Cellulase Assays - ACS Symposium Series (ACS Publications)

Oct 7, 1994 - Alternative Fuels Division, Applied Biological Sciences Branch, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80...
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Chapter 10

Cellulase Assays Methods from

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William

Empirical Mathematical

Models

S. Adney, C h r i s t i n e I. E h r m a n , J o h n O . B a k e r , Steven R . T h o m a s , a n d M i c h a e l E . H i m m e l

Alternative Fuels D i v i s i o n , Applied Biological Sciences B r a n c h , N a t i o n a l Renewable Energy Laboratory, 1617 Cole B o u l e v a r d , Golden, CO 80401-3393

Various kinetic models have been developed to determine the effectiveness of cellulase enzyme preparations on the rate and extent of cellulose hydrolysis. The assay method proposed by Sattler et al. (1) was used to compare five dilute acid treated wood sawdust samples and a control microcrystalline cellulose, Sigmacell 50. This study confirmed that where kinetic information is required for a specific biomass conversion process application, such as simultaneous saccharification and fermentation (SSF), hydrolysis kinetics must be evaluated using the actual substrate to acquire meaningful results.

Although it is an abundant biopolymer, cellulose is unique i n that it is highly crystalline, insoluble i n water, and highly resistant to depolymerization. The enzymatic degradation of cellulose to small reducing sugars is generally considered to be accomplished by the synergistic action of three classes of enzymes: the 1,4-0-Dglucan 4-glucanohydrolases ( E C 3.2.1.4), the exo-1,4-P-glucosidases, including both 1,4-P-D-glucan glucohydrolases ( E C 3.2.1.74) and 1,4-P-D-glucan cellobiohydrolase ( E C 3.2.1.91), and the p-D-glucosidases ( E C 3.2.1.21). The inconsistent structural features of cellulosic substrates, such as surface area, size distribution, crystallinity, and composition, affect the proportion of enzyme adsorbed and the relative rate and extent of hydrolysis (2-4). The use of pretreated, rather than ideal, biomass substrates for the production of fuels (ethanol) and other chemicals complicates most models not only because of inconsistent physical properties between individual pretreated substrates, but also because of variability i n the effectiveness of a certain pretreatment (5). It becomes evident that universal kinetic models can be extrapolated for use on any substrate, i f the key operational parameters for specific substrate-enzyme combinations are determined.

0097-6156/94/0566-0218S08.00/0 © 1994 American Chemical Society

Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

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Background Recent advances made i n the assay of cellulase preparations by Satder et al. (7) describe a relationship between the extent of hydrolysis, reaction time, and enzyme concentration. This procedure permits the ranking of the effectiveness of different enzymes and different pretreatment methods. In this study, Sattler's procedure was used i n the comparison of different pretreated biomass substrates. W i t h this approach, data are collected during the hydrolysis of a cellulosic substrate at various enzyme loadings and different digestion times. For a given digestion time, cellulose hydrolysis is described by a hyperbolic curve of glucose yield versus cellulase enzyme loading (FPU/g). A double reciprocal plot of hydrolysis yield as a function of enzyme loading can be obtained using the relationship proposed by Satder et al. (7): (Y/Cy

1

= ( K CJY J[E] m

1

+ (Y^/Q) ,

where Y / C is the fraction of substrate hydrolyzed; [E] is given i n F P U / g substrate initially added; and Y / C is the maximum fraction of substrate that could theoretically be hydrolyzed in a given time period at an infinite enzyme concentration. The y-axis intercept in the double reciprocal plot, (Y JC )'\ may be used to estimate the relative enzyme-saturated rate at a particular time for the enzyme preparation on a given substrate. Ideally, an enzyme should have a high Y and a l o w value for K CJY , because a shallow slope indicates that the enzyme concentration does not strongly affect the degree of hydrolysis. Thus, a low value for K is desirable because it is indicative of a higher enzyme-substrate affinity, less significant saturation effects, and higher rates at low enzyme loadings. Using this information, a prediction can be made, for a given pretreated substrate, of the enzyme loading required to achieve a particular level of hydrolysis i n a given length of time. This information is potentially useful in not only ranking biomass with regard to ease and extent of hydrolysis, but in modeling the performance of specific biomass substrates proposed for use in conversion processes to produce fuels and chemicals. 0

