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...

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Main Authors: Deepak Kumar, Ganti S. Murthy
Format: Article
Language:English
Published: SpringerOpen 2017-12-01
Series:Bioresources and Bioprocessing
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40643-017-0184-2
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spelling doaj-8d3bf4cefd4647b2a5eb8644020079b82020-11-25T02:33:11ZengSpringerOpenBioresources and Bioprocessing2197-43652017-12-014111710.1186/s40643-017-0184-2Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymesDeepak Kumar0Ganti S. Murthy1Biological and Ecological Engineering, Oregon State UniversityBiological and Ecological Engineering, Oregon State UniversityAbstract 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 Model simulations were in qualitative and quantitative agreement with experimental data from hydrolysis of various pure cellulose substrates by action of individual as well as multiple cellulases.http://link.springer.com/article/10.1186/s40643-017-0184-2HydrolysisCellulaseBioethanolModelingCellulase purificationSynergism
collection DOAJ
language English
format Article
sources DOAJ
author Deepak Kumar
Ganti S. Murthy
spellingShingle Deepak Kumar
Ganti S. Murthy
Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes
Bioresources and Bioprocessing
Hydrolysis
Cellulase
Bioethanol
Modeling
Cellulase purification
Synergism
author_facet Deepak Kumar
Ganti S. Murthy
author_sort Deepak Kumar
title Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes
title_short Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes
title_full Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes
title_fullStr Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes
title_full_unstemmed Development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes
title_sort development and validation of a stochastic molecular model of cellulose hydrolysis by action of multiple cellulase enzymes
publisher SpringerOpen
series Bioresources and Bioprocessing
issn 2197-4365
publishDate 2017-12-01
description 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 Model simulations were in qualitative and quantitative agreement with experimental data from hydrolysis of various pure cellulose substrates by action of individual as well as multiple cellulases.
topic Hydrolysis
Cellulase
Bioethanol
Modeling
Cellulase purification
Synergism
url http://link.springer.com/article/10.1186/s40643-017-0184-2
work_keys_str_mv AT deepakkumar developmentandvalidationofastochasticmolecularmodelofcellulosehydrolysisbyactionofmultiplecellulaseenzymes
AT gantismurthy developmentandvalidationofastochasticmolecularmodelofcellulosehydrolysisbyactionofmultiplecellulaseenzymes
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