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

Asztalos A, Daniels M, Sethi A, Shen T, Langan P, Redondo A, Gnanakaran S - Biotechnol Biofuels (2012)

Bottom Line: We present a coarse-grained stochastic model for capturing the key events associated with the enzymatic degradation of cellulose at the mesoscopic level.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. gnana@lanl.gov.

ABSTRACT

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.

No MeSH data available.


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Effect of composition of endo-exo mixture.(a) kon(endo) = 10(sM)-1, koff(endo) = 0.01 s-1 and (b) kon(endo) = 100(sM)-1, koff(endo) = 0.1 s-1. In both cases kon(exo-R) = 104(sM)1, koff(exo-R) = 10s-1. (N = 25000 glucose units, total cellulase concentration is 2 μM).
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Figure 13: Effect of composition of endo-exo mixture.(a) kon(endo) = 10(sM)-1, koff(endo) = 0.01 s-1 and (b) kon(endo) = 100(sM)-1, koff(endo) = 0.1 s-1. In both cases kon(exo-R) = 104(sM)1, koff(exo-R) = 10s-1. (N = 25000 glucose units, total cellulase concentration is 2 μM).

Mentions: Results regarding synergism between pure Trichoderma cellulases [14,57] showed that the endo-exo synergy depends on the ratio of the concentrations of the individual enzymes. Here, we tested whether our model qualitatively reproduces this observation by comparing conversion times—the time to degrade 5%, 25%, 50% or 80% of the substrate—for various exo-R/endo ratios. Using the hydrolysis rates listed in Table 4, we consider two cases: i) the overall hydrolysis of cellulose by endo-cellulases takes place at a slower rate than the overall hydrolysis of cellulose by exo-cellulases (Figure 13a); ii) the overall hydrolysis by exo-cellulases is set to be slower than that by endo-cellulases (Figure 13b). As the rate-limiting step in the model is the adsorption of cellulases onto the substrate, we attain this by varying the kon adsorption rate constant while fixing the equilibrium constant of each of the cellulases. In both cases the substrate conversion time has a minimum at a specific ratio of the concentrations of the individual enzymes. For the first case (see Figure 13a) the optimal exo-R/endo ratio is 2:1, while for the second case (see Figure 13b) this ratio is 5:1. These minimum conversion times in both cases are much smaller than the conversion times obtained in single cellulase runs. These optimal ratios were obtained for a perfectly regular cellulose substrate; however, as pointed out earlier [14,57], the optimal experimental ratio is strongly dependent on the characteristics of the substrate. For example, on filter paper [57] the optimal exo-R/endo ratio was found to be 74:26 when the total enzyme concentration was 1 μM and 90:10 when the total enzyme concentration was 10 μM.


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

Asztalos A, Daniels M, Sethi A, Shen T, Langan P, Redondo A, Gnanakaran S - Biotechnol Biofuels (2012)

Effect of composition of endo-exo mixture.(a) kon(endo) = 10(sM)-1, koff(endo) = 0.01 s-1 and (b) kon(endo) = 100(sM)-1, koff(endo) = 0.1 s-1. In both cases kon(exo-R) = 104(sM)1, koff(exo-R) = 10s-1. (N = 25000 glucose units, total cellulase concentration is 2 μM).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475064&req=5

Figure 13: Effect of composition of endo-exo mixture.(a) kon(endo) = 10(sM)-1, koff(endo) = 0.01 s-1 and (b) kon(endo) = 100(sM)-1, koff(endo) = 0.1 s-1. In both cases kon(exo-R) = 104(sM)1, koff(exo-R) = 10s-1. (N = 25000 glucose units, total cellulase concentration is 2 μM).
Mentions: Results regarding synergism between pure Trichoderma cellulases [14,57] showed that the endo-exo synergy depends on the ratio of the concentrations of the individual enzymes. Here, we tested whether our model qualitatively reproduces this observation by comparing conversion times—the time to degrade 5%, 25%, 50% or 80% of the substrate—for various exo-R/endo ratios. Using the hydrolysis rates listed in Table 4, we consider two cases: i) the overall hydrolysis of cellulose by endo-cellulases takes place at a slower rate than the overall hydrolysis of cellulose by exo-cellulases (Figure 13a); ii) the overall hydrolysis by exo-cellulases is set to be slower than that by endo-cellulases (Figure 13b). As the rate-limiting step in the model is the adsorption of cellulases onto the substrate, we attain this by varying the kon adsorption rate constant while fixing the equilibrium constant of each of the cellulases. In both cases the substrate conversion time has a minimum at a specific ratio of the concentrations of the individual enzymes. For the first case (see Figure 13a) the optimal exo-R/endo ratio is 2:1, while for the second case (see Figure 13b) this ratio is 5:1. These minimum conversion times in both cases are much smaller than the conversion times obtained in single cellulase runs. These optimal ratios were obtained for a perfectly regular cellulose substrate; however, as pointed out earlier [14,57], the optimal experimental ratio is strongly dependent on the characteristics of the substrate. For example, on filter paper [57] the optimal exo-R/endo ratio was found to be 74:26 when the total enzyme concentration was 1 μM and 90:10 when the total enzyme concentration was 10 μM.

Bottom Line: We present a coarse-grained stochastic model for capturing the key events associated with the enzymatic degradation of cellulose at the mesoscopic level.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. gnana@lanl.gov.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus