Obtaining cellulases against oil palm empty fruit bunch from a metagenomic library

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(1)OBTAINING CELLULASES AGAINST OIL PALM EMPTY FRUIT BUNCH FROM A METAGENOMIC LIBRARY.. DIANA CATALINA ARDILA MONTOYA. UNIVERSIDAD DE LOS ANDES ENGINEERING FACULTY DEPARTMENT OF CHEMICAL ENGINEERING BOGOTÁ D.C 2012 1.

(2) OBTAINING CELLULASES AGAINST OIL PALM EMPTY FRUIT BUNCH FROM A METAGENOMIC LIBRARY.. DIANA CATALINA ARDILA MONTOYA. Thesis project presented to obtain the title of Magister in Chemical Engineering. Advisor Andrés Fernando González Barrios, PhD. UNIVERSIDAD DE LOS ANDES ENGINEERING FACULTY DEPARTMENT OF CHEMICAL ENGINEERING BOGOTÁ D.C 2012 2.

(3) ABSTRACT. Palm growing is one of the most promising and important agricultural sectors in Colombia. However the increment in the palm oil production has resulted in the generation of millions of tons of wastes as empty fruit bunch. Given this, it is important to generate new uses for this subproduct. One of the alternatives is the simplification of cellulose content of the empty fruit bunch in fermentable sugars as cellobiose and glucose, for a subsequent carburant alcohol production. The degradation of cellulose requires enzymes named cellulases that have the capability to hydrolase glycosidic bonds from cellulose. This study focuses on identify and characterize cellulases active against oil palm empty fruit bunch from a metagenomic library. A functional screening of the library, alloed to select four clones with the ability to degrade cellulose. The effect of pH, temperature and ion metals over the cellulases extracted from the positive clones was evaluated. The range of pH and temperature that allowed the highest activities were 4-10 and 30ºC - 50ºC respectively depending on the cellulase, with a maximum production of sugars of 350µg/ml; one of the cellulases was improved by the presence of an ion metal (Zn2+) in the enzymatic reaction increasing the fermentable sugar production 2.5 times. Due the heterogeneous character of the reaction, hydrolysis profiles of the cellulases were fitted to a fractallike-kinetic model and the kinetic parameters were determined. Additionally, the sequences of the cellulases were obtaining and by bioinformatics analysis two types of cellulases motives were found: β-glucosidases and endoglucanases. These cellulases were modeled by homology and afterward geometrically optimized. To corroborate the enzyme-substrate complex formation, Molecular Docking simulations were performed using cellobiose and a chain of five glucoses as ligands. Negative binding energies were obtained and the results were compared with the same type of simulations for reported cellulases. The binding energies were in the same range (from -7.5 to -3 Kcal/mol). Given these results it cannot be discarded that the motives found are for exoglucanases or the cellulases obtained have also exoglucanase activity. The cellulases studied were compared with other cellulases reported by multiple alignments. It was interestingly found that most of the important aminoacids for interaction with ligands have a high percentage of preservation that could indicate the evolutionary importance in cellulase functionality. Nevertheless those aminoacids with low percentage of conservation could be important for specific interaction with OPEFB, since the sequenced colonies are specific for this substrate, and should have into account for further protein engineering studies.. 3.

(4) CONTENT. 1.. PROBLEM STATEMENT. 6. 2.. OBJECTIVES. 7. 2.1.. GENERAL OBJECTIVE. 7. 2.2.. SPECIFIC OBJECTIVE. 7. 3.. STATE OF ART. 8. 3.1.. OIL PALM. 8. 3.2.. LIGNOCELLULOSIC BIOMASS. 8. 3.3.. ENZYMATIC HYDROLYSIS.. 10. 3.4.. CELLULASES. 11. 3.5.. CELLULASES KINETICS. 13. 3.6.. METAGENOMICS OF CELLULASES. 19. 3.7.. MOLECULAR DOCKING. 20. 4. MATERIALS AND METHODS. 22. 4.1. METAGENOMIC LIBRARY CONSTRUCTION. 22. 4.2. OIL PALM EMPTY FRUIT BUNCH PRETREATMENT. 22. 4.3. FUNCTIONAL SCREENING OF HIGH-ANDEAN FOREST LIBRARY. 23. 4.4. GROWTH CURVES. 23. 4.5. ANALYTICAL METHODS FOR SUGARS AND PROTEIN CONCENTRATIONS¡ERROR! MARCADOR NO DEFINIDO.. 4.6. CHARACTERIZATION OF CELLULASES. 24. 4.7. DETERMINATION OF KINETIC PARAMETERS. 24. 4.8. CONCENTRATION OF CELLULASES. 25. 4.9. FOSMIDS EXTRACTION AND SEQUENCING. 25. 4.10. BIOINFORMATICS ANALYSIS OF THE SEQUENCES. 25. 5. RESULTS. 27. 4.

(5) 5.2. KINETIC CHARACTERIZATION OF CELLULASES. 28. 5.3. CONCENTRATION OF CELLULASES. 35. 5.4. FOSMID EXTRACTION. 36. 5.5. BIOINFORMATICS ANALYSIS. 37. 6. CONCLUSIONS. 44. 5.

(6) 1. PROBLEM STATEMENT. In Colombia, the production of oils from vegetable sources such as oil palm is in constant growth, not only for human consumption, but for production of alternative fuels as biodiesel. Palm growing is one of the country's most promising and important agricultural sectors and is at the heart of Colombia’s economic and social development (Fedepalma 2012). However this increment in the palm oil production has resulted in the generation of millions of tones of wastes as empty fruit bunch. In terms of mass, fresh fruit bunch contains 21% of oil palm, 26% of water and 43% of wastes of which 53.4% is empty fruit bunch (Umikalsom et al. 1997). Given that similar proportions of oil palm and empty fruit bunch are obtained, is important generate new uses for this subproduct, which is a lignocellulosic source, to generate added value to the oil palm industry that increase its productivity and sustainability. One of the alternatives is the production of carburant alcohol from simple sugars generated from lignocellulosic material (Sun and Cheng 2002). This process requires enzymes that convert cellulose polymer in fermentable sugars. It has been demonstrated that microorganisms as fungi have the ability to degrade cellulose trough the enzymatic hydrolysis of lignocellulosic material, converting it in simple sugars (Percival Zhang et al. 2006). The hydrolysis is made by three enzymes belonging to cellulases group: endoglucanase, exoglucanase, and -glucosidase. Nonetheless, many cellulases are not stable at industrial operation conditions. Due that, extremophile microorganisms have begun to be study; besides, they have a great biotechnology potential, especially useful in processes requiring very high or low conditions of temperature and pH. These microorganisms are a great source of enzymes and metabolites with different metabolic capabilities operating in extreme conditions (Van Den Burg 2003). Nowadays, a culture-independent strategy, named Metagenomics, is used to discover new microorganisms and products from DNA isolated directly from an environment. This DNA is amplified and cloning in a vector forming a library. Metagenomics has led to the discovery and characterization of a wide range and a remarkable number of biocatalysts as cellulases. This has only been possible because of the shear unlimited possibilities of prokaryotes to adapt thrive and populate every environment (Riesenfeld et al. 2004; Handelsman 2004; Schmeisser et al. 2007). Currently in Colombia, the GEBIX (Colombian Center for Genomics an Bioinformatics of Extreme Environments) group has implemented metagenomics for the study and appreciation of microbial diversity in extreme environments such as the Colombian National Natural Park ―Los Nevados‖. This work focuses on identify and characterize cellulases from a metagenomic library of a high-Andean forest ecosystem. These cellulases must be active against OPEFB and its potential use in second generation biofuels production.. 6.

(7) 2. OBJECTIVES. 2.1.. General Objective. To Identify and characterize cellulases active against oil palm empty fruit bunch from a metagenomic library of extreme environments. 2.2.. Specific objective. To Perform a functional screening to a metagenomic library of high Andean forest constructed by the Colombian Center for Genomics and Bioinformatics of Extreme Environments (GeBiX) in order to evaluate its cellulolytic capability.. To Characterize the cellulases found qualitatively using different types of cellulosic substrates for hydrolysis experiments To Determine the kinetic parameters of the hydrolysis reaction fitting the hydrolysis profiles to a semi-empirical fractal-like kinetic model.. To Isolate and sequence the fósmidos extracted from the cellulolytic clones.. Obtain the three-dimensional structure of the cellulases by homology modeling and geometrical minimization.. Study the cellulose-cellulase interaction by Molecular Docking, in order to identify to cavities and aminoacids for the specific interaction of the cellulases with the oil palm empty fruit bunch.. 7.

