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

Abstract

Background:Cellulose is hydrolyzed to sugar monomers by the synergistic action of multiple cellulase enzymes: endo-β-1,4-glucanase, exo-β-1,4 cellobiohydrolase, and β-glucosidase. Realistic modeling of this process for various substrates, enzyme combinations, and operating conditions poses severe challenges. A mechanistic hydrolysis model was developed using stochastic molecular modeling approach. Cellulose structure was modeled as a cluster of microfibrils, where each microfibril consisted of several elementary fibrils, and each elementary fibril was represented as three-dimensional matrices of glucose molecules. Using this in-silico model of cellulose substrate, multiple enzyme actions represented by discrete hydrolysis events were modeled using Monte Carlo simulation technique. In this work, the previous model was modified, mainly to incorporate simultaneous action enzymes from multiple classes at any instant of time to account for the enzyme crowding effect, a critical phenomenon during hydrolysis process. Some other modifications were made to capture more realistic expected interactions during hydrolysis. The results were validated with experimental data of pure cellulose (Avicel, filter paper, and cotton) hydrolysis using purified enzymes from Trichoderma reesei for various hydrolysis conditions. Results:Hydrolysis results predicted by model simulations showed a good fit with the experimental data under all hydrolysis conditions. Current model resulted in more accurate predictions of sugar concentrations compared to previous version of the model. Model results also successfully simulated experimentally observed trends, such as product inhibition, low cellobiohydrolase activity on high DP substrates, low endoglucanases activity on a crystalline substrate, and inverse relationship between the degree of synergism and substrate degree of polymerization emerged naturally from the model. Conclusions

Optimization of Surfactant Addition in Cellulosic Ethanol Process Using Integrated Techno-economic and Life Cycle Assessment for Bioprocess Design

“…1 Nonproductive binding of the enzyme is one of the challenges that limit enzyme productivity and reduce cellulose hydrolysis. 2 Polyethylene glycol (PEG) has been reported to enhance enzymatic hydrolysis of pretreated lignocellulosic feedstocks, increasing hydrolysis and fermentation yields. 3−8 The possible mechanisms for enhancing enzymatic hydrolysis include (1) hydrophobic structure of surfactants form a coating around lignin, thus preventing enzymes from adsorbing to lignin; (2) surfactants alter lignocellulose structure and enhance cellulose accessibility; and (3) surfactants form reverse micelles that envelope enzymes and protect them from denaturing.…”

Stochastic model of lignocellulosic material saccharification

“…In particular, the impact of the structure properties of the substrate on the saccharification recalcitrance requires further investigations. While saccharification is intensively investigated from an experimental angle [10-12, 34, 72, 82], computational models that focus on investigating the substrate structure while simulating the whole enzymatic digestion process are scarce, and insufficiently compared to experimental data [26][27][28][29].…”

“…Although they took into consideration crystallinity for the outermost regions of the substrate, they did not vary it in order to characterize its impact on the saccharification dynamics. In 2017, they published a refined version of their model [ 27 ] that showed improved comparison to experimental results. Still, in both studies the discrepancy between experimental and simulation results was noticeable, and the substrate modelled contained only cellulose.…”

“…To develop such a model we need to represent the biochemistry and the biophysics of the system at length scales of hundreds of nm and time scales of hours, and at the same time cope with finite computational power. We therefore build a coarse-grained model based on stochastic simulations alike Kumar and Murthy [ 26 , 27 ] and Asztalos et al [ 29 ]. Throughout, we follow a purely theoretical approach supplemented by abundant experimental data found in the literature.…”

Stochastic Model of Lignocellulosic Material Saccharification

“…Additionally, a few studies also address the saccharification dynamics. Vetharaniam et al [13] developed a 3-D, agent-based model of cell wall digestion, and Kumar and Murthy [14] combined experimental and theoretical (Monte Carlo) methods to investigate the effect of enzyme crowding.…”