Hydrolysis of cellulose using ternary mixtures of purified celluloses

Baker, John O.; Ehrman, Christine I.; Adney, William S.; Thomas, Steven R.; Himmel, Michael E.

doi:10.1007/bf02920154

Cited by 91 publications

(27 citation statements)

References 10 publications

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“…Few studies have investigated the synergism among catalytic domains of various bacterial enzymes, and the synergistic interactions between bacterial and fungal hydrolases acting on pretreated lignocellulosic biomass. Some reports have shown exo/exo and exo/endo synergism between fungal and bacterial enzymes hydrolyzing crystalline cellulose [21,22]. Recent publications have reported synergy between Trichoderma and Serratia/Streptomyces based on chitin-degrading hydrolases completely hydrolyzing untreated crab shells [23].…”

Section: Introductionmentioning

confidence: 99%

Strategy for Identification of Novel Fungal and Bacterial Glycosyl Hydrolase Hybrid Mixtures that can Efficiently Saccharify Pretreated Lignocellulosic Biomass

et al. 2010

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A rational four-step strategy to identify novel bacterial glycosyl hydrolases (GH), in combination with various fungal enzymes, was applied in order to develop tailored enzyme cocktails to efficiently hydrolyze pretreated lignocellulosic biomass. The fungal cellulases include cellobiohydrolase I (CBH I; GH family 7A), cellobiohydrolase II (CBH II; GH family 6A), endoglucanase I (EG I; GH family 7B), and β-glucosidase (βG; GH family 3). Bacterial endocellulases (LC1 and LC2; GH family 5), β-glucosidase (LβG; GH family 1), endoxylanases (LX1 and LX2; GH family 10), and β-xylosidase (LβX; GH family 52) from multiple sources were cloned, expressed, and purified. Enzymatic hydrolysis for varying enzyme combinations was carried out on ammonia fiber expansion (AFEX)-treated corn stover at three total protein loadings (i.e., 33, 16.5, and 11 mg enzyme/g glucan). The optimal mass ratio of enzymes necessary to maximize both glucan and xylan yields was determined using a suitable design of experiments. The optimal hybrid enzyme mixtures contained fungal cellulases (78% of total protein loading), which included CBH I (loading ranging between 9-51% of total enzyme), CBH II (9-51%), EG I (10-50%), and bacterial hemicellulases (22% of total protein loading) comprising of LX1 (13%) and LβX (9%). The hybrid mixture was effective at 50°C, pH 4.5 to maximize saccharification of AFEX-treated corn stover resulting in 95% glucan and 65% xylan conversion. This strategy of screening novel enzyme mixtures on pretreated lignocellulose would ultimately lead to the development of tailored enzyme cocktails that can hydrolyze plant cell walls efficiently and economically to produce cellulosic ethanol.

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Section: Introductionmentioning

confidence: 99%

Strategy for Identification of Novel Fungal and Bacterial Glycosyl Hydrolase Hybrid Mixtures that can Efficiently Saccharify Pretreated Lignocellulosic Biomass

et al. 2010

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“…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). Since conducting such large number of hydrolysis experiments is expensive, time-consuming and labor intensive, a comprehensive hydrolysis model that can that can capture process dynamics and predict hydrolysis profile under various scenarios could be an alternate feasible approach.…”

Section: Introductionmentioning

confidence: 99%

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

Kumar

Murthy

2017

Bioresour. Bioprocess.

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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

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“…Cellulases are a subset of the glycosyl hydrolase superfamily of enzymes that have been grouped into more than 100 families based on amino acid sequence similarity, enzyme reaction mechanism, and protein fold motif [5]. Endo-and exo-cellulases are two classes of cellulases that have been shown to be effective in the hydrolysis of cellulose from biomass [6].…”

Section: Introductionmentioning

confidence: 99%

A novel method for evaluating the release of fermentable sugars from cellulosic biomass

Jiménez-Flores

Fake

Carroll

et al. 2010

Enzyme and Microbial Technology

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a b s t r a c tEfficient hydrolysis of cellulosic biomass into free sugars for the production of bioethanol would allow utilization of an abundant and renewable natural resource. However, complex microcrystalline cellulose in biomass resists digestion. A preferred method to deconstruct cellulose is using enzyme cocktails, but the presence of multiple enzymes with synergistic outcomes for this process complicates direct biochemical analysis as a reliable indicator of deconstruction. Methods to detect release of free sugars rather than direct measurement of enzymatic activity are available but are time-consuming, and do not reflect the production of metabolic inhibitors of microbial growth which may interfere with the subsequent fermentation of sugars to ethanol. The work presented here describes an automated method for detecting the release of free sugars from enzymatic digestion of cellulosic biomass that allows for realtime measurements during the enzyme treatments and provides an indication of metabolic inhibitors that can interfere with subsequent growth of microbes during fermentation.

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Hydrolysis of cellulose using ternary mixtures of purified celluloses

Cited by 91 publications

References 10 publications

Strategy for Identification of Novel Fungal and Bacterial Glycosyl Hydrolase Hybrid Mixtures that can Efficiently Saccharify Pretreated Lignocellulosic Biomass

Strategy for Identification of Novel Fungal and Bacterial Glycosyl Hydrolase Hybrid Mixtures that can Efficiently Saccharify Pretreated Lignocellulosic Biomass

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

A novel method for evaluating the release of fermentable sugars from cellulosic biomass

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