Mechanistic kinetic models of enzymatic cellulose hydrolysis-A review

Bioconversion of lignocellulose forms the basis for renewable, advanced biofuels, and bioproducts. Mechanisms of hydrolysis of cellulose by cellulases have been actively studied for nearly 70 years with significant gains in understanding of the cellulolytic enzymes. Yet, a full mechanistic understanding of the hydrolysis reaction has been elusive. We present a review to highlight new insights gained since the most recent comprehensive review of cellulose hydrolysis kinetic models by Bansal et al. (2009) Biotechnol Adv 27:833-848. Recent models have taken a two-pronged approach to tackle the challenge of modeling the complex heterogeneous reaction-an enzyme-centric modeling approach centered on the molecularity of the cellulase-cellulose interactions to examine rate limiting elementary steps and a substrate-centric modeling approach aimed at capturing the limiting property of the insoluble cellulose substrate. Collectively, modeling results suggest that at the molecular-scale, how rapidly cellulases can bind productively (complexation) and release from cellulose (decomplexation) is limiting, while the overall hydrolysis rate is largely insensitive to the catalytic rate constant. The surface area of the insoluble substrate and the degrees of polymerization of the cellulose molecules in the reaction both limit initial hydrolysis rates only. Neither enzyme-centric models nor substrate-centric models can consistently capture hydrolysis time course at extended reaction times. Thus, questions of the true reaction limiting factors at extended reaction times and the role of complexation and decomplexation in rate limitation remain unresolved. Biotechnol. Bioeng. 2017;114: 1369-1385. © 2017 Wiley Periodicals, Inc.

Molecular and biochemical characterization of recombinant cel12B, cel8C, and peh28 overexpressed in Escherichia coli and their potential in biofuel production

BACKGROUND

The high crystallinity of cellulosic biomass myofibrils as well as the complexity of their intermolecular structure is a significant impediment for biofuel production. Cloning of celB-, celC-encoded cellulases (cel12B and cel8C) and peh-encoded polygalacturonase (peh28) from Pectobacterium carotovorum subsp. carotovorum (Pcc) was carried out in our previous study using Escherichia coli as a host vector. The current study partially characterizes the enzymes' molecular structures as well as their catalytic performance on different substrates which can be used to improve their potential for lignocellulosic biomass conversion.

RESULTS

β-Jelly roll topology, (α/α)₆ antiparallel helices and right-handed β-helices were the folds identified for cel12B, cel8C, and peh28, respectively, in their corresponding protein model structures. Purifications of 17.4-, 6.2-, and 6.0-fold, compared to crude extract, were achieved for cel12B and cel8C, and peh28, respectively, using specific membrane ultrafiltrations and size-exclusion chromatography. Avicel and carboxymethyl cellulose (CMC) were substrates for cel12B, whereas for cel8C catalytic activity was only shown on CMC. The enzymes displayed significant synergy on CMC but not on Avicel when tested for 3 h at 45 °C. No observed β-glucosidase activities were identified for cel8C and cel12B when tested on p-nitrophenyl-β-d-glucopyranoside. Activity stimulation of 130% was observed when a recombinant β-glucosidase from Pcc was added to cel8C and cel12B as tested for 3 h at 45 °C. Optimum temperature and pH of 45 °C and 5.4, respectively, were identified for all three enzymes using various substrates. Catalytic efficiencies (k_cat/K_m) were calculated for cel12B and cel8C on CMC as 0.141 and 2.45 ml/mg/s respectively, at 45 °C and pH 5.0 and for peh28 on polygalacturonic acid as 4.87 ml/mg/s, at 40 °C and pH 5.0. Glucose and cellobiose were the end-products identified for cel8C, cel12B, and β-glucosidase acting together on Avicel or CMC, while galacturonic acid and other minor co-products were identified for peh28 action on pectin.

CONCLUSIONS

This study provides some insight into which parameters should be optimized when application of cel8C, cel12B, and peh28 to biomass conversion is the goal.

