2. Theory of docking and virtual molecular dynamic

Within the process of living system, protein-ligand interactions have been known to play central roles. It has been considered interesting to obtain more comprehensive understanding of protein interactions with small molecules because it leads to better understanding of various functions and therapeutic intervention. As a matter of fact, molecular recognition is a complex interplay of several factors including inter-molecular interactions of protein, ligand and the surrounding solvent, conformational variations of binding partners and the thermodynamics of molecular association. The non-covalent reversible binding of small-molecules to proteins also plays a central role in the field of biology. Several processes are known crucial in living systems that involve specific recognition of small molecule ligands by proteins. For instance, certain enzymes affect their substrates and catalyze chemical reactions inside the cells, where transporters recognize specific molecules based on the movement across membrane barriers, receptors that are specifically bind to hormones or other chemical messengers for inter- and intracellular communication. Finally, antibodies uniquely can bind to other chemical agents to mount vital defense mechanisms against infections and diseases. In general, protein-ligand binding in an aqueous environment is described as follows.

$$\text{Protein } (\text{P})\_{\text{(aq)}} + \text{Ligand } (\text{L})\_{\text{(aq)}}\\\Delta \text{Protein } - \text{ Ligand } (\text{PL})\_{\text{(aq)}} \tag{1}$$

A change in the free energy (ΔG) is always followed by chemical reactions and change in two other important quantities; enthalpy (ΔH)—the heat content and entropy (ΔS) that showed disorder of temperature-independent degree. The relationship between these quantities is shown as follows:

$$
\Delta \mathbf{G}^\* = \Delta \mathbf{H} \text{-T} \Delta \mathbf{S} \tag{2}
$$

Some factors including electrostatic and van der Waals interactions, ionization effects, conformational changes and the role of solvent affect the changes in the binding of free energy. Those factors are manifested as either favorable or unfavorable changes in entropy and enthalpy. In order to create a spontaneous reaction, the free energy change should be negative at equilibrium, ΔG° which relates to the equilibrium constant (K) in this following expression:

$$
\Delta G^\circ = -\text{RTlıK} \tag{3}
$$

where R is the gas constant and T is the absolute temperature. Using this relationship, free energy changes can be derived from experimentally measurable quantity, K. Biological K values exhibit a wide range from weak to very strong binding.

sectors including national defense, energy, financial sectors and science. Within the global economic growth competition and the advancement in science and technology including the advancement in biology, chemistry, pharmacy and medicine, supercomputers play key roles. In-silico analysis has been developed as the compu-

Molecular interactions including protein-nucleic acid, drug-protein, proteinprotein, enzyme-substrate, and drug-nucleic acid play important roles in many essential biological processes, such as enzyme inhibition, signal transduction, antibody-antigen recognition, transport, gene expression control, cell regulation, up to multi-domain proteins assembly. Stable protein-protein or protein-ligand complexes are often produced by the interaction which complexes are considered essential in performing their biological functions. To determine the binding mode and affinity between interacting molecules, tertiary structure of proteins should be first identified. Unfortunately, conducting experiments to obtain complex structures has been considered challenging and expensive because the experiments would require X-ray crystallography or NMR. Docking computation has been considered a feasible and important approach to comprehend the protein–protein or protein-ligand interactions [3]. Experimental technique has been frequently used to determine the three-dimensional protein structures—and structure of the databases such as Protein Data Bank (PDB) and Worldwide Protein Data Bank. A total of 88,000 protein structures have been identified and most of them are significant in critical metabolic pathways which might be regarded as potential therapeutic target. Therefore, specific databases that contain binary complexes structures would be available, along with information about binding affinities, such as in PDBBIND, PLD, AffinDB and BindDB, molecular docking procedures improvement [3, 4]. In silico virtual screening is a popular identification technique used in in drug discovery projects which distinguishes true actives from inactive or decoy molecules effectively. To have better comprehension on the dynamic behavior of protein drug targets, compound databases can be screened against an ensemble of protein conformations through experiments or generated-computation [5]. Screening states include ligand preparation, protein targets, molecular docking, visualization, binding energy calculation, and scoring [6]. A computer simulation procedure in the form of molecular docking is commonly used to predict the conformation of certain receptor-ligand complex, which receptor is usually a protein or a nucleic acid molecule or the ligands in the form of a small molecule or other protein, or sesquiterpenoid/sesquiterpenoid alcohol interaction to protein cyclooxygenase, as shown Figure 1. In modern structure-based drug design, accurate prediction is necessary to determine the binding modes between the ligand and protein. Virtual screening is the most popular docking application that selects molecules from an existing database for further research. As the demand on this computational method keeps increasing, people expected a fast and reliable method. Another application used in this study was molecular complexes investigation [3, 6–11]. Previous studies have shown that dynamic molecular-generated conformations play considerable role in the identification of novel hit compounds because structural

tational approach [1, 2].

Molecular Docking and Molecular Dynamics

Figure 1.

42

Molecular docking-Molecular dynamic ligand to protein.

Scoring function in ligand-protein docking is expected to identify the preferred binding poses of ligands. However, considering the computational efficiency, approximations were usually introduced into the scoring functions, which unfortunately, often impair the prediction accuracy [12]. The scoring functions of ligand–protein docking can be roughly categorized into two classes: force field-based scoring functions and knowledge-based scoring functions. A force field-based scoring consists of a few potential terms, such as van der Waals interactions, electrostatic interactions, hydrophobic effects, desolvation energies, and entropic effects, and the total energy of a conformation is calculated by summing up the contributions of all energy terms [13, 14].

entropy. Hydrophobic interactions are entropy-driven and they play crucial roles in

Virtual Screening of Sesquiterpenoid Pogostemon herba as Predicted Cyclooxygenase Inhibitor

Finding an accurate modeling of protein-ligand binding is an extremely chal-

Scoring functions are based on knowledge focus on the optimization of specified terms and they use other optimization methods to set the best weighing for each scoring term regarding the training sets. Hence, these methods are often called as informatics-driven methods including IFACE [19, 20], DARS [21], SPA-PP [22], DrugScorePPI [23], TS [24], etc. In the past decades, free energy perturbation (FEP) [25] as well as energy representation (ER) [21] that are more theoretically rigorous free energy calculation methods, has emerged. Molecular Mechanics/ Poisson Boltzmann Surface Area (MM/PBSA) [26] and Molecular mechanics/generalized Born surface area (MM/GBSA) [27] are commonly employed to estimate ligand-protein binding free energies. Unfortunately, these methods are rather timeconsuming compared to other scoring functions. Yet, rapid advancement of computer hardware technology seems promising, and it is expected to allow these

The MM/PBSA and MM/GBSA approaches are more computationally efficient when they were compared to thermodynamic integration (TI) and free energy perturbation (FEP) approaches. Besides, they allow decomposition into different interaction terms to occur [13, 26, 28]. MM/PBSA and MM/GBSA are more efficient in tem of computation. Another similar approach is the linear interaction energy (LIE) method, which calculates the average energy interaction in MD simulations to estimate the absolute binding free energy. Similarly, LIE restricts the simulations only to two end points of ligand binding. Different from most molecular docking empirical scoring functions, MM/PBSA and MM/GBSA do not demand a large training set to fit different parameters for each energy term [25]. In addition, MM/ PBSA and MM/GBSA allow rigorous free energy decomposition into contributions to occur, originating from different groups of atoms or types of interaction [29, 30]. The binding free energy (ΔGbind) between a ligand and a receptor protein offered in these methods to form a complex Receptor protein - Ligand is calculated as follows.

> <sup>Δ</sup>Gbind <sup>¼</sup> Gcomplex Gprotein <sup>þ</sup> Gligand (4) ΔGbind ¼ ΔH � TΔS˜ΔEMM þ ΔGsol–TΔS (5)

> ΔEMM ¼ ΔEinternal þ ΔEelectrostatic þ ΔEvdW (6)

ΔGsol ¼ ΔGPB<sup>=</sup>GB þ ΔGSA (7)

lenging task due to its complexity. Usually, thermodynamics and statistical mechanical principles are employed to develop relatively accurate, but computationally demanding treatment of protein-ligand interactions. In this method, fullscale molecular dynamic simulation using explicit solvent and flexible protein and ligand molecules is employed [15, 17]. Both, absolute and relative binding energy can be measured using free energy approach. The absolute binding free energy method which has been considered an accurate method; it involves separate simulation treatment for solvated protein, ligand and the complex. Prior information is not quite necessary regarding the structure and binding affinity of the complex. A well-known structure for the complex is used within the context of relative free energy calculation as a reference, while the gaps in binding free energy are measured for the ligand of interest. Measurement can be carried out in the form of alchemical transformation of reference ligand into target ligand. Molecular dynamics is used in exhaustive sampling of the configuration space. The accuracy of these methods is determined by the underlying atomic force field and proper selection of

protocol to address certain problem at hand [17, 18].

methods to be used in protein-protein docking in the near future.

ligand binding [16, 18].