(8) 3. STATE OF ART. 3.1.. Oil palm. The oil palm (Elaeis oleifera), is a tropical plant that grows in warm climates at altitudes below 500 meters above sea level. It was first introduced to Colombia in 1932. But it was only in the mid-20th century that oil palm growing in Colombia first started to be commercialized across the country (Fedepalma 2012). Nowadays, Colombia is the leading producer of palm oil in Latin America and is the world´s fourth producer (Fedepalma 2012). By December of 2009, there was in the country 360.537 hectares planted with oil palm, with a growth of 7.7% respect to the last year (Fedepalma 2012). In the production process of palm oil, the fruit bunches are introduced in an extracting plant, where they are sterilized, separated from palm kernels, and crushed. Then the crude oil is extracted from the resulted pulp. After that the crude oil is clarified where is split in two different products: palm olein and palm stearin. The first is liquid in hot climates and can be mixed with any type of vegetable oil. The second is the most solid fraction and is used to make fats, mainly margarines and soaps. The properties of each part of the palm oil explain their versatility as well as their numerous applications as the production of cosmetics and biofuels (Fedepalma 2012). From the palm kernels are obtained the palm kernel oil and palm kernel cake, which serves as animal feed (Fedepalma 2012). The residues of the process describe above are: The empty fruit bunches, fiber, and shell (Teoh and Mashitah 2010). Using a base of 10.000 kg of fresh fruit bunches as feed in the process are obtained: 2.100 kg of palm oil, 2.200 kg of empty fruit bunches, 1.925 kg of fiber, and 520 kg of shell. This means that more than 43% of material entering the process is lignocellulosic biomass that is discarded (Teoh and Mashitah (2010).. 3.2.. Lignocellulosic Biomass. Lignocellulosic biomass, also called cellulosic biomass is a heterogeneous complex of carbohydrate polymers (cellulose and hemicellulose) and lignin (Mosier et al. 2005). It has a mass content of 5575% of cellulose and hemicellulose bound to lignin mainly by hydrogen bonds but also by some covalent bonds (Mosier et al. 2005; Lee 1997).. 8.

(9) Cellulose is the main component of most lignocellulosic materials and the organic compound most abundant in the planet (Orfao et al. 1999). It is a polysaccharide made of many glucose molecules linked by β-1, 4 glycoside bonds (Voet and Voet 2007). It is founded in crystalline or amorphous conformation. However is commonly founded crystalline, that are tightly packed, water insoluble and resistant to depolymerization (Mosier et al. 2005), due to the orientation of the linkages and additional hydrogen bonding. Cellulose is the 40–60% of the dry biomass of lignocellulosic materials. In hydrolysis the polysaccharide is broken down to free sugar molecules. The product, glucose, is a six carbon sugar or hexose (Hamelinck et al. 2005). However, the crystallinity of cellulose, the fact that is protected by lignin and sheathing by hemicellulose make cellulosic material difficult to hydrolyze (Mosier et al. 2005). This makes it necessary to remove hemicellolose and lignin in order to uncover cellulose. Hemicellulose is the 20–40% in dry weight of lignocellulosic materials. It consists of short highly branched chains of various sugars, mainly xylose, and further arabinose, galactose, glucose and it also contains smaller amounts of non-sugars such as acetyl groups (Hamelinck et al. 2005). The hemicellulose is not derived from cellulose, although both have glucose in their structure. The difference between them is the monomers that constitute its structure (Mosier et al. 2005). Hemicellulose hydrogen-bonds to cellulose microfibrils, thus forming a network that provides the structural backbone to plant cell wall (Mosier et al. 2005). Hemicellulose, because of its branched, amorphous nature, is relatively easy to hydrolyze (Hamelinck et al. 2005). Lignin is a large complex polymer of phenylpropane and methoxy groups, a non-carbohydrate polyphenolic substance that encrusts the cell walls and maintains the cells together (Hamelinck et al. 2005). These components are arranged randomly causing an amorphous structure. Lignin is the nonhydrated part of the cellular wall and it is not possible to hydrolyze in its original monomers (Mosier et al. 2005). It is degradable by only few organisms, into higher value products such as organic acids, phenols and vanillin. Via chemical processes valuable fuel additives may be produced (Hamelinck et al. 2005). Currently, many efforts have been made in developing technologies based on the fermentation of glucose derived from lignocellulosic materials as a renewable fuel source (ethanol). Ethanol industry is increase approximately in an order 109 in number of gallons per year. This indicates that the demand for lignocellulosic substrates will increase significantly (Mosier et al. 2005). Moreover use of lignocellulosic biomass in fermentation process, offers a great potential for reducing the production cost due its condition of waste in the oil palm production process. Nevertheless, for efficient hydrolysis of cellulose and its subsequent fermentation, it is necessary to remove the sheathing of hemicellulose and lignin. It is possible with a pretreatment of lignocellulosic biomass which leaves the cellulose exposed to the enzymatic attack (hydrolysis of cellulose) (Sun and. 9.

(10) Cheng 2002). The effects of the pretreatment are: The hemicellulose hydrolysis, removal of lignin, crystallinity reduction of cellulose, increment of surface area and porosity. Pretreatments are classified according to the mode of action in this way:. a. Physical -. Mechanic types: Grinding and sieving. -. Thermal types: Hot liquid water, low-temperature pyrolysis. b. Physico-chemical: -. Hydrothermal: Steam explosion, catalyzed steam explosion, ammonia fiber explosion.. c. Chemical: -. Alkaline hydrolysis: Using NaOH.. -. Acid hydrolysis: Concentrated acid hydrolysis, and diluted acid hydrolysis.. -. Ozonolysis: Using O3. d. Biological -Enzymatic: Carried out by enzymes produced by fungi or bacteria.. 3.3.. Enzymatic Hydrolysis.. The enzymatic hydrolysis is the conversion of cellulose in simple sugars including glucose. It is carried out by cellulases highly specific with an efficiency dependent to the lignocellulosic structure of the substrate. In the production of alcohol it occurs after pretreatment, when hemicellulose and lignin where removed (Mosier et al. 2005).. Three different type of cellulases are involve in the enzymatic hydrolysis: endoglucanases, exoglucanases, and β-glucosidases. Endoglucanases hydrolyze the crystalline cellulose converting it in amorphous cellulose. Exoglucanases is in charge to produce cellobiose, and β-glucosidases produce glucose from cellobiose (Shepherd et al. 1981). Enzymatic hydrolysis is preferable over chemical hydrolysis due it environmentalñ impact and the lack of subproducts that could affect the fermentation process. (Mosier et al. 2005). The factors that influence the production of fermentable sugars are: substrate structure, substrate concentration, and pretreatment type, thermal stability of enzymes, enzymes concentration, hydrolysis. 10.

(11) time, ph, temperature, and stir rate (Szczodrak and Fiedurek 1996). Besides, it is important to consider the optimal time of hydrolysis, after which the accumulated products inhibit hydrolysis reaction (Lee 1997).. 3.4.. Cellulases. Cellulases are an enzymatic complex capable to degrade cellulose in glucose. The principal reaction carried out by cellulases is the hydrolysis of 1, 4-β-D-glycosidic bonds. The mode of action of cellulases is according to the type of microorganism they come from. Aerobic microorganisms secrete the three types of cellulases (Endoglucanase, exoglucanase and β-glucosidase) independently. Then these cellulases acts in synergy depending on the substrate structure (Fig 1) (Lynd et al. 2002). Endoglucanases, also called CMCases, are enzymes that randomly break down internal glycosid linkages of the amorphous region of cellulose, releasing polysaccharides with lower degrees of polymerization than the original crystalline cellulase. They have an exposed active site; therefore have the ability to bind to the substrate at any point on the surface (Santoyo et al. 1999). Exoglucanases hydrolyze the β-1, 4 glycoside bonds in amorphous cellulose. It gradually attack cellulose molecules at nonreducing end of the chain. As a result of the hydrolysis, many cellobiose molecules are obtained. These types of enzymes are not really active against crystalline cellulose, nevertheless show a synergy action highly cooperative with endoglucanases. The action of exoglucanases is very low with substrates as CMC (carboximetilcelulose) or HEC (hydroximetilcelulose) (Santoyo et al. 1999). β-glucosidase is a type of cellulose that hydrolyze cellobiose and clyclodextrins of low molecular weight in glucose. These enzymes are very important components of cellulolitic complex, since they complete hydrolysis until glucose is obtained. Furthermore, the cellulolitic without glucosidase have very low activities due the inhibition of the exoglucanases and endoglucanases by cellobiose (Santoyo et al. 1999).. 11.

