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Enzymes are responsible for catalyzing reactions in a variety of biological processes in all living cells. It is well known that enzymes are highly efficient catalysts as they can accelerate reactions by as many as 17 orders of magnitude. The factors that enable enzymes to provide the large enhancement of reaction rates; however, still remain a matter of discussion. For more than a century, the activity of enzymes has been related to their structure; the "lock-and-key" and "induced-fit" hypotheses have suggested that the structural interactions between enzymes and the substrates play a role in enzyme catalysis. Such a view is incomplete as it fails to explain allosteric and cooperative effects, as well as the detailed mechanism of the large rate enhancement achieved by enzymes.
Many chemical transformation processes used in various industries have inherent drawbacks from a commercial and environmental point of view. Nonspecific reactions may result in poor product yields. High temperatures and/or high pressures needed to drive reactions lead to high energy costs and may require large volumes of cooling water downstream. Harsh and hazardous processes involving high temperatures, pressures, acidity, or alkalinity need high capital investment, and specially designed equipment and control systems. Unwanted by-products may prove difficult or costly to dispose of. High chemicals and energy consumption as well as harmful by-products have a negative impact on the environment.
Enzyme applications in detergents began in the early 1930s with the use of pancreatic enzymes in presoak solutions. It was the German scientist Otto Röhm who first patented the use of pancreatic enzymes in 1913. The enzymes were extracted from the pancreases of slaughtered animals and included proteases (trypsin and chymotrypsin), carboxypeptidases, alpha-amylases, lactases, sucrases, maltases, and lipases. Thus, with the exception of cellulases, the foundation was already laid in 1913 for the commercial use of enzymes in detergents. Today, enzymes are continuously growing in importance for detergent formulators.
The most widely used detergent enzymes are hydrolases, which remove soils formed from proteins, lipids, and polysaccharides. Cellulase is a type of hydrolase that provides fabric care through selective reactions not previously possible when washing clothes. Looking to the future, research is currently being carried out into the possibility of extending the types of enzymes used in detergents.Each of the major classes of detergent enzymes - proteases, lipases, amylases, mannanases, and cellulases - provides specific benefits for laundering and proteases and amylases for automatic dishwashing. Historically, proteases were the first to be used extensively in laundering. Today, they have been joined by lipases, amylases and mannanases in increasing the effectiveness of detergents, especially for household laundering at lower temperatures and, in industrial cleaning operations, at lower pH. Cellulases contribute to cleaning and overall fabric care by rejuvenating or maintaining the appearance of washed cotton-based garments.
Cellulases clean indirectly by hydrolyzing glycosidic bonds. In this way, particulate soils attached to cotton microfibrils are removed. But the most desirable effects of cellulases are greater softness and improved color brightness of worn cotton surfaces. Surfactants lower the surface tension at interfaces and enhance the repulsive force between the original soil, enzymatically degraded soil and fabric. Builders act to chelate, precipitate, or ion-exchange calcium and magnesium salts, to provide alkalinity, to prevent soil redeposition, to provide buffering capacity, and to inhibit corrosion.
The mode of action of cellulases is shown in Figure 1 . Denim garments are dyed with indigo, a dye that penetrates only the surface of the yarn, leaving the center light in color. The cellulose molecule binds to an exposed fibril (bundles of fibrils make up a fiber) on the surface of the yarn and hydrolyzes it, but leaving the interior part of the cotton fiber intact. When the cellulases partly hydrolyze the surface of the fiber, the blue indigo is released, aided by mechanical action, from the surface and light areas become visible, as desired. Both neutral cellulases acting at pH 6-8 and acid cellulases acting at pH 4-6 are used for the abrasion of denim. There are a number of cellulases available, each with its own special properties.
Figure 1: The mode of action of cellulose on denim
Enzymes have molecular weights ranging from about 12,000 to over 1 million dalton and demand physical space for movement and to be able to act on the much smaller functional groups in substrates. Enzymes are true catalysts. They greatly enhance the rate of specific chemical reactions that would otherwise occur only very slowly. They cannot change the equilibrium point of the reactions they promote. A reaction such as S ("substrate") P ("product") takes place because at a given temperature, there is at any instant a certain fraction of substrate molecules possessing sufficient internal energy to bring them to the top of the energy "hill" (see Figure 2(a) to a reactive form called the transition state. The activation energy of a reaction is the amount of energy required to bring all the molecules in one mole of a substance at a given temperature to the transition state at the top of the energy barrier. At this point there is an equal probability of them undergoing reaction to form the products or falling back into the pool of unreacted S molecules (see Figure 2(a). The rate of any chemical reaction is proportional to the concentration of the transition state species.