DOI: http://dx.doi.org/10.5772/intechopen.85319

45

Inter-atomic interactions mediate the non-covalent binding of small-molecule ligand to proteins. The interactions usually include electrostatic and van der Waals interactions (Figure 2). The affinity of receptor-ligand binding is strongly determined by other factors such as entropy, desolvation, flexibility of receptor structure and the structural water molecules in the binding site [15, 16]. A brief literature review of the importance of protein-ligand interactions and other factors contributing to binding affinity is described as follows.

Protein-ligand electrostatic complementarity and the ligand at the binding interface are both vital for the formation of complex. The predominant types of electrostatic interactions appear in the form of hydrogen bonding, salt bridges, and metal interactions.

As the most important directional interaction in biological macromolecules, hydrogen bonding is known for conferring stability to protein structure and selectivity to protein-ligand interactions [17]. Hydrogen bonding normally occurs between two electronegative atoms, which donor is covalently bound to hydrogen atom, while the acceptor contains a lone pair of electrons. The attractive interaction between partial positive charge in the hydrogen atom and partial negative charge on the acceptor atom forms strong electrostatic attraction. Several theoretical and experimental studies have successfully confirmed an additional covalent component to hydrogen bonds based empty σ\* anti- bonding orbital of the hydrogen atom and highest occupied orbital of the acceptor interaction [17, 18]. In hydrophobic interactions, non-polar parts of the molecule interact (Figure 2). The non-polar parts of protein-ligand complexes at the interacting surfaces are covered by the binding causing water molecules displacement which eventually increases the

Figure 2. Major type of non-bonded interactions in protein-ligand complexes [17].

Virtual Screening of Sesquiterpenoid Pogostemon herba as Predicted Cyclooxygenase Inhibitor DOI: http://dx.doi.org/10.5772/intechopen.85319

entropy. Hydrophobic interactions are entropy-driven and they play crucial roles in ligand binding [16, 18].

Finding an accurate modeling of protein-ligand binding is an extremely challenging task due to its complexity. Usually, thermodynamics and statistical mechanical principles are employed to develop relatively accurate, but computationally demanding treatment of protein-ligand interactions. In this method, fullscale molecular dynamic simulation using explicit solvent and flexible protein and ligand molecules is employed [15, 17]. Both, absolute and relative binding energy can be measured using free energy approach. The absolute binding free energy method which has been considered an accurate method; it involves separate simulation treatment for solvated protein, ligand and the complex. Prior information is not quite necessary regarding the structure and binding affinity of the complex. A well-known structure for the complex is used within the context of relative free energy calculation as a reference, while the gaps in binding free energy are measured for the ligand of interest. Measurement can be carried out in the form of alchemical transformation of reference ligand into target ligand. Molecular dynamics is used in exhaustive sampling of the configuration space. The accuracy of these methods is determined by the underlying atomic force field and proper selection of protocol to address certain problem at hand [17, 18].

Scoring functions are based on knowledge focus on the optimization of specified terms and they use other optimization methods to set the best weighing for each scoring term regarding the training sets. Hence, these methods are often called as informatics-driven methods including IFACE [19, 20], DARS [21], SPA-PP [22], DrugScorePPI [23], TS [24], etc. In the past decades, free energy perturbation (FEP) [25] as well as energy representation (ER) [21] that are more theoretically rigorous free energy calculation methods, has emerged. Molecular Mechanics/ Poisson Boltzmann Surface Area (MM/PBSA) [26] and Molecular mechanics/generalized Born surface area (MM/GBSA) [27] are commonly employed to estimate ligand-protein binding free energies. Unfortunately, these methods are rather timeconsuming compared to other scoring functions. Yet, rapid advancement of computer hardware technology seems promising, and it is expected to allow these methods to be used in protein-protein docking in the near future.

The MM/PBSA and MM/GBSA approaches are more computationally efficient when they were compared to thermodynamic integration (TI) and free energy perturbation (FEP) approaches. Besides, they allow decomposition into different interaction terms to occur [13, 26, 28]. MM/PBSA and MM/GBSA are more efficient in tem of computation. Another similar approach is the linear interaction energy (LIE) method, which calculates the average energy interaction in MD simulations to estimate the absolute binding free energy. Similarly, LIE restricts the simulations only to two end points of ligand binding. Different from most molecular docking empirical scoring functions, MM/PBSA and MM/GBSA do not demand a large training set to fit different parameters for each energy term [25]. In addition, MM/ PBSA and MM/GBSA allow rigorous free energy decomposition into contributions to occur, originating from different groups of atoms or types of interaction [29, 30]. The binding free energy (ΔGbind) between a ligand and a receptor protein offered in these methods to form a complex Receptor protein - Ligand is calculated as follows.

$$\Delta \mathbf{G}\_{\text{bind}} = \mathbf{G}\_{\text{complex}} \left( \mathbf{G}\_{\text{protein}} + \mathbf{G}\_{\text{ligand}} \right) \tag{4}$$

$$
\Delta \mathbf{G}\_{\text{bind}} = \Delta \mathbf{H} - \mathbf{T} \Delta \mathbf{S}^{\tau} \Delta \mathbf{E}\_{\text{MM}} + \Delta \mathbf{G}\_{\text{sol}} - T \Delta \mathbf{S} \tag{5}
$$

$$
\Delta \mathbf{E\_{MM}} = \Delta \mathbf{E\_{internal}} + \Delta \mathbf{E\_{electrostatic}} + \Delta \mathbf{E\_{vdW}} \tag{6}
$$

$$
\Delta \mathbf{G}\_{\text{sol}} = \Delta \mathbf{G}\_{\text{PB/GB}} + \Delta \mathbf{G}\_{\text{SA}} \tag{7}
$$

Scoring function in ligand-protein docking is expected to identify the preferred binding poses of ligands. However, considering the computational efficiency, approximations were usually introduced into the scoring functions, which unfortunately, often impair the prediction accuracy [12]. The scoring functions of ligand–protein docking can

knowledge-based scoring functions. A force field-based scoring consists of a few potential terms, such as van der Waals interactions, electrostatic interactions, hydrophobic effects, desolvation energies, and entropic effects, and the total energy of a conformation

Inter-atomic interactions mediate the non-covalent binding of small-molecule ligand to proteins. The interactions usually include electrostatic and van der Waals interactions (Figure 2). The affinity of receptor-ligand binding is strongly determined by other factors such as entropy, desolvation, flexibility of receptor structure and the structural water molecules in the binding site [15, 16]. A brief literature review of the importance of protein-ligand interactions and other factors contrib-

Protein-ligand electrostatic complementarity and the ligand at the binding interface are both vital for the formation of complex. The predominant types of electrostatic interactions appear in the form of hydrogen bonding, salt bridges, and

As the most important directional interaction in biological macromolecules, hydrogen bonding is known for conferring stability to protein structure and selectivity to protein-ligand interactions [17]. Hydrogen bonding normally occurs between two electronegative atoms, which donor is covalently bound to hydrogen atom, while the acceptor contains a lone pair of electrons. The attractive interaction between partial positive charge in the hydrogen atom and partial negative charge on the acceptor atom forms strong electrostatic attraction. Several theoretical and experimental studies have successfully confirmed an additional covalent component to hydrogen bonds based empty σ\* anti- bonding orbital of the hydrogen atom and highest occupied orbital of the acceptor interaction [17, 18]. In hydrophobic interactions, non-polar parts of the molecule interact (Figure 2). The non-polar parts of protein-ligand complexes at the interacting surfaces are covered by the binding causing water molecules displacement which eventually increases the

be roughly categorized into two classes: force field-based scoring functions and

is calculated by summing up the contributions of all energy terms [13, 14].

uting to binding affinity is described as follows.

Molecular Docking and Molecular Dynamics

metal interactions.

Figure 2.

44

Major type of non-bonded interactions in protein-ligand complexes [17].

Figure 3.