(12) Figure 1. Type of cellulases (Lynd et al. 2002). In contrast with aerobic microorganisms, anaerobic microorganisms do not secrete different types of cellulases to degrade cellulose. They secrete one type of enzyme that is organized in a supramolecular multiprotein complex named the cellulosome (Prates et al. 2001). The multiple subunits of cellulosomes are composed of numerous functional domains, which interact with each other and with the cellulosic substrate (Bayer et al. 1994). This complex with a molecular weight of approximately 3MDa contains numerous enzymatic functions that act in synergy to bring about the complete hydrolysis of cellulose (Prates et al. 2001). Cellulosomes are cell protuberances which tightly bind to crystalline cellulose. They mediate a close neighborhood between cell and substrate and thus minimize diffusion losses of hydrolytic products, which is thought to be a major advantage for attached cells (Schwarz 2001). Genetic and biochemical data revealed that, in all cellulosomes, the components of the multi-enzyme complex are strongly bound to each other. The interaction is mediated by a duplicated, non-catalytic segment of 22 amino acid residues found to be conserved in all enzymes located in the cellulosome. This is the dockerin module. It binds specifically to the cohesin modules, located in a noncatalytic cellulosome component, named ―scaffoldin‖. The catalytic components themselves are complex proteins consisting of catalytic and non-catalytic modules (Schwarz 2001). The structure of hypothetical cellulosome is shown in figure 2.. 12.

(13) Figure 2. Cellulosome structure The molecular weight of this enzymatic complex is around 3MDa (Bayer et al. 1994).. 3.5.. Cellulases kinetics. In hydrolysis of lignocellulosic material is important to characterize kinetically the enzymes used, because of many factors that contribute to decreasing rates with conversion. To understand the phenomenon occurs on the enzymatic reaction, mathematical modeling of the hydrolysis process is an important tool (Bansal et al. 2009). Most of these models are based on the relative importance of kinetic and physical parameters of the model including substrate absorption rate and surface accessibility. To find and alleviate bottlenecks on hydrolysis comprehension, the kinetic and the physical parameters in the model have to be estimated correctly (Bansal et al. 2009).. Based on the fundamental approach and methodology used, the models can broadly be divided into three classes: empirical models, Michaelis–Menten based model, and. models accounting for. adsorption. (Bansal et al. 2009).. Empirical models. Empiricalmodels help in quantifying the effects of various substrate and enzyme properties on hydrolysis . These empirical models have been generally used to correlate hydrolysis with either the structural properties of the substrate or with time. They can help in understanding the interactions. 13.

(14) between the substrate properties and it can be useful for initial rate estimations, which are important for resuspension experiments and Lineweaver–Burk plots used in the Michaelis–Menten models. For example, there exist empirical expressions that represent the hydrolysis rate (Bansal et al. 2009):. Where P is product concentration So is the initial substrate concentration v0 is the initial rate, k is the retardation constant, and t is time (Bansal et al. 2009).. where Y is the concentration of hydrolyzed cellulose, Ca and Cb are concentrations of easily and difficult hydrolysable parts of cellulose respectively, ka and kb are the rate constants of the first order hydrolysis of easily and difficult hydrolysable parts of cellulose, t is time (Bansal et al. 2009).. where x is the conversion of cellulose to glucose, xmax is the maximum conversion, t1/2 is the time required for 50% conversion, t is time (Bansal et al. 2009). The various levels of these factors were achieved by pretreating the substrate to different extents. Once the desired levels of the model inputs are determined, pretreatment conditions can be set to achieve the values closest to the optimal ones (Bansal et al. 2009).. Michaelis-Menten based model. The Michaelis–Menten scheme is based on mass action laws that hold for homogenous reaction conditions but can be directly applied to the heterogeneous reaction conditions of enzymatic hydrolysis of insoluble cellulosic substrates (Bansal et al. 2009). Cellulose hydrolysis is a heterogeneous reaction occurring on the substrate surface. For heterogeneous reaction systems, classical chemical kinetics assumption of uniformly mixed systems does not hold, resulting in apparent rate orders, time-dependent rate constants, and non-uniform concentration variation of reacting species in the fractal or dimensionally restricted media (Bansal et al. 2009). Such a behavior. 14.

(15) is termed fractal kinetics, Monte Carlo simulations have corroborated that the quasi-steady state assumption cannot be applied in these reaction systems (Bansal et al. 2009). However, Michaelis–Menten model fit the experimental data of enzymatic hydrolysis very well under certain conditions (Bansal et al. 2009). The Michaelis –Menten model is based on the concept of an enzyme forming a non-covalent complex with its substrate before catalyzing the reaction, and then dissociating from the product. The model is shown below (Cermak 2009):. Where E is the enzyme, S is the substrate, and P is the product of enzymatic reaction. The mass action law is written as follows:. Where [P] is the concentration of the product, [E] is the concentration of enzyme, [S] is the concentration of substrate, and [ES] is the concentration of substrate-enzyme complex.. In the case of enzymatic reaction the reaction order is one because those involving a single reaction step and a single transition state. Then equation is simplify to (Cermak 2009):. One more common simplification of the above model is to assume that enzymes have very low affinity for their products, and thus that. is negligible (Cermak 2009):. 15.

(16) Integrating this equation yields a linear equation in [P], in which product formation is constant as long as the steady-state approximation holds. However, it is not particularly useful since we do not know [ES], which is a function of [Etotal] and [Stotal] (Cermak 2009):. where,. then. Or. So,. In order to deal with experimental data, the equation is written as:. 16.

(17) Where v is the reaction rate, Km is the Michaelis-Menten constant, Vmax is the maximum reaction rate at the saturation point, and [S] is the concentration of substrate.. In an experiment that measures v as a function of [S], the plot is 1/v against 1/[S]. Then, KM/vmax is the slope and 1/vmax the intercept. This is the Lineweaver-Burk plot (Fig. 3) (Cermak 2009).. Figure 3. Lineweaver-Burk Plot.. When velocity or reaction rate is plotted as a function of concentration of substrate, the MichaelisMenten plot is obtained (Fig. 4). Figure 4 . Michaelis Menten Plot (Cermak 2009). 17.

(18) The Michaelis-Menten plot shows that Km is the substrate concentration at the half of Vmax or saturation point.. Adsorption in cellulase hydrolysis models Incorporation of adsorbed cellulase concentration into hydrolysis models has been achieved mainly in two ways: with the Langmuir adsorption isotherm, or with the help of kinetic equations. A model that use the Laingmuir isotherm is:. Where Eb is the bound enzyme concentration, Ef is the free enzyme concentration, Kad is the dissociation constant for adsorption, S is the substrate concentration, and Emax is the maximum adsorption capacity in amount of cellulase per amount of cellulose (Bansal et al. 2009). The use of isotherm or any other mathematical expression for calculating the adsorbed amount of enzyme during hydrolysis, evolve the assumption that the adsorption equilibrium is established very fast as compared to the hydrolysis step. This assumption may not be valid under all experimental conditions. Lignin and hemicellulose act as barriers to cellulases to reach the cellulose core, and thus the changes in adsorption characteristics will be more pronounced for lignocellulosic substrates as compared to pure cellulosic substrates (Bansal et al. 2009). The adsorption characteristics can depend on the type of substrate used, and since the isotherm parameters can change with conversion, it is important to validate the model against a measured amount of adsorbed cellulase during the hydrolysis (Bansal et al. 2009). An example of the models using kinetic equations for the amount of enzyme is as follows (Bansal et al. 2009):. Where E is the enzyme, Sc is the active cellulose, E*Sc is the enzyme– cellulose complex, CE is the enzyme concentration, CE*Sc is the enzyme–cellulose complex concentration, CSc is the active cellulose. 18.

(19) concentration, ksc1 is the adsorption constant on active cellulose, ksc2 is the desorption constant on active cellulose, and kp is the product formation constant (Bansal et al. 2009). Some of the kinetic models assume instantaneous substrate–enzyme complex formation, and then the adsorbed amount of cellulase is the same as the amount of substrate–enzyme complexes. Some others assume an additional kinetic step on the substrate surface after cellulase adsorption, where the adsorbed cellulase combines with substrate to form a cellulase–substrate complex (Bansal et al. 2009).. 3.6.. Metagenomics of cellulases. Metagenomics is the functional and sequence based analysis of the collective microbial genomes contained in an environmental sample (Riesenfeld et al. 2004). The metagenomic analysis is culture independent, making it a useful tool for the study of uncultured microorganisms (more than 99% of whole microorganisms in earth are uncultured, and it represents the majority of the planet’s biological diversity) (Riesenfeld et al. 2004).. Direct isolation of genetic material from an environment. eliminates the need to culturing the organisms under study; the only requirements are the amplification and cloning of DNA into a cultured organism. This with the aim of captures the genetic material for study, preservation, and analysis of microorganisms biotechnological potential (Handelsman 2004). Metagenomic analysis involves isolating DNA from an environmental sample (water, soil, and eukaryotic host), cloning it into a suitable vector, transforming the clones into a host bacterium, and screening the resulting transformants (Fig. 5) (Handelsman 2004).. Figure. 5. Schematic representation of metagenomics strategy.(Schmeisser et al. 2007).. 19.