There are two general ways of increasing the rate of a chemical reaction. One is to increase the reaction temperature in order to increase the thermal motion of the molecules and, thus, increase the fraction having sufficient internal energy to enter the transition state. The second way of accelerating a chemical reaction is to add a catalyst, e.g., an enzyme. Catalysts enhance reaction rates by lowering activation energies.
In enzymatic reactions, binding groups and catalytic centers ("active sites") in enzyme molecules bind substrate molecules to form intermediate complexes with lower energy contents than those of the transition states of the uncatalyzed reactions. These complexes undergo certain atomic and electronic rearrangements, after which the products are released, see Figure 2(b). Thus, the enzymes work by providing alternative reaction pathways with lower activation energies than those of the uncatalyzed reaction, see Figure 2 (a). Mere recognition of a substrate is far from enough to guarantee that catalysis will take place - and if a bound compound is recognized, but no reaction takes place, it becomes an inhibitor rather than a substrate.
Figure 2(a): A comparison of the enzyme-catalyzed and the uncatalyzed reaction
(b): The course of an enzyme reaction
There are two different protein engineering approaches to increase the activity of enzyme, which is rational design and directed evolution. E.coli pyhtase, for example, has been studied and engineered to have greater activity by using both methods. E. coli has the highest specific activity of any phytase characterized, which 44KDa acid phosphatase isolated from the periplasm of E. coli is highly specific for phytate (811.2 U/mg) and is almost eight time higher than the A. neiger (102.5 U/mg) (Lim et al., 2000 and Lim, 1999). Based on rational design method, mutation suggested at position M216R and E219R shows the improvement in the binding strength while mutation at position H17A show decrease in binding and mutation at A116T have no effect in the binding strength. The verification of the transition states was determined by vibrational analysis that gives only one negative frequency value which is -1281.82 Kcal/mol. Due to the structure of cellulose has been known and the gene also is available in our laboratory, therefore, this research proposal intends to model and conduct computational engineering of cellulase from a thermophilic pathogenic plant fungus, Fusarium oxysporum by using rational design approach which can lead to be a new model for better activity cellulase structure and can be confirmed by laboratory work in the future.
Cellulose is commonly degraded by an enzyme called cellulase. This enzyme is produced by several microorganisms, commonly by bacteria and fungi (Bahkali, 1996; Magnelli and Forchiassin, 1999; Shin et al., 2000; Immanuel et al., 2006). Although a large number of microorganisms are capable of degrading cellulose, only a few of these produce significant quantities of cell free enzymes capable of completely hydrolysing crystalline cellulose invitro. Fungi are the main cellulase producing microorganisms, though a few bacteria and actinomycetes have also been reported to yield cellulase activity.
Fungal genera like Trichoderma and Aspergillus are taught to be cellulase producers and crude enzymes produced by these microorganisms are commercially available for agricultural use (Kazuhisa Miyamoto, 1997). In general, bacterial cellulases are constitutively produced, whereas fungal cellulase is produced only in the presence of cellulose (Suto and Tomito, 2001). Filamentous fungi particularly Aspergillus and Trichoderma spp. are well known efficient producers of cellulases (Peig et al., 1998).
Several studies were carried out to produce cellulolytic enzymes from biowaste degradation process by many microorganisms including fungi such as Trichoderma, Penicillium, Aspergillus spp. etc. by Mandels and Reese (1985), Hoffman and Wood (1985), Brown et al. (1987), Lakshmikant and Mathur (1990) etc. Similarly celluloytic property of bacterial species like Pseudomonas, Cellulomonas, Bacillus, Micrococcus, Cellovibrio and Sporosphytophaga spp. were also reported (Nakamura and Kappamura, 1982; Immanuel et al., 2006). The specific cellulolytic activity shown by the bacterial species is found to be depending on the source of occurrence (Saxena et al., 1993).
Some features of natural cllulosic materials are known to inhabit their degradation / bioconversion (Solomon et al., 1990 and 1999). These are degree of crystalinity, lignification and the capillary structure of cellulose to celluylolytic enzymes and other hydrolytic agents (Fan et al., 1987). However, many physical, chemical and microbial pre-treatment methods for enhancing bioconversion of cellulosic materials have been reported (Kumakura, 1997; Wu and Lee, 1997; Kanosh et al., 1999; Solomon et al., 1999).
Since the production of cellulase enzyme is a major process and economically viable, much work has been done on the production of cellulase from lignocellulosics and major attention has been given to use baggase as substrate (Kanosh et al., 1999; Solomon et al., 1999). The bioconversion of various complex cellulosic waste materials such as baggase (Kanosh et al., 1999), corncob (Ojumu et al., 2003); saw dust (Solomon et al., 1999) have been reported. Likewise coir fibres are major biowaste discarded along with coir retting effluent to estuarine environment. Yet literature related to coir fibre as a carbohydrate source and cellulolytic activity by microorganisms involved in coir retting process is not studied properly. Hence, the present study was carried out to determine the cellulolytic enzyme activity of fungi, Aspergillus niger and A. fumigatus against coir waste and saw dust as carbohydrate source.
Investigate chemical reactions in condensed phase environments. Exploit explicit treatment of electrons with QM approaches in combination with computationally cheap MM approach to treat condensed phase environment. (Field et al., 1990; Gao, 1996). A capacity to degrade cellulose is a character distributed among a wide variety of aerobic, facultative aerobic, anaerobic bacteria and fungi. The characters are restricted to a few species among several major taxa (Gooday, 1979). The important cellulolytic fungus like Trichoderma sp. (Wood and Mc Care, 1972; Shaw and Quejesky, 1979; Mandels and Reese, 1985); Penicillium Sp. (Hoffman and Wood, 1985; Brown et al., 1987); Sporotrichium Sp (Erikkson and Johnsrud, 1983); Aspergillus sp (Kazuhisa Miyamoto, 1997) etc have been reported to have cellulolytic activity.
In the present study, two fungal strains such as Aspergillus niger and A. fumigatus were isolated from the coir retting soil samples and selected as the major cellulytic fungal strains for cellulase enzyme production. The natural sources of cellulose degradation are varied and these may be investigated by several investigators. For eg. termits are the best cellulase degrader in soil from the tropic to desert, they stir and mix with the aid of bacteria, protozoa and fungi, thereby they effectively recycle cellulose. The degradation activity of termites is by microbes present in their intestine (Saxena et al., 1993), by mushroom eg. Lentinula edodes etc. (Jose Antonio et al., 2003). The carbon sources induce production of cellulase, but amount produced is variable. This is because of the influence of substrate (carbon source) on the growth of cellulolytic organisms (Mandels and Reese, 1985; Zhu et al., 1988; Lakshmikant and Mathur, 1990). In the present study, two different substrates such as saw dust and coir wastes were used as major carbon source. Apart from this, some environmental factors are also influenced the growth of organisms as well as maximum production of enzymes will be at certain optimum temperature, pH, salt concentration etc. (Immanuel et al., 2006).
In the present study, the effect on environmental factors such as pH and temperature against A. niger and A. fumigatus were analysed. The optimum temperature of cellulase enzyme was found to be around 40°C. This value is lower than that of commercial cellulase production (60°C) (Deerlands co-operation). On the other hand, endoglucanase from A. niger was reported to be stable at 50°C and the enzyme showed major peaks at pH 4.5 and 7.5. The result is probably due to the presence of two isoenzymes or subunits in enzyme preparation. It was reported that optimal pH for CMCase from A. niger was found to be 6.0 to 7.0 (Parry et al., 1983). But Akiba et al. (1995) reported that the production was high at pH 4 and 4.5 by A. niger. The ability of cellulase degrading fungi, A. niger and A. fumigatus on saw dust and coir waste substrates were performed in various pH using Dinitrosalicylic acid method and filter paper activity method. The results showed that in DNS method, cellulase enzyme production by both organisms was maximum (0.092 and 0.198 IU ml-1) at pH 5 with coir waste as substrate. While in saw dust used as carbon source, high level of production was found at pH 6 by both organisms (0.052 IU ml-1 by A. fumigatus and 0.038 IU ml-1 by A. niger).