Calculation of binding energy using the MM-GB/SA approach [36].

where ΔGbind shows the free energy of ligand-protein total binding; ΔEMM reflects the total gas phase energy (sum of ΔEinternal, ΔEelectrostatic, and ΔEvdw); ΔGsol is the sum of polar (ΔGPB/GB) and non-polar (ΔGSA) contributions to solvation; and -TΔS refers to the conformational binding entropy (commonly calculated by normal-mode analysis). ΔEinternal shows the internal energy that arises from different bond, angle, and dihedral in molecular mechanics (MM) force field (in the MM/PBSA and MM/GBSA, this always amounts to zero as shown in single trajectory of a complex calculation). ΔEelectrostatic and ΔEvdw are the electrostatic and van der Waals energies resulted from the calculation of MM, while ΔGPB/GB shows the polar contribution to the solvation free energy (calculated using Poisson–Boltzmann (PB) or generalized Born (GB) method). ΔGSA is the nonpolar solvation free energy that is usually computed using a linear function of the solvent-accessible surface area (SASA). EMM is molecular mechanical energy calculated from CHARMM force field, Gelec and GNP are electrostatic polar components and non-solvation free energy. TS term refers to the entropy of the solute which is assumed to be constant between one set of poses for the same ligand on the active side. EMM is a gas phase forcefield energy and consists of internal energy (Eint), electrostatic energy (Eelec) and van der Waals energy components. Eint is further divided into Ebond, Eangle, Etorsion and Eoop to calculate account energy related to bonds, angles, torque and outside as shown in Figure 3.

Sesquiterpenoid, such as alpha-bulnesene (CID94275), alpha-guaiene (CID107152), and seychellene (CID519743) has molecular weight 204.35 g/mol, molecular formula C15H24, and XLogP3-AA: 4.60; 4.6; and 5.10 respectively (Table 1). And alpha-patchouli alcohol isomers has molecular weight:

Virtual Screening of Sesquiterpenoid Pogostemon herba as Predicted Cyclooxygenase Inhibitor

C15H26O; XLogP3-AA: 4.1; H-Bond Donor: 1; and H-Bond Acceptor: 1.

Kinase inhibitor 1.33 1.33 1.30 0.88 Nuclear receptor inhibitor 0.19 0.19 0.27 0.55 Protease inhibitor 0.60 0.60 0.50 0.32 Enzyme inhibitor 0.07 0.07 0.28 0.40 xlogP3-AA 4.6 4.60 5.10 4.10 H-Bond donor 0 0 0 1 H-Bond aseptor 0 0 0 1

Physical–chemical properties and predicted activity of sesquiterpenoids and sequiterpenoid alcohols

Description sequiterpenoid sesquiterpenoid alcohol

CID94275 CID107152 CID519743 CID442384, CID521903,

) 204.35 204.35 204.35 222.36

CID6432585, CID3080622, CID1095517,CID56928117

The number of isomers of alpha-Patchouli alcohol is six. In Figure 4

Structure of sequiterpenoid and sesquiterpenoid alcohols Pogostemon herba.

222.36634 g/mol; molecular formula:

DOI: http://dx.doi.org/10.5772/intechopen.85319

Molecular Weight (g mol<sup>1</sup>

Figure 4.

Table 1.

47

Pogostemon herba.

MM/GBSA and MM/PBSA have been successfully applied to predict the binding free energies for various ligand sesquiterpenoid/sesquiterpenoid alcohol to protein COX-1/COX-2, but the previous studies mostly focused on certain specific systems and the prediction results cannot afford the overall accuracy of MM/PBSA and MM/ GBSA for ligand-protein systems.

## 3. Ligand sesquiterpenoid

Ligand sequiterpenoid was obtained from pubchem.ncbi.nlm.nih.gov, such as (alpha-bulnesene (CID94275), alpha-guaiene (CID107152), and seychellene (CID519743). Also, sesquiterpenoid alcohols, including alpha-Patchouli alcohol isomers (CID442384, CID521903, CID6432585, CID3080622, CID10955174, and CID56928117) as 3D-SDF format. Then, its energy was minimized which files were converted to 3D-PDB format by Open Babel 2.3.1 in Hex 8.0 as the ligands prepared for virtual screening [6, 8, 11, 31]. The structures of studied ligands are shown in Figure 4.