(20) Metagenomics has led to the discovery and characterization of a wide range and a remarkable number of biocatalysts. This has only been possible because of the shear unlimited possibilities of prokaryotes to adapt thrive and populate every environment.. By far, too many different biocatalysts have been. detected to include new lipases, chitinases, cellulases, proteases and other novel enzymes from metagenomes (Schmeisser et al. 2007; Lorenz et al. 2002). Specifically there is a great interest in the search of novel cellulases, because of its application in biofuels development. Through metagenomics, it have been found new sequences that code for cellulases active against lignocellulosic material (Voget et al. 2006; Duan et al. 2009). Nevertheless it is necessary to find out resistant cellulases to extreme conditions of temperature and pH as many industrial conditions. Nowadays in Colombia, the GEBIX® group has implemented metagenomics for the study and appreciation of microbial diversity in extreme environments such as the Parque Natural Nacional Los Nevados, and has built two metagenomic libraries with DNA extracted from different substrates in the Park. These libraries are being mainly evaluated by their capacity to metabolize cellulose. The Grupo de Diseño de Productos y Procesos (GDPP) from Universidad de los Andes has lead studies with a focus on harnessing the cellulose and hemicellulose content in the waste generated from the extraction of palm oil and its potential use to produce simple sugars. Currently the principal research in this area is focus on found, purify, and characterize celluloses from the metagenomic library of extreme environment of GEBIX® group. These celluloses must be active against oil palm empty fruit bunch.. 3.7.. Molecular docking. The application of computational methods to study the formation of intermolecular complexes has been the subject of intensive research during the last decade (Teodoro et al. 2001). The computational process of searching for an optimal conformation of proteins with ligands that is able to fit, both geometrically and energetically, the binding site is called molecular docking (Teodoro et al. 2001). In other words, molecular docking predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex (Lengauer and Rarey 1996). Molecular docking simulations may be used for reproducing experimental data through docking validations algorithms, where protein-ligand conformations are obtained in silico and compared to structures obtained from X-ray crystallography or nuclear magnetic resonance (Lengauer and Rarey 1996; Dias et al. 2008). Molecular docking can be thought of as a problem of “lock-and-key”, where one is interested in finding the correct relative orientation of the ―key‖ which will open up the ―lock‖ (where on the surface of the lock is the key hole, which direction to turn the key after it is inserted, etc.). Molecular docking may be defined as an optimization problem,with energy minimization, which would describe the best-fit orientation of a ligand that binds to a particular protein of interest (Jorgensen 1991).. 20.

(21) In molecular modeling there are two important parameters: the collection of atoms, and the collection of bonds between pairs of atoms. Information used for energy computations is associated with each of the atoms and bonds. Each atom carries standard information, such as its var der Waals radius. The information associated with each bond is: the bond length (distance between atom centers), the bond angle, (angle between two consecutive bonds), and the rotation of the bond (Teodoro et al. 2001). The most important aspect of molecular docking is calculating the energy of conformations and interactions. This energy can be calculated with a wide range of methods ranging from quantum mechanics to empirical energy functions. The accuracy of these functions is usually proportional to its computational expense. Choosing the correct energy calculation method is highly dependent on the application (Teodoro et al. 2001). There are several docking programs, such as DOCK, AUTODOCK, GOLD, FLEXX, ZDOCK, MZDOCK, MSDOCK , Surflex , MCDOCK, and others [32]. Each docking application is based on a specific search algorithm, such as Incremental Construction (IC), Genetic Algorithm (GA), Monte Carlo Simulations (MC), Fast shape matching (SM), Simulated annealing (SA), Distance Geometry (DG), Evolutionary programming (EP), and Tabu search (TS). Each one has its specific parameter set and search method (Dias et al. 2008). The molecular docking simulation depends also on the assumption whether the protein is rigid or flexible. In Rigid protein docking the ligand binding don´t affect the three dimensional structure of protein. The ligand begins the search process randomly outside the binding site and by exploring the values for translations, and the internal degrees of freedom, it will eventually reach the bound conformation. Distinction between good and bad docked conformations is carried out by the scoring function (Moult et al. 1999). In flexible model both protein and ligand are flexible and when they interact to form a complex both structures change their conformation to form a minimum energy perfect-fit (Moult et al. 1999). However, the exact modeling of the flexibility is far beyond of actual computational capability. Do that the partial flexibility model is used. In this model the protein is considered relatively rigid and is the ligand who changes its three dimensional conformation during the binding (Moult et al. 1999). There are two main components to docking algorithms. The first part is the search algorithm that will generate different binding modes of the ligand to the active site. The second part is the scoring function, which will rank the different binding modes based on the complementarity to the binding site. The conformation corresponding to the mode in the crystal structure should be ranked the lowest. Then the successful binding mode generation is defined as the root mean square deviation (RMSD) of the docked conformation compare to the crystal conformation at a certain cutoff generally 2 et al. 1999). The scoring function is the binding free energy. (Moult. and it is directly related to the. equilibrium constant of the complex (Moult et al. 1999). There are three types of scoring function that. 21.

(22) are use in docking, namely, force field based function, empirical scoring function, and knowledge based scoring functions (Moult et al. 1999). Force field scoring functions are semiempirical, and use the intermolecular terms of a force field to estimate the complementarity of each ligand conformation to the receptor molecule. Van der waals interactions are estimated using Lennard-Jones potential, electrostatic interactions are taken into account by coulomb equation, and solvent effects can be estimated using distant-depend dielectric constant (Moult et al. 1999). Empirical scoring functions are derived by collecting terms that play a role in protein-ligand interactions and modeling the equation to reproduce experimental binding free energies (Moult et al. 1999). Knowledge based scoring functions are derived based on observed frequencies of interactions seen in protein ligand complexes. These frequencies are translated into energies using Boltzmann distributions: The more frequently observed the more important interaction and thus the lower energy assigned (Moult et al. 1999). In recent years, several studies have been published that compare different docking programs, or different combinations of search algorithms and scoring functions. Also, their performance in binding mode generation and virtual screening. The success rate for flexible ligand docking seems to be between 50-60%. Programs with empirical scoring function give the best results. However, it is difficult to assess the true success rate for these functions, because it is not always known how many complexes are present in the test set (Moult et al. 1999).. 4. MATERIALS AND METHODS 4.1.. Metagenomic library construction. The metagenomic library was constructed by GEBIX® using DNA extracted from different environments of Parque Nacional de los Nevados in Colombia. The collection of genetic material was made in four different ecosystems of the park: snow, wilderness, super-wilderness, and high-Andean forest. The two conditions of the collection were rain and drought. The DNA was purified and amplified by Multiple Displacement Amplification (MDA) using Phi 29 phage. The amplified DNA was cloned in pCC2FOS™ fosmids and inserted in Escherichia coli EPI300™ from CopyControlTM (Park et al. 2008). In this study the high-Andean forest library was harnessed. 4.2.. Oil palm empty fruit bunch pretreatment. The oil palm empty fruit bunch was shredded by grinding in a hammer mill. Then a diluted acid prehydrolysis was carried out, by soaking the oil palm empty fruit bunch in 1% (w/v) H 2SO4 (100ml for each 5 g of oil palm empty fruit bunch) for 1 h, followed by autoclaving the solution at 121 ºC for 15. 22.