Computer simulation techniques have become very important tools for understanding and exploring the physical basis of the structure and function of biomacromolecules. In a theoretical study the objective is to create a simplified model of a real physical system in order to reproduce known structural changes and dynamics behaviour of the related system. The application of computer simulation in the structural and dynamics studies of proteins and understanding the mechanisms of protein folding and unfolding at atomic details has been the subject of much research for many years. Both simplified and all-atom level molecular dynamics (MD) simulations is a common computational method in this area. All-atom level MD simulations with explicit solvent, however, are more preferred; having high resolution in time and space that enable detailed comparison between energetic and structural properties of a protein at various temperatures, and provide a large amount of information which is not directly accessible from the laboratory experiment (Karjiban et al., 2009).
Molecular dynamics simulation of a simple model system of the geminate radical pair in solution, an example of using Molecular dynamics method in solution, has been performed to elucidate the dynamic behavior of radicals. The diffusion process of radicals in the microscopic region was simulated and the effect of the dipole of the radicals was investigated in both the nonpolar and polar solvents. We found that the dipole-dipole interaction stabilized the radical pair with a small separation. The dipole pair can be the precursor of the experimentally observed sandwich radical dimer. The conformation of the dipole pair may not be favorable for the recombination of the radicals, which can be the reason of the high escape probability observed for the p-aminophenylthiyl radical (Hirata and Okada, 2000).
Molecular docking has become a useful tool in drug discovery efforts. Improvement in computing power and advances in the energy calculation techniques such as the introduction of a grid-based receptor field representation and the use of internal coordinates], make the simulations of continuously flexible ligands computationally feasible (Bursulaya et al, 2003). Docking program utilizes this observation and implements Metropolis Monte-Carlo or genetic algorithms to search for the global minimum of the energy function in the continuous conformational space of the ligand (Bursulaya et al, 2003). It is clear that the systematic investigation of existing docking approaches would be helpful in selecting those algorithms and energy/scoring functions that are optimal (Bursulaya et al, 2003).
Enzyme catalyzed a reaction by decreasing the energy required to start a reaction. At the molecular level, it is achieve by understanding that the enzyme can store the energy from the binding of the substrate and was used later to make the catalysis more efficient (Thular, 1996). In 1940, new theory of chemistry to enzyme catalysis was introduced by Pauling which called the transition state theory (Gao et al, 2002). In this concept, an intermediate form exists between the substrates and products in an enzyme catalyzed reaction and this intermediate is called the transition-state intermediate (Gao et al, 2002).
Figure 3: Diagram illustrating the concept of transition-state intermediate in a chemical reaction
A number of commercial software packages are available for molecular docking are VegaZZ or AutoDock and Gaussin09 for transition state modeling. AutoDock is an automated procedure for predicting the interaction of ligands with biomacromolecular targets. The motivation for this work arises from problems in the design of bioactive compounds, and in particular the field of computer-aided drug design. Progress in biomolecular x-ray crystallography continues to provide important protein and nucleic acid structures. These structures could be targets for bioactive agents in the control of animal and plant diseases, or simply key to the understanding of fundamental aspects of biology. The precise interaction of such agents or candidate molecules with their targets is important in the development process. Our goal has been to provide a computational tool to assist researchers in the determination of biomolecular complexes.
In any docking scheme, two conflicting requirements must be balanced: the desire for a robust and accurate procedure, and the desire to keep the computational demands at a reasonable level. The ideal procedure would find the global minimum in the interaction energy between the substrate and the target protein, exploring all available degrees of freedom (DOF) for the system. However, it must also run on a laboratory workstation within an amount of time comparable to other computations that a structural researcher may undertake, such as a crystallographic refinement. In order to meet these demands a number of docking techniques simplify the docking procedure. AutoDock combines two methods to achieve these goals: rapid grid-based energy evaluation and efficient search of torsional freedom.