m a x

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In this study, this method of kinetic analysis was applied to the hydrolysis of microcrystalline cellulose (Sigmacell 50) as well as samples of pretreated sawdust from five species of hardwoods and softwoods (maple, sycamore, walnut, red oak, and pine) that were collected from sawmills located i n the Appalachian region of the southern United States. Waste material from the lumbering of these species of woods represents a significant source of raw material feedstock for conversion into fuels and chemicals. These wood samples were air dried to 8%-10% moisture, pretreated using a dilute sulfuric acid cooking scheme at 160°C, exhaustively washed, and then used as substrates for the enzymatic digestion studies. Although the glucan content of each wood was found to be relatively invariant throughout the samples tested, hemicellulosic sugar and lignin contents were unique to each wood as reported by Vinzant et al. (7). In this study, these and other differences in chemical composition were related to the resulting kinetic performance. Materials and Methods Biomass Handling and Pretreatment.

Samples of wood sawdust were obtained

Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

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ENZYMATIC CONVERSION OF BIOMASS FOR FUELS PRODUCTION

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from green trees harvested in Ohio, Kentucky, and West Virginia i n January 1992. The samples of wood sawdust were dried and milled according to the procedures described by Vinzant et al. (7). Dilute sulfuric acid pretreatment and composition analysis was conducted on 500-g milled wood samples as described by Torget et al. (6) and Vinzant et al. (7), respectively. Standard Reagents, Enzyme, and Substrate. Laminex (Code #6-5950, L o t #1390091-01) from Genencor, International (South San Francisco, C A ) was the cellulase mixture used in all hydrolysis studies. The filter paper activity in this preparation was determined using I U P A C methods (8) and was found to contain 63.8 ± 0.5 F P U / m L (n=4) and 82.1 U / m L P-glucosidase activity. The enzyme preparation contained 126.6 ± 2.9 m g / m L protein as determined by the Pierce M i c r o B C A method (Pierce, Rockford, EL) with bovine serum albumin as standard. Sigmacell 50 (Sigma Chemical, L o t 113F-0187) was used as the model substrate for the hydrolysis of a clean, microcrystalline cellulose. A l l other chemicals were reagent grade and obtained from Sigma unless otherwise noted. Enzymatic Hydrolysis. Enzymatic hydrolysis of the standard cellulose preparation was initiated by the addition of 2 g of Sigmacell 50 to 90-mL of 50 m M citrate buffer, p H 4.8 containing 0.02% sodium azide as a preservative, i n a 250-mL Erlenmeyer flasks. The mixture was pre-equilibrated to 50°C i n a N e w Brunswick Scientific (Model G-26) shaking incubator at 200 rpm for 1 h before the addition of 10-mL of appropriately diluted (in the same buffer) enzyme mixture, which was also pre-incubated for 1 h. Enzymatic hydrolyses of the pretreated sawdust samples were carried out exactiy as i n the case of the Sigmacell 50 model substrate, with a substrate loading of 2 g dry weight pretreated biomass per 100-mL total digestion mixture. In contrast to Sigmacell 50, which is approximately 98% cellulose, these pretreated substrates have been found to contain from 55% to 63% cellulose on a dry weight basis (7). In the analysis of the results, the enzyme loadings for each of the pretreated substrates are given i n terms of F P U per gram of cellulose content, rather than F P U per gram of substrate. Products From Enzymatic Digestion. One-milliliter aliquots were removed from well-dispersed reaction slurries i n such a way as to ensure that representative mixtures of enzyme and biomass were obtained for various time points during the hydrolysis. The aliquots were placed i n 1.5-mL microcentrifuge tubes, sealed with Gripper/1.5-mL tube closures (Rainin Instrument C o . , Inc., Woburn, M A ) , and boiled for 10 m i n i n a boiling water bath to terminate the enzyme reactions. The aliquots were then neutralized with calcium carbonate and the debris removed by centrifugation for 10 min using an Eppendorf (Model 5415C) microcentrifuge. After filtration through a 0.2 p m syringe filter, the aliquots were analyzed for neutral sugars by ion-moderated partition ( I M P ) chromatography using a Fast Carbohydrate column (BioRad) with distilled water as the mobile phase and refractive index detection. Glucose concentrations were confirmed using a Y S I M o d e l 2700 Select Biochemistry Analyzer with attached M o d e l 2710 autosampler (Yellow Springs Instrument C o . , Y e l l o w Springs, O H ) . The concentrations of glucose and cellobiose contained i n the cellulase

Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

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digestion supernatant were calculated according to the relationship: Y = [Glu]0.9 + [Cell]0.95 where [Glu] and [Cell] are concentrations in g per L by H P L C . The hydrolysis yield was expressed as the fraction (Y/Q>), where C is the total cellulose concentration. Double reciprocal plots of yield as a function of time were used to predict the maximum digestibility of individual substrates at infinite enzyme concentrations ( Y ) and to predict finite enzyme loadings required to achieve a given level of hydrolysis in various reaction times. 0

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m a x

Results H y d r o l y s i s of Sigmacell 50. Enzymatic hydrolysis of Sigmacell 50 was monitored over a period of 96 h with different enzyme-substrate ratios ranging from 5 to 100 F P U / g (Figure 1). A n initial rapid reaction phase lasting about 10 h was observed, followed by a relatively slow reaction phase with the maximum enzyme loading used (100 F P U / g cellulose), hydrolyzing roughly 70 % of the Sigmacell 50 i n 96 h. The relationship of enzyme dosage to hydrolysis yields at different reaction times, as shown in Figure 2, demonstrates the manner i n which the fraction of cellulose hydrolyzed increases, in an apparently hyperbolic fashion, as the enzyme-substrate ratio is increased for each incubation time. For a given enzyme-substrate ratio, the level of cellulose conversion was observed to increase, again in apparently hyperbolic fashion, as incubation times were extended. These results are consistent with those reported by Sattler et al. (7) for Sigmacell 50. A double reciprocal plot of cellulose hydrolysis yield versus enzyme-substrate ratio and linear least-squares fitting of the data (Figure 3) allows for determination of the maximum theoretical value for cellulose conversion at infinite enzyme loading ( Y ) , and a given incubation time. W i t h correlation coefficients for the fits being generally greater than 0.95, this method allows for convenient and reliable interpolation to obtain estimates of the extent of conversion expected at given digestion times for enzyme loadings intermediate between those actually tested. m a x

The proper level of p-glucosidase is known to be required in cellulase mixtures for achievement of maximal rates and extents of hydrolysis (9). N o additional P-glucosidase was added to any of the digestion mixtures because the starting enzyme mixture contained 81.2 U / m L P-glucosidase activity as measured on p-nitrophenyl-pD-glucopyranoside under the conditions of the digestions. This level of P-glucosidase activity was adequate to prevent buildup of high concentrations of cellobiose, because only l o w levels of cellobiose (less than 1.5 mg/mL) were observed early in the digestion and no more than trace amounts were present after 24 h. H y d r o l y s i s of Pretreated W o o d Samples. Hydrolysis data for the pretreated substrates maple, sycamore, pine, walnut, and red oak were analyzed in the same fashion as for Sigmacell 50 using the assumptions outlined by Sattler et al. (7). Enzyme loadings were calculated based on cellulose content as determined previously (6). Double reciprocal plots of l/(percent cellulose conversion) versus l / ( F P U / g cellulose) were used to predict conversion yields for given enzyme loadings in F P U / g

Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

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Relationship of increasing time on the hydrolytic yield from Sigmacell 50 expressed as a percentage o f cellulose conversion ( Y / C ) using Genencor Laminex at various loadings.

