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

Deepak Kumar , Ganti S. Murthy

Bioresources and Bioprocessing ›› 2017, Vol. 4 ›› Issue (1) : 54

PDF
Bioresources and Bioprocessing ›› 2017, Vol. 4 ›› Issue (1) : 54 DOI: 10.1186/s40643-017-0184-2
Research

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

Author information +
History +
PDF

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.

Keywords

Hydrolysis / Cellulase / Bioethanol / Modeling / Cellulase purification / Synergism

Cite this article

Download citation ▾
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, 2017, 4(1): 54 DOI:10.1186/s40643-017-0184-2

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Andersen N. Enzymatic hydrolysis of cellulose—experimental and modeling studies, 2007, Copenhagen: Technical University of Denmark.

[2]

Andersen N, Johansen KS, Michelsen M, Stenby EH, Krogh KBRM, Olsson L. Hydrolysis of cellulose using mono-component enzymes shows synergy during hydrolysis of phosphoric acid swollen cellulose (PASC), but competition on Avicel. Enzym Microb Technol, 2008, 42: 362-370.

[3]

Asztalos A, Daniels M, Sethi A, Shen T, Langan P, Redondo A, Gnanakaran S. A coarse-grained model for synergistic action of multiple enzymes on cellulose. Biotechnol Biofuels, 2012, 5: 55.

[4]

Baker JO, Ehrman CI, Adney WS, Thomas SR, Himmel ME. Hydrolysis of cellulose using ternary mixtures of purified celluloses. Appl Biochem Biotechnol, 1998, 70: 395-403.

[5]

Ballesteros M. Waldron K. Enzymatic hydrolysis of lignocellulosic biomass. Bioalcohol production: biochemical conversion of lignocellulosic biomass, 2010, Boca Raton: CRC Press.

[6]

Banerjee G, Car S, Scott-Craig JS, Borrusch MS, Bongers M, Walton JD. Synthetic multi-component enzyme mixtures for deconstruction of lignocellulosic biomass. Bioresour Technol, 2010, 101: 9097-9105.

[7]

Banerjee G, Car S, Scott-Craig JS, Borrusch MS, Walton JD. Rapid optimization of enzyme mixtures for deconstruction of diverse pretreatment/biomass feedstock combinations. Biotechnol Biofuels, 2010, 3: 22.

[8]

Bansal P, Hall M, Realff MJ, Lee JH, Bommarius AS. Modeling cellulase kinetics on lignocellulosic substrates. Biotechnol Adv, 2009, 27: 833-848.

[9]

Berlin A, Maximenko V, Gilkes N, Saddler J. Optimization of enzyme complexes for lignocellulose hydrolysis. Biotechnol Bioeng, 2007, 97: 287-296.

[10]

Besselink T, Baks T, Janssen AEM, Boom RM. A stochastic model for predicting dextrose equivalent and saccharide composition during hydrolysis of starch by α-amylase. Biotechnol Bioeng, 2008, 100: 684-697.

[11]

Bezerra RMF, Dias AA. Discrimination among eight modified Michaelis-Menten kinetics models of cellulose hydrolysis with a large range of substrate/enzyme ratios. Appl Biochem Biotechnol, 2004, 112: 173-184.

[12]

Bezerra RMF, Dias AA, Fraga I, Pereira AN. Cellulose hydrolysis by cellobiohydrolase Cel7A Shows mixed hyperbolic product inhibition. Appl Biochem Biotechnol, 2011, 165: 178-189.

[13]

Chang VS, Holtzapple MT. Fundamental factors affecting biomass enzymatic reactivity. Appl Biochem Biotechnol, 2000, 84: 5-37.

[14]

Chinga-Carrasco G. Cellulose fibres, nanofibrils and microfibrils: the morphological sequence of MFC components from a plant physiology and fibre technology point of view. Nanoscale Res Lett, 2011, 6: 417.

[15]

Eriksson T, Karlsson J, Tjerneld F. A model explaining declining rate in hydrolysis of lignocellulose substrates with cellobiohydrolase I (Cel7A) and endoglucanase I (Cel7B) of Trichoderma reesei. Appl Biochem Biotechnol, 2002, 101: 41-60.

[16]

Fan L, Lee Y. Kinetic studies of enzymatic hydrolysis of insoluble cellulose: derivation of a mechanistic kinetic model. Biotechnol Bioeng, 1983, 25: 2707-2733.

[17]

Fan L, Gharpuray MM, Lee YH. Cellulose hydrolysis. Biotechnology monographs, 1987, Berlin: Springer

[18]

Gao D, Chundawat SPS, Krishnan C, Balan V, Dale BE. Mixture optimization of six core glycosyl hydrolases for maximizing saccharification of ammonia fiber expansion (AFEX) pretreated corn stover. Bioresour Technol, 2010, 101: 2770-2781.

[19]

Ghose T. Measurement of cellulase activities. Pure Appl Chem, 1987, 59: 257-268.

[20]

Hall M, Bansal P, Lee JH, Realff MJ, Bommarius AS. Cellulose crystallinity—a key predictor of the enzymatic hydrolysis rate. FEBS J, 2010, 277: 1571-1582.

[21]

Igarashi K, . Traffic jams reduce hydrolytic efficiency of cellulase on cellulose surface. Science, 2011, 333: 1279-1282.

[22]

Jäger G, . Practical screening of purified cellobiohydrolases and endoglucanases with α-cellulose and specification of hydrodynamics. Biotechnol Biofuels, 2010, 3: 18.