AutoDock predicts where a ligand binds on the surface of a macromolecule, such as a protein or DNA, whose tertiary structure is known.8 AutoDock treats the macromolecule as rigid, while the ligand is allowed torsional flexibility. Although conformational changes are often observed upon ligand binding to enzymes, the treatment in this case is a reasonable one, since human and yeast ERManI active sites are practically identical, and as binding of DMJ and KIF in the human ERManI active site causes insignificant side-chain rearrangements in their respective crystal structures.5 AutoDock computes the nonbonded interaction energy between ligand and macromolecule, the problem therefore being searching the ligand conformational space in the vicinity of the macromolecule to find the conformation with the lowest interaction energy. The AutoDock suite provides four different ways to search this conformational space: simulated annealing algorithm (SAA), genetic algorithm (GA), Lamarckian genetic algorithm (LGA), and local search (LS), the last based on the Solis and Wets (SW) method (Chandrika, 2006).
In addition to the ERManI study, we have used Auto- Dock to understand enzyme structure-function relationships in glucoamylase,19 b-amylase,20 surfactant protein D,21 and phospholipase D.22 Recently, substrate binding energies on docked substrates were complemented with computed forces on substrate atoms in crystal structures of cellulases Cel7A and Cel7B.23,24 The forces give insights on substrate dynamics in the active site, which cannot be inferred from the binding energies that Auto- Dock generally outputs (Chandrika, 2006).
VEGA ZZ is the evolution of the well-known VEGA OpenGL package and includes several new features and enhancements making our research jobs very easy. VEGA was originally developed to create a bridge between most of the molecular software packages only, but in the years, enhancing its features, it's evolved to a complete molecular modelling suite. GriDock was designed to perform the molecular dockings of a large number of ligands stored in a single database (SDF or Zip format) in the lowest possible time. It takes the full advantage of all local and remote CPUs through the MPICH2 technology, balancing the computational load between processors/grid nodes.
Gaussian 09 is the latest version of the Gaussian® series of electronic structure programs, used by chemists, chemical engineers, biochemists, physicists and other scientists worldwide. Starting from the fundamental laws of quantum mechanics, Gaussian 09 predicts the energies, molecular structures, vibrational frequencies and molecular properties of molecules and reactions in a wide variety of chemical environments. Gaussian 09's models can be applied to both stable species and compounds which are difficult or impossible to observe experimentally (e.g., short-lived intermediates and transition structures). Researchers have used these fundamental capabilities of Gaussian 09 to study isopenicillin N synthase (IPNS), a member of a family of mononuclear nonheme iron enzymes (illustrated at the bottom right of the next image). Transition metal enzymes catalyze some of the most important biochemical processes, and they can also serve as inspiration for novel biomimetic catalysis. In the latter context, these researchers wanted to determine how the metal center and the protein matrix separately contribute to the enzyme system's catalytic activity. They analyzed the catalytic mechanism of IPNS, exploring the potential energy surface for the transformation of the tripeptide substrate Î´-(l-Î±-aminoadipoyl)-l-cysteinyl-d-valine (ACV) to isopenicillin N (IPN). The ONIOM facility in Gaussian 09 enables the transition structures and reaction paths to be computed for the reactions involving large proteins like this system.
Figure 4: Reaction path using the ONIOM
The reactants (left), transition structure (center) and products (right), as well as the IRC reaction path, are all computed using the ONIOM facility. The highlighted inset focuses on the active atoms in the high accuracy layer, treated with density functional theory. The grey region outside is a tiny portion of the low accuracy layer, treated with molecular mechanics in the integrated QM: MM method (Lundberg, 2009).
Molecular mechanics is also known as the force-field or potential energy method. The Born-Oppenheimer is the approximation to the potential energy with respect to the potential energy and relate to the nuclei EP(X) which can use as the target function in molecular mechanics (X is collective position vector for the nuclei) (Tamara, 2001). An underlying principle in molecular mechanics is the cumulative physical forces can be used to describe molecular geometries and energies. A molecule is considered as a collection of masses centered at the nuclei (atom) connected by springs (bond) in respond to inter and intramolecular forces, the molecule stretches, bends and rotate about those bond (Tamara, 2001). This description of a molecular system as a mechanical body is usually associated with a "classical" system. This classical mechanics description is an appropriate characterization even as the amount of quantum mechanical information used to derive force fields increase the work generally well for describing molecular structure and processes with the exception of bong-breaking events (Tamara, 2001).