The productive cellulase binding capacity of cellulosic substrates

Cellulosic biomass is the most promising feedstock for renewable biofuel production; however, the mechanisms of the heterogeneous cellulose saccharification reaction are still unsolved. As cellulases need to bind isolated molecules of cellulose at the surface of insoluble cellulose fibrils or larger aggregated cellulose structures in order to hydrolyze glycosidic bonds, the "accessibility of cellulose to cellulases" is considered to be a reaction limiting property of cellulose. We have defined the accessibility of cellulose to cellulases as the productive binding capacity of cellulose, that is, the concentration of productive binding sites on cellulose that are accessible for binding and hydrolysis by cellulases. Productive cellulase binding to cellulose results in hydrolysis and can be quantified by measuring hydrolysis rates. In this study, we measured the productive Trichoderma reesei Cel7A (TrCel7A) binding capacity of five cellulosic substrates from different sources and processing histories. Swollen filter paper and bacterial cellulose had higher productive binding capacities of ∼6 µmol/g while filter paper, microcrystalline cellulose, and algal cellulose had lower productive binding capacities of ∼3 µmol/g. Swelling and regenerating filter paper using phosphoric acid increased the initial accessibility of the reducing ends to TrCel7A from 4 to 6 µmol/g. Moreover, this increase in initial productive binding capacity accounted in large part for the difference in the overall digestibility between filter paper and swollen filter paper. We further demonstrated that an understanding of how the productive binding capacity declines over the course of the hydrolysis reaction has the potential to predict overall saccharification time courses. Biotechnol. Bioeng. 2017;114: 533-542. © 2016 Wiley Periodicals, Inc.

Cellular automata modeling depicts degradation of cellulosic material by a cellulase system with single-molecule resolution

BACKGROUND

Enzymatic hydrolysis of cellulose involves the spatiotemporally correlated action of distinct polysaccharide chain cleaving activities confined to the surface of an insoluble substrate. Because cellulases differ in preference for attacking crystalline compared to amorphous cellulose, the spatial distribution of structural order across the cellulose surface imposes additional constraints on the dynamic interplay between the enzymes. Reconstruction of total system behavior from single-molecule activity parameters is a longstanding key goal in the field.

RESULTS

We have developed a stochastic, cellular automata-based modeling approach to describe degradation of cellulosic material by a cellulase system at single-molecule resolution. Substrate morphology was modeled to represent the amorphous and crystalline phases as well as the different spatial orientations of the polysaccharide chains. The enzyme system model consisted of an internally chain-cleaving endoglucanase (EG) as well as two processively acting, reducing and non-reducing chain end-cleaving cellobiohydrolases (CBHs). Substrate preference (amorphous: EG, CBH II; crystalline: CBH I) and characteristic frequencies for chain cleavage, processive movement, and dissociation were assigned from biochemical data. Once adsorbed, enzymes were allowed to reach surface-exposed substrate sites through "random-walk" lateral diffusion or processive motion. Simulations revealed that slow dissociation of processive enzymes at obstacles obstructing further movement resulted in local jamming of the cellulases, with consequent delay in the degradation of the surface area affected. Exploiting validation against evidence from atomic force microscopy imaging as a unique opportunity opened up by the modeling approach, we show that spatiotemporal characteristics of cellulose surface degradation by the system of synergizing cellulases were reproduced quantitatively at the nanometer resolution of the experimental data. This in turn gave useful prediction of the soluble sugar release rate.

CONCLUSIONS

Salient dynamic features of cellulose surface degradation by different cellulases acting in synergy were reproduced in simulations in good agreement with evidence from high-resolution visualization experiments. Due to the single-molecule resolution of the modeling approach, the utility of the presented model lies not only in predicting system behavior but also in elucidating inherently complex (e.g., stochastic) phenomena involved in enzymatic cellulose degradation. Thus, it creates synergy with experiment to advance the mechanistic understanding for improved application.

Mechanistic modeling of enzymatic hydrolysis of cellulose integrating substrate morphology and cocktail composition

A mechanistic model of enzymatic hydrolysis taking into account the morphology of the cellulosic particles and its evolution with time was developed. The individual behavior of the main enzymes involved in the reaction (cellobiohydrolases, endoglucanases, and β-glucosidases), as well as synergy effects, were also included. A large panel of experimental tests was done to fit and validate the model. This database included different enzymes mixtures and operating conditions and allowed to determine and compare with accuracy the adsorption and kinetic parameters of the different enzymes. Model predictions on short hydrolysis times were very satisfactory. On longer times, a deactivation constant was added to represent the hydrolysis slowdown. The model also allowed to predict the impact of enzymes ratios and initial substrate parameters (chain length distribution, polymerization degree) on hydrolysis, and to follow the evolution of these parameters with time. This model revealed general trends on the impact of cellulose morphology on hydrolysis. It is a useful tool to better understand the mechanisms involved in enzymatic hydrolysis of cellulose and to determine optimal cellulolytic cocktails for process design.

Stochastic molecular model of enzymatic hydrolysis of cellulose for ethanol production

BACKGROUND

During 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.

RESULTS

Cellulose 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.

CONCLUSIONS

The 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.