(23) min. The pretreated oil palm empty fruit bunch was filtered and washed with deionized water to a pH of 6.4. Then, it was dried in an oven at 45 °C for 24h (Park et al. 2008). 4.3.. Functional screening of high-Andean forest library. Two different pools of the metagenomics library were cultured in minimal media with 1% w/v of oil palm empty fruit bunch pretreated or 1%w/v filter paper watman Nº 1 as carbon source, supplemented with 12.5 mg/ml of chloramphenicol. The cultures were shacked at 30 ºC and 200 rpm for a month, in order to obtain only the surviving cells 100 µl of each culture were transferred to solid LB media with 12.5 mg/ml of chloramphenicol in a 1:100 dilution and were incubated for 16h at 37 ºC. The colonies were shifted one by one to a petri plates containing minimal media with 0.2% CMC, 17 g/l agar and 12 mg/ml of chloramphenicol in a 5x5 array. The plates were incubated at 37 ºC for 10 days. The visual detection of cellulolytic clones was performed using aqueous congo red solution covering the plates for 60 min and washing with excess of 1M NaCl solution. 4.4.. Growth curves. The positive clones were cultured in four different media and the OD600 was registered each 1.5 h by 24h. The media used were: minimal media with 1%w/v of oil palm empty fruit bunch, minimal media with 1%w/v of filter paper watman Nº1, LB 50% with 0.2% CMC, and minimal media with 0.2% CMC. All media were supplemented with 12.5 mg/ml of chloramphenicol. 4.5.. Selection of clones with oil palm empty fruit bunch degradation capacity. In order to corroborate the presence of cellulases two assays were carried out: growth curve assays and quantification of sugars on medium by high performance liquid chromatography (HPLC). Growth curves on two different media were evaluated for each positive colony. The tested media were MM with OPEFB (1% w/v) as carbon source and LB 50% with 0.2% of CMC. All media were supplemented with 12.5 µg/ml of chloramphenicol. E. coli EPI300 with the fosmids without metegenomic insert was used as a negative control. With the purpose of performing sugar quantification on medium, the clones were grown on MM with addition of OPEFB (1% w/v) and after overnight incubation the samples were filtrated to eliminate the OPEFB. HPLC assays were carried out with Aminex® HPX-87P column. The standard solutions were: pure CMC (2 mg/ml), pure Cellubiose (2 mg/ml), pure glucose (2 mg/ml) and mixed solutions of these (1.2 mg/ml, 0.8 mg/ml, 0.5 mg/ml, 0.25 mg/ml, 0.125 mg/ml).. 23.

(24) 4.6.. Characterization of cellulases. Effect of metal ions Four different ionic salts (MgCl2, CuSO4, ZnSO4 and KCl) were mixed with 1% w/v of oil palm empty fruit bunch cellobiose and avicel in buffer McIllvaine pH 5.5 to a final concentration of 10 mM. Samples were incubated at 50°C for 2 h. Cellulases activities were determined by the reducing sugars production using DNS, sulphuric acid-phenol, and Glucose oxidase methods. The experiments were performed by duplicate. Influence of pH on cellulases activity. The influence of pH on the enzymes was determined performing hydrolysis experiments over oil palm empty fruit bunch under different pH conditions. For this purpose seven different buffers were used (KCl-HCl pH 1, KCl-HCl pH 2.5, McIllvaine pH 4, McIllvaine pH 5.5, McIllvaine pH 7, Tris-HCl pH 8.5, Borax-NaOH pH10) and incubation of the reaction at 50°C for 2h. Reducing sugars production were measured using DNS, sulphuric acid-phenol, and Glucose oxidase methods. The experiments were performed by duplicate. Influence of temperature on cellulases activity The influence of temperature on enzymes was determined performing hydrolysis experiments over oil palm empty fruit bunch. These experiments were carried out under the pH conditions that allow the highest production of sugars for each clone. The temperatures tested were 10 °C, 20 °C, 30 °C, 40 °C, 50 °C, 60 °C,and 70 °C with an incubation of 2h. Reducing sugars production were measured using DNS, phenol- sulphuric acid, and glucose oxidase methods. The experiments were performed by duplicate. 4.7.. Determination of kinetic parameters. In order to determine the kinetic parameters for the cellulases, the production of fermentable sugars were measured in time at different initial substrate concentrations.. For each experiment,. corresponding to each clone, five concentrations of oil palm empty fruit bunch were used (2.5%, 3.5%, 5%, 6.25%, 7.5%) suspended in buffer according to the conditions of metal ions, temperature, and pH that previously allowed the highest cellulolytic activities. The enzyme extracts were added at time cero and samples of the liquid media were taken each 3 minutes for 30 minutes in order to determine the sugars formed. The concentration of cellobiose and glucose were measured with phenolsulphuric acid method. The experiments were performed by duplicate.. The profiles of sugars. concentration were fitted to the Michaelis-Menten model and to an empirical fractal-like kinetics model developed by (Väljamäe et al. 2003).. 24.

(25) 4.8.. Concentration of cellulases. Clones were growth in minimal media with 0.1% of oil palm empty fruit bunch for 5 h (half of the exponential phase) at 37 ºC and 250 rpm. The oil palm empty fruit bunch was filtered and the cells were recovery by centrifugation at 4500 rpm for 20 min. The pellets were resuspended in Buffer TrisHCL pH 7.7 supplemented with EDTA and Triton. The cells were lysed by bead beating for 5 minutes and then centrifuged by 15 minutes. The pellets were discarded and the supernatants were ultrafiltered in a centrifuge with a 10- kDa nylon membrane Centriprep® from PallTM at 4500 rpm for 60 minutes. To determine the molecular weight of the cellulases, aliquots of 100 µl of the protein fractions above and below the membrane were taken. Incubation with 5% of oil palm empty fruit bunch for 2h was carried out. Reducing sugars and glucose concentrations were measured by phenol-sulphuric acid and glucose oxidase methods respectively. 4.9.. Fosmids extraction and sequencing. The fosmids extractions were carried out using PureLink™ Quick Plasmid Miniprep Kit from Invitrogen® according to the manufacturer`s protocol, to a final preparation volume of 60 µl. A verification of the DNA quality was achieved by 2% agarose gel in 1X TAE buffer for 2 h with an empty pCC2FOS™ as high weight molecular marker (36 kbp). The quantity and quality were analyzed using Nanodrop® (absorbance 260/280). The fosmids were sequenced by Ion TorrentTM technology with 314 chip. 4.10.. Bioinformatics analysis of the sequences. Determination of sequences that code for cellulases in each clone To obtain only the sequences that codes for cellulases in the metagenomic insert, the first ,the reads were trimming in Galaxy® (Giardine et al. 2005) to a final longitude of 150 pb which had the allowed quality for assembly (quality value of 20). Afterwards, the sequences were mapped with the Escherichia coli K12 genome and the pCC2FOSTM sequence as templates. The mapped sequences were removed with the aim of cleaning the metagenomics insert from contaminant DNA. The assembly was performed de novo in command line in CLC bio®.The contigs obtained after assembly were aligned with the non-redundant protein sequences (nr) data base from NCBI by BLASTX (Tatusova and Madden 1999) in command line.. Three-dimensional modeling of cellulases In order to determine the tertiary structures of the enzymes, the sequences that aligned with cellulases were selected and modeled by homology in Swissmodel® (Schwede et al. 2003). Afterwards the structures were geometrically optimized in Hyperchem® (Gutowska et al. 2005) using Polak-Riviere algorithm and a RMS gradient of 0.1 kcal/(Å mol) as termination condition. Then assessment of each. 25.

(26) protein model with the PROCHECK analysis was achieved in Swissmodel® (Laskowski et al. 1993; Schwede et al. 2003).. Molecular docking The simulations of molecular docking were performed with to aims: First, to determine if there are spontaneous complexes formations between the cellulases found and the cellulosic molecules (in terms of energy) and which aminoacids are important in the complexation process. Second, to establish the preferences of the enzymes for one type of cellulose or other and compare the interactions that occur in ech complex with complexes with the same substrate but with other cellulases reported. For the simulations, the first step was the preparation of molecules in AutodockTools ® (Morris et al. 2009). For the cellulases it was necessary to eliminate the water molecules and the addition of hydrogen atoms and Gasteiger charges. The ligands used were cellobiose and a chain of five glucose molecules. The box size for the simulations was 126x126x126 Å in such a way that cover the entire protein (blind docking). The distance among the net points was 0.375 Å, which is one quarter of the length of a C-C bond. The energy function used by Autodock® follows (Morris et al. 2009) :. (1) Were ΔG is the free energy change, V is the de sum of Vander Waals interactions potential, hydrogen bond potential, electrostatic potential, and solvation potential. L-L is ligand-ligand interaction, P-P is protein- protein interaction, P-L is the protein-ligand interaction, ΔS is the conformational energy change. Genetic algorithm was used as search algorithm with a number of 10000 evaluations.. 26.