QM/MM simulations are useful tools to study the energetics of the reactions and analyze the active-site structures at different states of the catalysis, including the formation of unstable transition states. Here, a brief description of previous work on glycoside hydrolases is first given. The results of the QM/MM potential energy and free energy simulations corresponding to glycosylation and deglycosylation processes are then provided for two retaining endoglucanases, Cel12A and Cel5A. The active-site structural features are analyzed based on the QM/MM results (Moumita, 2010).
The common approach to obtain potential energy surfaces for chemical reactions is to use quantum chemical computational methods. Though these methods have become quite effective in treating small molecules in the gas phase (e.g. Pople 1999), we are here interested in chemical reactions in very large systems that at present cannot be explored by ab initio methods. Similarly, molecularmechanics(MM) simulations (e.g. Shurki &Warshel 2003), which have been proven to be very effective in exploring protein configurational space, cannot be used to describe bond-breaking and bondmaking reactions in proteins or solutions. The generic solution to this problem has been provided by developing hybrid quantum mechanics/molecular mechanics (QM/MM) approaches (Warshel & Levitt 1976). These approaches divide the simulation system (e.g. the enzyme/substrate complex) into two regions. The inner region, region I, contains the reacting fragments, which are represented quantum mechanically, whereas the surrounding protein/solvent region, region II, is represented by an MMforce field.
Molecular orbital (MO) QM/MM methods are now widely used in studies of complex systems in general, and enzymatic reactions in particular, and we can only mention severalworks (e.g. Field et al. 1990; The´ry et al. 1994; Bakowies & Thiel 1996; Gao 1996; Friesner & Beachy 1998; Monard & Merz 1999; Mulholland et al. 2000; Zhang et al. 2000; Cui et al. 2001; Garcia-Viloca et al. 2001; MartÄ±´ et al. 2001; Field 2002). Despite these advances, we are not yet at the stage where MO-QM/ MMapproaches can be used in fully quantitative studies of enzyme catalysis. One of the major problems is associated with the fact that evaluating the potential energy surface quantitatively for the reacting fragment requires ab initio electronic structure calculations, and such calculations are too expensive to allow for the configurational averaging needed for proper free energy calculations. Specialized approaches can help one move toward ab initio QM/MM free energy calculations (Strajbl et al. 2002; Olsson et al. 2003), but even these approaches are still in a development stage.
In terms of better understanding of cellulase usage in various applications, the characteristics of the enzyme (example: thermostable enzymes, enzymes active in extreme pH, enzymes using different types of substrates, etc.) are essential. Here, a thermophilic fungus (Fusarium oxysporum) is used as the gene source for the production of cellulase due to the better activity. However, the optimal temperature is only 45-65°C (Christakopoulos et al., 1995).
Although the cellulase from thermophilic fungus (Fusarium oxysporum) seems to be stable at high tempurature, the activity improvement of the enzyme is still needed for better industial application of cellulase. Computer simulation is useful method to enhance the activity of the enzymes from the structure before experiment in lab-scale. Here molecular dockings and transition state modeling program will be employed for available cellulase crystal structure computer simulation following lab-scale experiments. Thus the computational studies for increasing the activity of this enzyme lead to be the novel theoretical design model for engineering cellulase to improve the activity of this enzyme in our future work. This project involve computer modeling of cellulose with the objective of understanding the nature of the interactions involved in the active site and how it can be modified to improve the cellulase activity.
The primary goal of enzyme engineering is to find a sequence of amino acids that will stabilized a particular three-dimensional structure with desirable attributes (Truhlar et al, 1996). The increasing availability of crystal structure of enzyme complexes allowed the characterization of the interfaces between the enzyme in the complexes with the goal of understanding interaction which stabilize such complexes and determine specificity of the enzyme (Janin et al, 1998). The use of force field, which characterizes atomic interactions by classical physics, can provide a basis for molecular modeling and simulation (Kleist et al, 2003). Improvement in this approach through use of multiple electrostatic and polarization has finally provided a tool that can calculate thermodynamic properties of system (Kleist et al, 2003). Therefore it is efficient to calculate the affinity of one enzyme with confidence assuming that adequate degrees of freedom within the interacting molecule are explored. Combinations of molecular mechanics with quantum mechanics allow exploration of chemical in which bond length are variable by two rational approaches which are the site directed mutagenesis and directed evolution of the enzyme (Kleist et al, 2003).
