A stochastic model for predicting dextrose equivalent and saccharide composition during hydrolysis of starch by α‐amylase

Abstract

A stochastic model was developed that was used to describe the formation and breakdown of all saccharides involved during alpha-amylolytic starch hydrolysis in time. This model is based on the subsite maps found in literature for Bacillus amyloliquefaciens alpha-amylase (BAA) and Bacillus licheniformis alpha-amylase (BLA). Carbohydrate substrates were modeled in a relatively simple two-dimensional matrix. The predicted weight fractions of carbohydrates ranging from glucose to heptasaccharides and the predicted dextrose equivalent showed the same trend and order of magnitude as the corresponding experimental values. However, the absolute values were not the same. In case a well-defined substrate such as maltohexaose was used, comparable differences between the experimental and simulated data were observed indicating that the substrate model for starch does not cause these deviations. After changing the subsite map of BLA and the ratio between the time required for a productive and a non-productive attack for BAA, a better agreement between the model data and the experimental data was observed. Although the model input should be improved for more accurate predictions, the model can already be used to gain knowledge about the concentrations of all carbohydrates during hydrolysis with an alpha-amylase. In addition, this model also seems to be applicable to other depolymerase-based systems.

Starch hydrolysis modeling: application to fuel ethanol production

“…As sugar DP decreases, variation between model predictions and experimental values decreased. Similar difficulties in predicting the maltose and maltotriose were also observed by other researchers [9,10]. These deviations could be due to the differences in the action pattern of the a-amylase enzyme [24].…”

“…A similar approach considering the hydrolysis time also leads to a similar exponential function [10].…”

“…Further, enzyme change or addition of new types of enzymes can be incorporated without need for extensive experimentation for all types of substrates. This approach has been used by several researchers [6][7][8][9][10]. All these researchers have modeled liquefaction (a-amylolysis) process only.…”

Optimization of bioconversion process for trehalose production from enzymatic hydrolysis of kudzu root starch using a visualization method

“…Previous studies suggested that the liquefaction DE value of starch was found to influence the saccharide composition, and the optimum liquefaction DE of starch for maltose syrup production was 8-11 (Marchal et al 1999). Both a too high and too low DE value can significantly affect the production of maltose, the former one causes a rapid decrease of maltose yield, while the latter one results in a high viscosity which is harmful to the following saccharification process (Besselink et al 2008). In this study, α-amylase was used to hydrolyze kudzu root starch granules to obtain utmost maltose.…”

Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes

“…For example, cellulase from Trichoderma reesei contains low fractions of β-glucosidase enzyme and this enzyme is added to the cellulase preparation to increase hydrolysis rates. It has been reported that synthetic enzyme mixture (designer combinations) of cellulase enzymes can give relatively higher hydrolysis yields (Ballesteros 2010;Banerjee et al 2010a, b;Besselink et al 2008). Currently, the only reliable method for designing optimal cellulase mixtures involves extensive experimentation using statistically designed combinations of various enzyme levels (Baker et al 1998;Banerjee et al 2010a, b;Berlin et al 2007;Gao et al 2010).…”