(27) 5. RESULTS 5.1.. Selection of clones with cellulolytic activity. With the functional screening thirteen clones were identified (named 1 to 13) with presumed cellulolytic activity. Growth curves of these clones in different media evidenced the ability of most of them to grow in cellulose as main carbon source (Fig 6).. Clone 1. 1,7. A. Clone 1. Clone 2. B. 1. Clone 3. Clone 3. Clone 4. 1,3. Absorbance OD600. Clone 5. 1,1. Clone 6. 0,9. Clone 7. 0,7. Clone 8 Clone 9. 0,5. Clone 10. 0,3. Clone 11. 0,1. Clone 12. 0. 5. 10. 15. 20. 25. Time (h). C. 1. 0,6. Clone 9. 0,2. Clone 10 Clone 11. 0 5. 0,4. 25. empty fosmid. Clone 4. 1,4. Clone 5. 1,2. Clone 6. Clone 2 Clone 3. D. Clone 4. Clone 7. 1. Clone 8. 0,8. Clone 9. 0,6. Clone 10. 0,4. Clone 11. 0,2. Clone 12 Clone 13. 0. Clone 12. 0. Clone 13. Time (h). Clone 13. Time (h). 1,6. Clone 11. 20. Clone 12. 20. Clone 3. Clone 10. 0. 15. 2. Clone 9. 0,2. 10. 1,8. Clone 8. 15. Clone 8. Clone 2. Clone 7. 10. Clone 7. 0,4. Clone 1. Clone 6. 5. Clone 6. 0. Clone 5. 0,8. 0. Clone 5. 0,6. empty fosmid. 1,4 1,2. Clone 4. 0,8. Clone 13. Absorbance OD600. Absorbance OD600. 1,5. Absorbance OD600. 1,2. Clone 2. empty fosmid. 5. 10. 15. Time (h). 20. 25. Clone 1 empty fosmid. Figure 6. Growth curves of thirteen clones in different culture media. A) Minimal media with oil palm empty fruit bunch. B) Minimal media with filter paper watman Nº1. C) Minimal media with 0.2% of CMC. D) LB50% with 0.2% of CMC.. The growth curves for the thirteen clones are different in all media tested which is a sign that the clones are different among them and therefore the metagenomic insert is also different. The clones with the lower growth were discarded (clones 1, 2 and 10). To the remaining 10 clones, sugar concentration was measure directly in the culture media with HPLC. Interestingly we found that in four of the ten clones (4, 8, 12, 13) a peak appears in a retention time of approximately 10.3 minutes which correspond to cellobiose, thus indicating the possible presence of exoglucanases (Fig 7).. 27.

(28) B. A. C. D. Figure 7. HPLC results for A) clone 4, B) clone 8, C) clone 12 D) clone 13,. At approximately 10.3 minutes of residence time in column appears a peak corresponding to cellobiose.. 5.2.. Kinetic characterization of cellulases. For the kinetic characterization of the enzymes clones 12 and 13 were selected randomly.. Effect of metal ions on cellulases activity The ion metal that had the strongest effect over the cellulases was zinc for the selected clones. The effect was determined observing the production reducing sugars under different ion salt conditions (Fig.8). These results indicate the possibility of a dependence of some of the cellulases to metals, which may be an important factor to increase the cellulolytic activity of these enzymes. The metals selected were used for the evaluation experiments of pH and temperature.. 28.

(29) [Reducing sugars](μg/ml). 350 300 250. MgCl2. 200. CuSO4. 150. ZnSO4. 100. KCl. 50. (-). 0 12. 13 Clone. Figure 8. Evaluation of the effect of different ion metals over the cellulolytic activity of the enzymes based on production of reducing sugars.. Effect of the pH on cellulases activity. For the selected clones it was observe that the metal ion presence change considerably the range of pH in which the cellulases can be actives (Fig 9). The pH that allowed the highest production of reducing sugars in clone 12 was pH 5.5 in presence of zinc and pH 8.5 in absence of zinc. Is possible to notice that the cellulases that come from this clone can be active in a range from pH 4 to pH 8.5 without zinc and in a more basic range, from pH 7 to pH 10, when the reaction includes zinc (Fig 9A). Nevertheless, for this clone, the concentration of sugars is greater when the reaction is conducted in absence of the ion zinc. With respect to the clone 13 the pH that allows the highest activities was pH 7 in presence of zinc and pH 10 in absence of zinc. When the hydrolysis reactions include this ion metal, the range of pH in which the cellulases that come from clone 13, is from pH 4 to pH 8.5. In contrast without zinc the range is narrow, from pH 8.5 to pH 10 and the production of sugars decreases (Fig 9B). For this clone the production of reducing sugars increase when the reaction is carried out including a salt of zinc.. 29.

(30) [Reducing sugars] μg/ml. 450 400 350. A. 300 250 200 150 100 50 0 1. 2,5. 4. 5,5. 7. 8,5. 10. 7. 8,5. 10. [Reducing sugars] μg/ml. pH. 200 180 160 140 120 100 80 60 40 20 0. B. 1. 2,5. 4. 5,5. pH. Figure 9. Reducing sugars produced by cellulases at different pH. A) clone 12 B) clone 13. Dashed line: without metal. Solid line: with metal. Effect of the Temperature on cellulases activity. The effect of temperature was measured using the conditions of pH found in the experiments discussed above. For clone 12 in presence of zinc the range of temperature in which the cellulases are active is from 20ºC to 60ºC with a highest activity at 40ºC. In absence of zinc the range is from 40ºC to 60ºC with the highest activity at 50 ºC (Fig 10A). However as it was previously discussed for clone 12, there is a greater production of fermentable sugars when the hydrolysis of the oil palm empty fruit bunch is in absence of zinc which indicates that this ion is maybe changing the structure of the proteins unfavorably and thus the hydrolysis decreases. The cellulases that come from clone 13, in presence of zinc are active in a range that comes from 10ºC to 60ºC with a highest activity at 40ºC and in absence of zinc the from 10ºC to 40ºC without zinc with the highest activity at 50ºC (Fig 10B). Is possible to notice that for this clone the presence of the. 30.

(31) ion metal promotes the production of reducing sugars 2.5 times. This may indicate that the cellulases codes for this clone are using the zinc as a cofactor.. [Reducing sugars] μg/ml. 450 400. A. 350 300 250 200 150 100 50 0 10. 20. 30. 40. 50. 60. 70. 60. 70. [Reducing sugars] μg/ml. Temperature ( C). 450 400 350 300 250 200 150 100 50 0. B. 10. 20. 30. 40. 50. Temperature ( C). Figure 10. Reducing sugars produced by cellulases at different Temperatures. A) clone 12 B) clone 13. Dashed line: without metal. Solid line: with metal. The conditions of pH and temperature found are in the usual range for cellulases reported as is shown in Table 1. However it is not very common that cellulases depend on metals to improve its activity which is an interesting result (Chinedu et al. 2008; Ng et al. 2010).. 31.

(32) Table 1. Optimal pH and Temperature of cellulases reported. Cellulase. Organism. ph. Temperature. Reference. β-glucosidase. Penicillium. 5. 60ºC. (Hadj-Taieb et al.. occitanis Exoglucanase. Sporotrichum. 1992) 6. 65ºC. (Grajek 1987). 5. 60ºC. (Grajek 1987). 5. 65ºC. (Grajek 1987). 4. 35ºC. (Sohail et al. 2009). 5. 50ºC. (Liu et al. 2011). 6. 45ºC. (Baig et al. 2005). 7. 50ºC. (Li and Calza 1991). 4. 50ºC. (Kalogeris et al.. thermophile Endoglucanase. Sporotrichum thermophile. β-glucosidase. Sporotrichum thermophile. Endoglucanase. Aspergillus niger. Exoglucanase. Aspergillus fumigatus. Exoglucanase. Trichoderma lignorum. Endoglucanase. Neocallimastix frontalis. Endoglucanase. Thermoascus aurantiacus. β-glucosidase. Thermoascus. 2003) 4. 40ºC. (Kalogeris et al.. aurantiacus. 2003). Kinetic parameters determination The profiles of reducing sugars concentration were performed with the conditions of pH and temperature, and ion metal selected previously. According to the Michaelis-Menten model, the slope of the product concentration profile (that must be a straight line) at a given substrate concentration, is the initial velocity. Afterwards the initial velocities are plotted vs the different substrate concentration to determine the parameters Km and Vmax. Nonetheless the hydrolysis. profiles obtained have a. different tendency (Fig. 11) that is not adjusted to the Michaelis-Menten model. This is because Michaelis-Menten assumes that the reaction follows the traditional mass action kinetics, and the system is completely homogeneous (Xu and Ding 2007; Savageau 1995).. 32.