The main objectives of this project are:
To perform computer simulation of a cellulase with the objective of increasing its activity.
To determine the mechanism of the cellulase activity.
To determine a cellulase whose 3D structure, active site and the reaction of mechanism is well known.
To improve the binding of substrate in active site by doing mutation on the phytase receptor at active site.
To perform a molecular docking by using either VegaZZ or AutoDock for docking.
To investigate an enzyme reaction modelling using QM/MM methods.
To design and simulate enzymes with better activity by study the transition state of the celluase reaction and celluase activation energy barrier by using Gaussian09 (ONIOM method).
Scope of Works
Scope this work is to verify that the mutation on the cellulase structure from Protein Data Bank decrease the strength of binding, determine which mutation has severe effect in decrease the binding strength, do mutation on residue at the active site to improve the binding strength of the ligand and to improve the activity of the enzyme using Computer Aided Design.
There are two main parts in this research which is to increase the activity of cellulase, the first is to design the molecular docking and the second is to determine the transition state modeling. The different methodologies that will be used to achieve the research objectives can be summarized as follows:
Acquiring crystal structure
The crystal structure of cellulase will obtain from the NCBI Entrez Protein database (http://www.ncbi.nlm.gov/entrez/).
Three dimensional crystal structure of cellulase is obtain from Protein Data Bank (PDB: www.rcsb.org/ pdb) that is maintained by the Research Collaboratory for Structural Bioinformatics (RCSB).
Cleaning protein structure
The new information is automatically calculated and added to the new .csf file once the PDB file was imported. For molecular modeling information such as hydrogen atoms, atom hybridization and correct bond types of hetero group (HET) group and non-standard residue is required. In this method, the bonding in HET group will be checked and corrected. Then the hydrogen atom will be added to the molecule and the atom hybridization will be defined. Lastly, the charge of the atom will be balanced. The structure is then optimized using molecular mechanics (MM3) until it reaches convergence threshold of 0.001 kcal/mol.
Analyzing enzyme, ligand and complexes
Properties of enzyme that contained thousands of atoms are best analyzed by representing molecular properties with graphics that reduce complexity. BioMedCAChe's unique Sequence View provide simple, fast and easy finding and visualizing of small molecule-protein interactions, such as hydrogen bonding. From this powerful tool, cellulase enzyme structure and the sequence can be viewed and analyzed by molecular graphics. This enable molecules and bonding to be easily sought and visualized easier and adjustment of the conformation structure of the enzyme aligned sequence is possible.
Next, the active site of the enzyme is identified by referring to Ligand Protein contact, amino acid residue within a distance of 7Å of the structure ligand is defines as an active site. Then the hydrogen bond between the ligand and protein is confirmed. The active site location is studied through accessible surfaces to observe the nature of the active site in terms of hydrophilicity and hydrophobicity areas and crevice surface able to pin point ideal location on the active site where potential binding is possible.
Automated docking and scoring
The active site of cellulose is well understood and the reaction mechanism is well known. Firstly, a copy of cellulase molecules is created in the cellulase chemical sample and automatically docks the cellulase copy into the cellulase active site using the VegaZZ or AutoDock for docking and Gaussian09 for transition state modelling. Docking model used in VegaZZ or AutoDock assumes that the protein and ligand dock non-covalently. It is assumed that bonds are not formed between the ligand and protein because bond formation would cause changes in the atom types and possible substantial changes in the shape of the ligand and active sites. Ligand and active site is set to be both flexible. Lastly the average score is taken as a record.