A coarse-grained model for synergistic action of multiple enzymes on cellulose

BACKGROUND

Degradation of cellulose to glucose requires the cooperative action of three classes of enzymes, collectively known as cellulases. Endoglucanases randomly bind to cellulose surfaces and generate new chain ends by hydrolyzing β-1,4-D-glycosidic bonds. Exoglucanases bind to free chain ends and hydrolyze glycosidic bonds in a processive manner releasing cellobiose units. Then, β-glucosidases hydrolyze soluble cellobiose to glucose. Optimal synergistic action of these enzymes is essential for efficient digestion of cellulose. Experiments show that as hydrolysis proceeds and the cellulose substrate becomes more heterogeneous, the overall degradation slows down. As catalysis occurs on the surface of crystalline cellulose, several factors affect the overall hydrolysis. Therefore, spatial models of cellulose degradation must capture effects such as enzyme crowding and surface heterogeneity, which have been shown to lead to a reduction in hydrolysis rates.

RESULTS

We present a coarse-grained stochastic model for capturing the key events associated with the enzymatic degradation of cellulose at the mesoscopic level. This functional model accounts for the mobility and action of a single cellulase enzyme as well as the synergy of multiple endo- and exo-cellulases on a cellulose surface. The quantitative description of cellulose degradation is calculated on a spatial model by including free and bound states of both endo- and exo-cellulases with explicit reactive surface terms (e.g., hydrogen bond breaking, covalent bond cleavages) and corresponding reaction rates. The dynamical evolution of the system is simulated by including physical interactions between cellulases and cellulose.

CONCLUSIONS

Our coarse-grained model reproduces the qualitative behavior of endoglucanases and exoglucanases by accounting for the spatial heterogeneity of the cellulose surface as well as other spatial factors such as enzyme crowding. Importantly, it captures the endo-exo synergism of cellulase enzyme cocktails. This model constitutes a critical step towards testing hypotheses and understanding approaches for maximizing synergy and substrate properties with a goal of cost effective enzymatic hydrolysis.

Computational investigation of glycosylation effects on a family 1 carbohydrate-binding module

Carbohydrate-binding modules (CBMs) are ubiquitous components of glycoside hydrolases, which degrade polysaccharides in nature. CBMs target specific polysaccharides, and CBM binding affinity to cellulose is known to be proportional to cellulase activity, such that increasing binding affinity is an important component of performance improvement. To ascertain the impact of protein and glycan engineering on CBM binding, we use molecular simulation to quantify cellulose binding of a natively glycosylated Family 1 CBM. To validate our approach, we first examine aromatic-carbohydrate interactions on binding, and our predictions are consistent with previous experiments, showing that a tyrosine to tryptophan mutation yields a 2-fold improvement in binding affinity. We then demonstrate that enhanced binding of 3-6-fold over a nonglycosylated CBM is achieved by the addition of a single, native mannose or a mannose dimer, respectively, which has not been considered previously. Furthermore, we show that the addition of a single, artificial glycan on the anterior of the CBM, with the native, posterior glycans also present, can have a dramatic impact on binding affinity in our model, increasing it up to 140-fold relative to the nonglycosylated CBM. These results suggest new directions in protein engineering, in that modifying glycosylation patterns via heterologous expression, manipulation of culture conditions, or introduction of artificial glycosylation sites, can alter CBM binding affinity to carbohydrates and may thus be a general strategy to enhance cellulase performance. Our results also suggest that CBM binding studies should consider the effects of glycosylation on binding and function.

Examination of the α-chitin structure and decrystallization thermodynamics at the nanoscale

Chitin is the primary structural material of insect and crustacean exoskeletons and fungal and algal cell walls, and as such it is the one of the most abundant biological materials on Earth. Chitin forms linear polymers of β1,4-linked-N-acetyl-D-glucosamine (GlcNAc), and in Nature, enzyme cocktails deconstruct chitin to GlcNAc. The mechanism of chitin deconstruction, like that of cellulose deconstruction, has been under investigation due to its importance in the global carbon cycle and in production of renewable and sustainable products from biological matter. To further understand the nanoscale properties of chitin, here we simulate crystals of α-chitin, which is the most prevalent form in Nature. We find excellent agreement with the recently reported crystal structure and we report the salient features of the simulations related to crystalline stability. We also compute the thermodynamic work required to peel individual chains from α-chitin surfaces, which a chitinase enzyme must conduct to deconstruct chitin. Compared with previous simulations of native plant cellulose Iβ, α-chitin exhibits higher decrystallization work for chains in the middle of surfaces and similar work for chains on the edges of crystals. Unlike cellulose, the free energy profile is dominated by a single bifurcated hydrogen bond between chains formed by the GlcNAc side chains and the O6 atoms on the primary alcohol group. This study highlights the molecular features of chitin that make it such a tough, recalcitrant material, and provides a key thermodynamic parameter in our quantitative understanding of how enzymes contribute to the turnover of carbohydrates in the biosphere.