(33) 180. 300. [Reducing sugars] (μg/ml). [Reducing sugars] (μg/ml). 350. A. 250. 200 150. 100 50 0 0. 5. 10. 15. 20. 160. B. 140 120 100. 80 60. 40 20. 0 0. 25. 5. 10. 20. 25. 250. 160. C. 140. [Reducing sugars] (μg/ml). [Reducing sugars] (μg/ml). 15. Time (min). Time (min). 120. 100 80 60 40. 20. D 200 150. 100 50 0. 0 0. 5. 10. 15. 20. 25. 0. 5. 10. 15. 20. 25. 30. Time (min). Time (min). Figure 11. Hydrolysis of oil palm empty fruit bunch vs time A) Clone 12 Without zinc B) Clone 12 with zinc. C) Clone 13 Without zinc. D) Clone 13 with zinc. (■) 7.5% of substrate (●) 6.25% of substrate (∆) 5% of substrate. The continous lines are according to Eq (2) . Solid line; 7.5% of substrate. Dashed line; 6.25% of substrate. Dotted line; 5% of substrate.. In contrast, the reaction that is presented with cellulases over oil palm empty fruit bunch is completely heterogeneous and biphasic because of the solid nature of the substrate which make the reaction diffusion limited (Xu and Ding 2007; Yao et al. 2011; Bansal et al. 2009). With this conditions the rate coefficient k is time dependent and is related to the classical rate constant k by k = k * t-h, where h is a fractal kinetics exponent. In a three dimensional homogeneous space h = 0 and thus k is a constant. For a typical fractal system h has a value around 0.33 (Väljamäe et al. 2003; Bansal et al. 2009). In Väljamäe et al 2003 hydrolysis kinetics is described by empirical equation for the fractal like kinetics analogue of pseudo first order reaction :. P(t ). [ S ]0 1 exp. kt (1. h). (2). Where P(t) is the product concentration in μg/ml, [S]0 is the initial substrate concentration in μg/ml; t is time; k and h are empirical constants. The hydrolysis profiles for each oil palm empty fruit bunch concentration were fitted with the model showed in Eq 2. The distance between the experimental data and the model was measured by least squares differences (Table 2.), which demonstrated an acceptable tolerance for the adjustment.. 33.

(34) Table 2. Least squares differences for the adjustment of fractal-like kinetic model to the hydrolysis profiles. Least squares. Least squares. Least squares. difference. difference. difference. (5% substrate). (6.25% substrate). (7.5% substrate). 12 without Zn2+. 1992.2. 1378.3. 2221.3. 12 with Zn2+. 1375.5. 1045.8. 4596.7. 13 without Zn2+. 1173.2. 1398.9. 1377.9. 837.8. 854.12. 718.6. Clone. 13 with Zn. 2+. Is possible to notice, that increasing the substrate concentration in the reaction, are obtained more reducing sugars as we expected. As it was discussed in a previous section, for clone 12 the product concentration is greater when the reaction is carried out without zinc. In contrast, for the clone 13 this metal has a positive effect over the production of sugars, which corroborate our earlier results. Nonlinear regression of the hydrolysis data according to Eq (2) showed different value of h depending on the substrate concentration (Table 3).. Table 3. Kinetic parameters for hydrolysis profiles based on fractal-like kinetic model. h ( 5% oil. h ( 6,5% oil. h (7.5% oil palm. palm empty. palm empty. empty fruit. fruit bunch). fruit bunch). bunch). 12 without Zn2+. 0,17231975. 0,197796116. 0,202909506. 0,00035683. 12 with Zn2+. 0,158467313. 0,17950019. 0,153849336. 0,00015387. 0,375697703. 0,322488468. 0,40237537. 0,000273801. 0,32270063. 0,271965323. 0,246190812. 0,000216732. Clone. 13 without Zn 13 with Zn. 2+. 2+. K. These values do not show a clear tendency for the cellulases that come from clones 12 and 13 with and without zinc respectively. This is due to the low concentration of sugars produced in the reaction, making it difficult to detect by conventional methods as phenol-sulphuric acid. The measurements error affects the model adjustment and therefore the parametric regression loses accuracy. In contrast the value of the h parameter for the cellulases that come from cones 12 and 13 without and with zinc. 34.

(35) respectively have a clear tendency.. For the clone 12, h increases its value as the substrate. concentration increase. This implies that for the highest substrate concentrations the rate of product formation reaches a constant value faster than the lowest substrate concentration; which means it takes less time to approach the saturation point. On the other hand for the clone 13, h decreases with increasing substrate concentration. It implies that the saturation point for the reaction at high substrate concentration is reached after the reactions at low substrate concentrations show a constant product formation rate. It is possible that in this reaction, maybe there is more than one cellulase acting over the oil palm fiber. 5.3.. Concentration of cellulases. From measurements of sugars concentration produced by proteins that were above and below the nylon membrane (Table 4 and 5), it was established that cellulases were focused mainly in the zone above the membrane, thereby it could be concluded that the cellulases have a size greater than 10kDa. Furthermore, it was observed that the concentration of glucose is very low in contrast with the concentration of reducing sugars that is more than 16 times higher. With these results is possible to assert that the main sugar produced by cellulases is cellobiose, which once again suggest the presence of enzymes with exoglucanase activity.. Table 4: Reducing sugars concentration produced by proteins in zones above and below the membrane after ultrafiltration Clone. 12. 13. [Reducing sugars] (µg/ml) above the membrane. 0,6524. 0,5530. [Reducing sugars] (µg/ml) below de membrane. 0,3476. 0,3621. Table 5: Glucose concentration produced by proteins in zones above and below the membrane after ultrafiltration Clone. 12. 13. [Glucose] (µg/ml) above the membrane. 0,0096. 0,019. [Glucose] (µg/ml) below de membrane. 0,0061. 0,0046. Additionally, the concentration of proteins before ultrafiltration and after ultrafiltration was measured (Table 6). It was found after ultrafiltration the proteins are more concentrated and certainly cleaner of impurities as salts and ions that have a size smaller than 10kDa.. 35.

(36) Table 6. Concentration of proteins before and after ultrafiltration. Clone. 5.4.. Concentration of proteins (mg/ml) Before ultrafiltration. After ultrafiltration. 12. 6,89. 9,01. 13. 7,81. 11,13. Fosmid extraction. After fosmids extraction, DNA quality was evaluated by 2% agarose gel electrophoresis and 260/280 absorbance reads. The gel analysis shows no host-cell chromosomal DNA and no fosmid isoforms (opencircular DNA) which is an indicative of the fosmid preparation good quality (Fig. 12). This could be proven quantitatively by absorbance readings (260/280) which are all above 1.8 (Table 7) indicating a high-quality DNA. Also, the quantity of DNA was measured by Nanodrop®. It was found that all the fosmid preparations have more than 5 µg of DNA (Table 7) which is enough for sequencing.. Figure 12. Examination of fosmids extraction. DNA was isolated form 250 ml overnight culture of each clone. An aliquote of 3 µl of each 60µl fosmid preparation was analyzed on a 2% agarose gel in 1X TAE buffer for 2h. lane 1 corresponds to clone 4, lane 2 corresponds to clone 8, clone 3 correspond to 36 kbp fosmid (without insert) used as DNA ladder, lane 4 correspons to clone 12 and lane 5 correspons to clone 13. The 36kbp fosmid has a concentration of 80 ng/µl. 36.

(37) Table 7. Quantity concentration and quality of fosmids preparations measured by Nanodrop® Fosmid. Quality (260/280). Concentration (ng/µl). Total quantity in 60 µl (µg). 12. 1.97. 278.5. 16.61. 13. 1.87. 168.9. 10.13. 5.5.. Bioinformatics analysis. After BlastX the contigs that aligned with cellulases with an homology percentage above 60% were modeled in order to obtain an approximation of its 3D structure. It was found that clone 12 contains a contig that aligned with one cellulase: a 6-phospho-β-glucosidase from Shigella flexneri. The tertiary structure of this cellulase was modeled with the chain A of a cellulase that comes from Hordeun vulgare (PDB id: 1iexA) as template. For the clone 13 it was found two contigs that aligned with other two different cellulases: a putative endoglucanase with Zn-dependent domine from Escherichia coli and a β-glucosidase (Cellobiase) from Escherichia coli. These cellulases were modeled with and hydrolase from Pyrococcus horikoshii (PDB id: 1xfo) and with a β-glucosidase from Escherichia coli (PDB id: 2xhyA) respectively. Interestingly, clone 13 has a zinc-dependent enzyme which is the metal that most affect the cellulolytic activity of this clone corroborating our previous findings. The 3D models (Fig. 13) were minimized geometrically in Hyperchem®. With this procedure were obtained the coordinates of the proteins that make them be in its lowest energy level. Afterwards the minimized models were evaluated using the Qmean statistic and the PROCHECK structure assessment. C. B A. Z. Figure 13. 3D structures of cellulases modeled by homology. A) clone 12 β-glucosidase. B) clon 13 putative endoglucanase with Zn-dependent domine. C) clon 13 β-glucosidase (Cellobiase).. 37.