Simulated point mutation is done on the amino acid residue in the conserve active site of the enzyme, define 7Å from the ligand to determine the significance of each residue in altering binding affinity whether it's improving the binding strength of the enzyme by addition of the hydrogen bond between residue in the active site and ligand or reduce binding affinity. It can be done by doing amino acid substitutions, which selection and mutation process were done randomly. Each mutation will be optimizing until it reach the convergence threshold of 0.001 kcal/mol.
The effect of point mutation can be evaluated by comparison of the structure before and after mutation. Besides, comparisons of docking score, accessible surface and change in hydrogen bond can also be evaluated. To indicate the better binding affinity, the docking score should give more negative value. For the hydrogen, the value should be increase in number to indicate the binding is stronger.
Gaussian09 uses quantum mechanics/molecular mechanic (QM/MM methods) to investigate the conformation of enzyme design in a chemical reaction from reactant molecules to product molecules by computational experiment which is called "in silico experiment" that include transition state experiment. Firstly, the search for saddle experiment locates the stationary point between the reactant and product that corresponds to the transition state for the reaction. Before that, a reactant molecule and product molecule for input must be specified; therefore the transition state structure can be generated.
Next, the transition state structure can be refines using a minimized gradient calculation in the refine transition state experiment. Verification of transition state can be done by calculating the molecules vibrational transition to verify the current transition state geometry using the verify transition state experiment. A single negative vibrations conforms a true transition state.
Investigating reaction path structure
The reaction pathway is the minimum energy path from reactant to transition state structure to product. Gaussian09 varies two internal coordinates of the enzyme sample to form the principle deformation that occur in the course of a reaction. A series of reaction path are calculated between the transition state structure to either product or reactant. The direction for the reaction path calculation can be specified by choosing a vibrational mode for the initial motion of 1 or -1.
It is well known in this field of research that the final outcome is hard to predict. However, by the end of the research an increase in the activity of cellulase in term of molecular docking and transition state modeling is expected. Nonetheless, the information obtained by computer simulations and the introduction of new mutations will help increase the activity of cellulase by the same range. As a result, an increase of cellulase is anticipated where the new mutant cellulase is suitable for industrial applications. It will be summarized as below:
1. The 3-Dimentional model for cellulase
2. The active site of cellulase will be understood and the reaction of mechanism is well
3. The new design of cellulase shall have the better activity by using molecular docking and
transition state modeling.
4. Research papers presented in seminars/workshops.
5. Publications in journals.
Detergents currently represent one of the largest single markets for industrial enzymes, and so we begin our look into the future here. Enzymes have been responsible for numerous improvements in wash performance since the early 1960s. Enzymes have also contributed to more environmentally adapted washing and cleaning because they are biodegradable, they can replace harsh chemicals, and they reduce high temperatures in certain cases. Benefit of this project is new enzyme with better binding strength and activity had been produced.
Nevertheless, the process of washing laundry or dishes in a machine still requires large quantities of chemicals, energy, and water. Past developments have clearly shown that detergent formulations can be optimized based on biological systems. In future, this trend could lead to the development of effective detergent systems that use much smaller quantities of chemicals, less water, and less energy to attain maximum washing or cleaning performance. One possibility is the development of special dosing techniques that add active ingredients as and when they are needed at a particular stage in the washing or cleaning cycle and so enhance their performance.
The continued development of new enzymes through modern biotechnology may, for example, lead to enzyme products with improved cleaning effects at low temperatures. This could allow wash temperatures to be reduced, saving energy in countries where hot washes are still used. New and exciting enzyme applications are likely to bring benefits in other areas: less harm to the environment; greater efficiency; lower costs; lower energy consumption; and the enhancement of a product's properties. New enzyme molecules capable of achieving this will no doubt be developed through protein engineering and recombinant DNA techniques.
Industrial biotechnology has an important role to play in the way modern foods are processed. New ingredients and alternative solutions to current chemical processes will be the challenge for the enzyme industry. When compared with chemical reactions, the more specific and cleaner technologies made possible by enzyme-catalyzed processes will promote the continued trend towards natural processes in the production of food.
As computational protein engineering is becoming popular especially in biotechnology engineering, I would like to recommend that for future study, more effort can be concentrate at finding the effect of mutation on the activity or reactivity of the enzyme.
Research Flow Chart
See Appendix A
Research Milestone / Gantt Chart
See Appendix B