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Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

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Relationship of cellulose conversion expressed as percentage of cellulose conversion ( Y / C ) as a function of enzyme loading at various time intervals.

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Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994. O

1 h 2 h 4 h 8 h 13 h

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Double reciprocal plots (1/percent conversion ( Y / Q ) versus 1/enzyme loading i n F P U / g of cellulose) for the hydrolysis of Sigmacell 50 at various time intervals during the digestions. Lines represent the least squares linear regression fit of data.

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cellulose and reaction times. A s in the case of the double reciprocal plots for the hydrolysis of Sigmacell 50, the correlation coefficient for the linear least-squares fit of hydrolysis data of pretreated woods was found to be greater than 0.95 i n most cases, except for incubation times of less than 2 h. Double reciprocal plots shown i n Figure 4 for sycamore are typical of the relationship observed for cellulose conversion as a function of enzyme loading for a l l pretreated substrates used i n this study. Linear-least squares analyses from the double reciprocal plots for pretreated wood samples were used to generate the curves in Figures 5-7. Figures 5-7 provide a direct means of comparing the overall conversion levels of the pretreated substrates used i n this study at 24, 48, and 96 h. A t all time points, maple was easiest to convert, followed by sycamore, red oak, walnut, and pine. A comparison of double reciprocal plots at 24 h (Figure 8) shows that Y / C values for all of the pretreated substrates with the exception of pine are clustered between 75% and 100% conversion of the cellulose contents (and are thus well above the Y / C value of 74.6% for the model compound Sigmacell 50), whereas for pretreated white pine, only 12.9% of the total cellulose would appear to be available for hydrolysis i n 24 h by even an infinite concentration of cellulase. m a x

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The slopes of the double reciprocal plots i n Figures 4 and 8 indicate significant differences among all of the pretreated lignocellulosics i n terms of the relationship between enzyme loading and the percentage of the theoretical maximum yield (100 x Y / Y ) that is actually achieved. The activity of Laminex against pine is much more strongly dependent on loading (large value for slope) than are the activities of the cellulase mixture against the other substrates. The curves for maple and Sigmacell 50 have the smallest slopes in Figure 8, which is to say that at any finite cellulase loading, the extent of cellulose solubilization at 24 h w i l l be a larger fraction of the conversion level theoretically achievable at infinite cellulase loading, than w i l l be the case for the other substrates. For the pretreated pine, on the other hand, the extent of conversion falls off very rapidly as the enzyme loading is decreased (Figure 8); obtaining 24-h yields anywhere near the Y for pine w i l l therefore require quite high enzyme loadings. In Table 1, the six substrates are ranked according to the theoretical hydrolyzability of their cellulose contents in a 24-h digestion (i.e., according to the values of 100 x Y / C in the first column). It w i l l be noted that ranking the substrates on the basis of the preferred (large) values of Y / C places the pretreated wood substrates in the same order that would be observed, were they ranked on the basis of the preferred (small) values of slope of the double reciprocal plots for 24 h digestions (second column, Table 1). The sole exception to this correlation is the microcrystalline-cellulose model substrate, Sigmacell 50, which has the secondsmallest (and therefore second-best) slope among all of the substrates, but which also has the second-smallest (and i n this case, next-to-worst) Y / C value for a 24 h digestion. Basically similar rankings can be established using the Y / C values at 48 h and 96 h digestion times. (The confusion seen between the "top three" substrates at 48 and 96 h, with the extent of conversion shown as exceeding 100%, is probably traceable to underestimates of the glucan content of the substrates i n an analytical procedure involving hydrolysis catalyzed by sulfuric acid.) m a x

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Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

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Himmel et al.; Enzymatic Conversion of Biomass for Fuels Production ACS Symposium Series; American Chemical Society: Washington, DC, 1994.

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Double reciprocal plots (1/percent conversion ( Y / C ) versus 1/enzyme loading i n F P U / g of cellulose) for the hydrolysis of pretreated sycamore at various time intervals during the digestions. Lines represent the least squares linear regression fit of data.

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