[23]

Jeoh T, Ishizawa CI, Davis MF, Himmel ME, Adney WS, Johnson DK. Cellulase digestibility of pretreated biomass is limited by cellulose accessibility. Biotechnol Bioeng, 2007, 98: 112-122.

[24]

Kadam KL, Rydholm EC, McMillan JD. Development and validation of a kinetic model for enzymatic saccharification of lignocellulosic biomass. Biotechnol Prog, 2004, 20: 698-705.

[25]

Kleman-Leyer KM, Siika-Aho M, Teeri TT, Kirk TK. The cellulases endoglucanase I and cellobiohydrolase II of Trichoderma reesei act synergistically to solubilize native cotton cellulose but not to decrease Its molecular size. Appl Environ Microbiol, 1996, 62: 2883-2887.

[26]

Kumar D. Biochemical conversion of lignocellulosic biomass to ethanol: experimental, enzymatic hydrolysis modeling, techno-economic and life cycle assessment studies, 2014, Corvallis: Oregon State University.

[27]

Kumar D, Murthy GS. Impact of pretreatment and downstream processing technologies on economics and energy in cellulosic ethanol production. Biotechnol Biofuels, 2011, 4: 27.

[28]

Kumar D, Murthy GS. Stochastic molecular model of enzymatic hydrolysis of cellulose for ethanol production. Biotechnol Biofuels, 2013, 6: 63.

[29]

Levine SE, Fox JM, Blanch HW, Clark DS. A mechanistic model of the enzymatic hydrolysis of cellulose. Biotechnol Bioeng, 2010, 107: 37-51.

[30]

Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol Mol Biol Rev, 2002, 66: 506-577.

[31]

Marchal L, Zondervan J, Bergsma J, Beeftink H, Tramper J. Monte Carlo simulation of the α-amylolysis of amylopectin potato starch. Bioprocess Biosyst Eng, 2001, 24: 163-170.

[32]

Marchal L, Ulijn R, Gooijer CD, Franke G, Tramper J. Monte Carlo simulation of the α-amylolysis of amylopectin potato starch. 2. α-amylolysis of amylopectin. Bioprocess Biosyst Eng, 2003, 26: 123-132.

[33]

Matsumoto M, Nishimura T. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul, 1998, 8: 3-30.

[34]

Medve J, Karlsson J, Lee D, Tjerneld F. Hydrolysis of microcrystalline cellulose by cellobiohydrolase I and endoglucanase II from Trichoderma reesei: adsorption, sugar production pattern, and synergism of the enzymes. Biotechnol Bioeng, 1998, 59: 621-634.

[35]

Merino S, Cherry J. Progress and challenges in enzyme development for biomass utilization. Biofuels, 2007, 108: 95-120.

[36]

Mosier N, Hall P, Ladisch C, Ladisch M. Reaction kinetics, molecular action, and mechanisms of cellulolytic proteins. Recent Progress Bioconversion Lignocellul, 1999, 65: 23-40.

[37]

Murthy GS, Johnston DB, Rausch KD, Tumbleson M, Singh V. Starch hydrolysis modeling: application to fuel ethanol production. Bioprocess Biosyst Eng, 2011, 34: 879-890.

[38]

Sangseethong K, Penner MH. p-Aminophenyl β-cellobioside as an affinity ligand for exo-type cellulases. Carbohydr Res, 1998, 314: 245-250.

[39]

Srisodsuk M, Kleman-Leyer K, Keränen S, Kirk TK, Teeri TT. Modes of action on cotton and bacterial cellulose of a homologous endoglucanase–exoglucanase pair from Trichoderma reesei. Eur J Biochem, 1998, 251: 885-892.

[40]

Teugjas H, Väljamäe P. Product inhibition of cellulases studied with 14C-labeled cellulose substrates. Biotechnol Biofuels, 2013, 6: 104.

[41]

Väljamäe P, Sild V, Nutt A, Pettersson G, Johansson G. Acid hydrolysis of bacterial cellulose reveals different modes of synergistic action between cellobiohydrolase I and endoglucanase I. Eur J Biochem, 1999, 266: 327-334.

[42]

Wang M, Li Z, Fang X, Wang L, Qu Y. Cellulolytic enzyme production and enzymatic hydrolysis for second-generation bioethanol production. Adv Biochem Eng Biotechnol., 2012, 128: 1-24.

[43]

Wojciechowski PM, Koziol A, Noworyta A. Iteration model of starch hydrolysis by amylolytic enzymes. Biotechnol Bioeng, 2001, 75: 530-539.

[44]

Wood T (1974) Properties and mode of action of cellulases. In: Biotechnology and bioengineering symposium, vol 5, pp 111–133

[45]

Zhang YHP, Lynd LR. Toward an aggregated understanding of enzymatic hydrolysis of cellulose: noncomplexed cellulase systems. Biotechnol Bioeng, 2004, 88: 797-824.

[46]

Zhang YHP, Lynd LR. A functionally based model for hydrolysis of cellulose by fungal cellulase. Biotechnol Bioeng, 2006, 94: 888-898.

[47]

Zhou W, Hao Z, Xu Y, Schüttler HB. Cellulose hydrolysis in evolving substrate morphologies II: numerical results and analysis. Biotechnol Bioeng, 2009, 104: 275-289.

[48]

Zhou W, Xu Y, Schüttler HB. Cellulose hydrolysis in evolving substrate morphologies III: time-scale analysis. Biotechnol Bioeng, 2010, 107: 224-234.

Funding

National Science Foundation(1236349)

AI Summary AI Mindmap
PDF

111

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/