(38) With a PROCHECK analysis are obtained Ramachandran plots for the protein and for each aminoacid (Laskowski et al. 1993).. These results show that Qmean punctuations are above 0.5 (Table 8.) which means that the modeled 3D structures of cellulases are acceptable because the aminoacids do not have energetic problems.. Table 8. Qmean scores and Ramachandran Plots statistics from PROCHECK analysis for cellulases from clones 12 and 13.. Cellulase. Qmean. %Residues in most favoured regions. %Residues in additional allowed regions. %Residues in generously allowed regions. %Residues in disallowed regions. 85.9%. 12.4%. 1.4%. 0.3%. 11%. 0.7%. 1.7%. 13%. 1.7%. 0.2%. 6-phosphoβglucosidase from clone 12. 0.63. Putative endoglucanase with Zn-dependent. 0.61. 86.7%. domine from clone 13 Β-glucosidase (Cellobiase) from clone. 0.63. 85.1%. 13. To verify the good quality of the models is also important to study the stereochemistry of the proteins by Ramachandran plots. These plots allowed us to identify the aminoacids with geometrical problems by regions depending on the torsion angles of each aminoacid (Fig. 14). It was found that more than 85% of the aminoacids of the three cellulases are in the most favoured regions, and less than 2% of the aminoacids are in disallowed regions meaning that the proteins are close to their native structure.. 38.

(39) A. B. C. Figure 14. Ramachandran plots for 3D models of A) β-glucosidase from clone 12. B) Putative endoglucanase with Zn-dependent domine from clone 13. C) β-glucosidase (Cellobiase) from clone 13. In Docking simulations, for the endoglucanase a chain of five glucoses was used as ligand. For the βglucosidases there were used to types of ligand: a chain of five glucoses and a cellobiose molecule. This, with the aim to explore the possibility that these enzymes can hydrolyze longer glucose chains than cellobiose. It was found that the three cellulases interact with the ligands by hydrogen bonds and the ligands are located in cavities which are presumed active sites (Fig. 15-19). The important aminoacids for the interaction are listed in Table 9. It is possible to observe that polar aminoacids as Asparangine, Threonine, Proline or Glutamine are present in all complexes and form more than one hydrogen bond with the ligand. This make it truly important for the interaction and thus for the complex formation. The other hydrogen bonds are formed with the hydrophilic side chain of the aromatic and aliphatic aminoacids such as Tyrocine, Leucine, Glycine, Valine and Phenilalanine.. 39.

(40) Table 9. Important aminoacids and binding energy of complexes formed by cellulases from clones 12 and 13 with a chain of five glucoses and a cellobiose molecule as ligands. Complex. Important aminoacids for interaction. β-glucosidase from clone 12 with a. Ile109, Val71, Arg104. chain of five glucoses β-glucosidase from clone 12 with a. Leu19, Tyr340, Gly15, Phe345,. cellobiose molecule. Asn346. β-glucosidase from clone 13 with a a. Asn331, Val332, Val333, Asn323,. chain of five glucoses. Leu185, Glu268, Gly269. β-glucosidase from clone 13 with a. Pro101, His142, Phe55, Thr145,. cellobiose molecule. Glu146. Binding energy (kcal/mol). -3.96. -5.46. -3.78. -6.54. Endoglucanase with Zn-dependent domine from clone 13with a. Tyr327, Thr328, Pro270, Arg305. -3.68. cellobiose molecule.. In Table 9 are also listed the binding energies for the complexes. As may be noticed, all the energies are negative indicating that the complexes are formed as a spontaneous reaction. For the complexes formed between the β-glucosidases from clones 12 and 13 and the chain of five glucoses have the greater number of hydrogen bonds, (8 and 9 respectively) which causes that these complexes are more stable than those formed with cellobiose. However, these latter have the most negative binding energies, suggesting that their formation is more spontaneous. This can occur due to the small size of the cellobiose molecule which makes it more accessible to the protein, and thereby the interaction is more probable. Given these results, it cannot be discarded the possibility that the β-glucosidases can hydrolyze long glucose chains. The potential explanations for that phenomenon are: First, The βglucosidases have exoglucanases action, or second, the motives for cellulases obtained by BLASTX, can be also motives for exoglucanases. Moreover, these statements can explain the fact that experimentally we can read more cellobiose than glucose. . The β-glucosidase from clone 12 was compare with a β-glucosidase from Salmonella enterica (GI: 16761109). The β-glucosidase from clone 13 was compare with a β-glucosidase from Streptococcus anginosus (GI: 319939943). The. 40.

(41) endoglucanase Zn dependent was compared with a cellulase from Methanothermococcus okinawensis (GI: 336122540). The molecular docking results are shown in Table 10.. Table 10. Binding energy of complexes formed by cellulases reported with a chain of five glucoses and a cellobiose molecule as ligands. Complex. Binding energy (kcal/mol). β-glucosidase from Salmonella enterica with a chain of five glucoses. -3.78. β-glucosidase from Salmonella enterica with a cellobiose molecule. -6.07. β-glucosidase from Streptococcus anginosus with a chain of five glucoses. -3.08. β-glucosidase from Streptococcus anginosus with a cellobiose molecule. -7.53. Cellulase from Methanothermococcus okinawensis with a -4.23. cellobiose molecule.. The values of binding energies are similar than we found with the cellulases from clones 12 and 13. Given these results is possible to say that the cellulases found can hydrolyze cellobiose molecules and also can degrade long cellulose chains having an exoglucanase action. The endoglucanase from clone 13 in complex with the chain of five glucoses have a negative binding energy indicating that the reaction is spontaneous as we expected.. Figure 15. Complex formed by 6-phospho-β-glucosidase from clone 12 with a chain of five glucoses. Left is shown the hydrogen bonds and the aminoacids which interacts with the ligand. Right is shown the protein as a surface with the cavity in which the ligand is located.. 41.

(42) Figure 16. Complex formed by β-glucosidase from clone 12 with cellobiose as ligand. Left is shown the hydrogen bonds and the aminoacids which interacts with the ligand. Right is shown the protein as a surface with the cavity in which the ligand is located.. Figure 17. Complex formed by β-glucosidase from clone 13 with a chain of five glucoses. Left is shown the hydrogen bonds and the aminoacids which interacts with the ligand. Right is shown the protein as a surface with the cavity in which the ligand is located.. 42.

(43) Figure 18. Complex formed by β-glucosidase from clone 13 with a cellobiose molecule. Left is shown the hydrogen bonds and the aminoacids which interacts with the ligand. Right is shown the protein as a surface with the cavity in which the ligand is located.. Figure 19. Complex formed by endoglucanase with Zn-dependent domine from clone 13with a cellobiose molecule. Left is shown the hydrogen bonds and the aminoacids which interacts with the ligand. Right is shown the protein as a surface with the cavity in which the ligand is located. 43.

(44) 6. CONCLUSIONS The functional screening of metagenomic library and the measurement of sugars in culture media, allow as identify four clones with cellulolytic activity against oil palm empty fruit bunch. These clones are capable to hydrolyze the pretreated lignocellulosic material to more simple sugars as cellobiose. The temperature and pH values that allow the highest activities for the cellulases are in the usual range reported for these types of enzymes. Nevertheless the fact that the activity of one of them is improved by the presence of an ion metal is not very common. In the most of the reported studies about cellulolytic reactions in presence of ionic salts, the cellulases are inhibited (Clarke and Adams 1987; Kundu et al. 1988; Ferchak and Pye 1983). The kinetics obtained for the lignocellulolytic reaction cannot be adjusted to the standard enzymatic Michaelis-Menten model. Given the heterogeneous character of the reaction and the solid nature of the substrate, the kinetics can be fit to a fractal-like kinetics model that includes the adsorption and diffusion of cellulase on the substrate. Endoglucanases and β-glucosidases were found in the metagenomic DNA inserted in each clone. Nevertheless the molecular docking sowed that both endoglucanases and β-glucosidases form complexes spontaneously with branched chains of more than two glucoses, which is a typical phenomenon for exoglucanase activity which is concordant with the experimental results. This leads us to conclude that although by sequence analysis it was found β-glucosidases and endoglucanases it cannot be discarded that the motives found are for exoglucanases or the cellulases obtained have also exoglucanase activity.. Acknowledgements This research has been founded by the Universidad de los Andes and supported by Corpogen. The contributions of Laura Marcela Palma Medina were definitive for the development of this project.. 44.