and the literature. In this study it was found that optimum pH of cellulases is different than that recommended by its manufacturer at higher solids load saccharification. The optimum pH changes depending on the consistency or solids loads of the matrix where the enzyme is acting upon. Steam exploded corn stover was tested with cellulases and xylanases at different pH, consistencies and ionic strength. Results showed that the optimum pH at lower consistency （1% w/w） is the same as the one recommended by the manufacturer and in the literature; however at higher consistency the value obtained was higher （pH 5.5 to pH 6.5） instead ofpH 4.8. The difference could represent up to 30-50% higher yields and hence of great importance for the economics of second generation fuel production. An explanation of this behavior could be associated with the Donnan effect theory. This effect indicates that the presence of charged groups in the fiber matrix creates a pH gradient within the slurry. If the charged groups are negatively charged this would create a local or internal pH lower than the surrounding liquid pH. This could explain why by reducing the concentration of H^＋ higher enzymatic conversion yields were observed.展开更多
文摘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.
文摘The understanding of enzymatic saccharification of pretreated lignocellulosic material is of great importance. There are several important commercially available enzymes in the market that are used for this purpose. The conditions of pH and temperature performance of any particular enzyme are very well defined and it is clearly indicated by its manufacturer and it depends on the type of enzyme or enzymes in the complex pool. It is well know that commercial cellulases work best at pH around 4.8-5.0 and as a consequence this is widely used in the industry and the literature. In this study it was found that optimum pH of cellulases is different than that recommended by its manufacturer at higher solids load saccharification. The optimum pH changes depending on the consistency or solids loads of the matrix where the enzyme is acting upon. Steam exploded corn stover was tested with cellulases and xylanases at different pH, consistencies and ionic strength. Results showed that the optimum pH at lower consistency （1% w/w） is the same as the one recommended by the manufacturer and in the literature; however at higher consistency the value obtained was higher （pH 5.5 to pH 6.5） instead ofpH 4.8. The difference could represent up to 30-50% higher yields and hence of great importance for the economics of second generation fuel production. An explanation of this behavior could be associated with the Donnan effect theory. This effect indicates that the presence of charged groups in the fiber matrix creates a pH gradient within the slurry. If the charged groups are negatively charged this would create a local or internal pH lower than the surrounding liquid pH. This could explain why by reducing the concentration of H^＋ higher enzymatic conversion yields were observed.
基金supported by Guangxi Academy of Sciences(08YJ6SW06)Guangxi Science Foundation(0907016,0991013,0991006Z,1004606,1103111,2010GXNSFF013003 and 2010GXNSFA013046)
