Stochastic molecular model of enzymatic hydrolysis of cellulose for ethanol production

Abstract

BackgroundDuring cellulosic ethanol production, cellulose hydrolysis is achieved by synergistic action of cellulase enzyme complex consisting of multiple enzymes with different mode of actions. Enzymatic hydrolysis of cellulose is one of the bottlenecks in the commercialization of the process due to low hydrolysis rates and high cost of enzymes. A robust hydrolysis model that can predict hydrolysis profile under various scenarios can act as an important forecasting tool to improve the hydrolysis process. However, multiple factors affecting hydrolysis: cellulose structure and complex enzyme-substrate interactions during hydrolysis make it diffucult to develop mathematical kinetic models that can simulate hydrolysis in presence of multiple enzymes with high fidelity. In this study, a comprehensive hydrolysis model based on stochastic molecular modeling approch in which each hydrolysis event is translated into a discrete event is presented. The model captures the structural features of cellulose, enzyme properties (mode of actions, synergism, inhibition), and most importantly dynamic morphological changes in the substrate that directly affect the enzyme-substrate interactions during hydrolysis.ResultsCellulose was modeled as a group of microfibrils consisting of elementary fibrils bundles, where each elementary fibril was represented as a three dimensional matrix of glucose molecules. Hydrolysis of cellulose was simulated based on Monte Carlo simulation technique. Cellulose hydrolysis results predicted by model simulations agree well with the experimental data from literature. Coefficients of determination for model predictions and experimental values were in the range of 0.75 to 0.96 for Avicel hydrolysis by CBH I action. Model was able to simulate the synergistic action of multiple enzymes during hydrolysis. The model simulations captured the important experimental observations: effect of structural properties, enzyme inhibition and enzyme loadings on the hydrolysis and degree of synergism among enzymes.ConclusionsThe model was effective in capturing the dynamic behavior of cellulose hydrolysis during action of individual as well as multiple cellulases. Simulations were in qualitative and quantitative agreement with experimental data. Several experimentally observed phenomena were simulated without the need for any additional assumptions or parameter changes and confirmed the validity of using the stochastic molecular modeling approach to quantitatively and qualitatively describe the cellulose hydrolysis.

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

“…During developing algorithms for cellulase actions, enzyme accessibility was determined based on these parameters (data set with each glucose molecule) and action pattern of enzymes. For additional details of cellulose model please refer to earlier publications (Kumar 2014;Kumar and Murthy 2013).…”

“…Using a novel stochastic molecular modeling approach, in which each hydrolysis event is translated into a discrete event, we developed a first three-dimensional mechanistic cellulose hydrolysis model. The model captured the structural properties of cellulose, enzyme properties, the effect of reaction conditions, and most importantly dynamic changes in these properties (Kumar and Murthy 2013). Other than accurate predictions of hydrolysis profile, this modeling approach incorporates detailed structural features of cellulose and provides unique advantages compared to mathematical models, such as tracking of multiple oligomers as well as chain distribution, tracking of morphological changes in cellulose, elimination of the need for parameter changes with a change in experimental data set.…”

“…Other than accurate predictions of hydrolysis profile, this modeling approach incorporates detailed structural features of cellulose and provides unique advantages compared to mathematical models, such as tracking of multiple oligomers as well as chain distribution, tracking of morphological changes in cellulose, elimination of the need for parameter changes with a change in experimental data set. Please refer to our earlier paper (Kumar and Murthy 2013) for more detailed comparison of this modeling approach and comparisons to other modeling approaches.…”

Theoretical and Practical Studies on the Metabolic Engineering of Streptomyces for Production of Butanols

“…The realization that some Streptomyces are prestigious at degradation of lignocelluloses [31] and many have highly evolved enzymes, such as the laccases for lignin degradation [32], bodes well that Streptomyces could provide a robust organism chassis on which to build the capability to degrade lignocelluloses effectively. To enhance that approach, genetic engineering could be used to introduce to alcohol producing microbes the metabolic pathways needed to utilize sugar sources, such as the five carbon xyloses and other pentoses that are released from hydrolysis of woody biomass [33]. Much of today's commercial activity using advanced biotechnology for biofuels production processes on the 'creation; selection or improvement of strains of desired microorganisms having enhanced properties of these traits' [34].…”

Structural Characterization of Lignin Isolated from Wheat-Straw during the Alkali Cooking Process

“…Its potential, however, is currently underutilized. Approximately 2% of the lignin extracted from pulping processes is used for high-value products, while the rest is burned as a low-value fuel (Stewart 2008;Liu et al 2012;Kumar and Murthy 2013).…”