Virtual Screening of Sesquiterpenoid Pogostemon herba as Predicted Cyclooxygenase Inhibitor DOI: http://dx.doi.org/10.5772/intechopen.85319

#### Figure 4.

where ΔGbind shows the free energy of ligand-protein total binding; ΔEMM reflects the total gas phase energy (sum of ΔEinternal, ΔEelectrostatic, and ΔEvdw); ΔGsol is the sum of polar (ΔGPB/GB) and non-polar (ΔGSA) contributions to solvation; and -TΔS refers to the conformational binding entropy (commonly calculated by normal-mode analysis). ΔEinternal shows the internal energy that arises from different bond, angle, and dihedral in molecular mechanics (MM) force field (in the MM/PBSA and MM/GBSA, this always amounts to zero as shown in single trajectory of a complex calculation). ΔEelectrostatic and ΔEvdw are the electrostatic and van der Waals energies resulted from the calculation of MM, while ΔGPB/GB shows the polar contribution to the solvation free energy (calculated using Poisson–Boltzmann (PB) or generalized Born (GB) method). ΔGSA is the nonpolar solvation free energy that is usually computed using a linear function of the solvent-accessible surface area (SASA). EMM is molecular mechanical energy calculated from CHARMM force field, Gelec and GNP are electrostatic polar components and non-solvation free energy. TS term refers to the entropy of the solute which is assumed to be constant between one set of poses for the same ligand on the active side. EMM is a gas phase forcefield energy and consists of internal energy (Eint), electrostatic energy (Eelec) and van der Waals energy components. Eint is further divided into Ebond, Eangle, Etorsion and Eoop to calculate account energy related to bonds, angles, torque and

Calculation of binding energy using the MM-GB/SA approach [36].

Molecular Docking and Molecular Dynamics

MM/GBSA and MM/PBSA have been successfully applied to predict the binding free energies for various ligand sesquiterpenoid/sesquiterpenoid alcohol to protein COX-1/COX-2, but the previous studies mostly focused on certain specific systems and the prediction results cannot afford the overall accuracy of MM/PBSA and MM/

Ligand sequiterpenoid was obtained from pubchem.ncbi.nlm.nih.gov, such as

(CID519743). Also, sesquiterpenoid alcohols, including alpha-Patchouli alcohol isomers (CID442384, CID521903, CID6432585, CID3080622, CID10955174, and CID56928117) as 3D-SDF format. Then, its energy was minimized which files were converted to 3D-PDB format by Open Babel 2.3.1 in Hex 8.0 as the ligands prepared for virtual screening [6, 8, 11, 31]. The structures of studied ligands are shown in

(alpha-bulnesene (CID94275), alpha-guaiene (CID107152), and seychellene

outside as shown in Figure 3.

GBSA for ligand-protein systems.

3. Ligand sesquiterpenoid

Figure 4.

46

Figure 3.

Structure of sequiterpenoid and sesquiterpenoid alcohols Pogostemon herba.

Sesquiterpenoid, such as alpha-bulnesene (CID94275), alpha-guaiene (CID107152), and seychellene (CID519743) has molecular weight 204.35 g/mol, molecular formula C15H24, and XLogP3-AA: 4.60; 4.6; and 5.10 respectively (Table 1). And alpha-patchouli alcohol isomers has molecular weight: 222.36634 g/mol; molecular formula:

C15H26O; XLogP3-AA: 4.1; H-Bond Donor: 1; and H-Bond Acceptor: 1. The number of isomers of alpha-Patchouli alcohol is six. In Figure 4


#### Table 1.

Physical–chemical properties and predicted activity of sesquiterpenoids and sequiterpenoid alcohols Pogostemon herba.

2D-sesquiterpernoid/sesquiterpenoid alcohol, such as alpha-bulnesene (CID94275), alpha-guaiene, and seychellene (CID519743), and alpha-Patchouli isomers (CID442384, CID521903, CID6432585, CID3080622, CID10955174, and CID56928117) show the different position of hydroxyl group and hydrogen atom. The 3D structure of sesquiterpenoid/sesquiterpenoid alcohol was retrieved in 3D-SDF format from http://pubchem.ncbi.nlm.nih.gov/. For the preparation of docking, 3D-SDF format of isomers was converted to 3D-PDB using open babel software. This program helps to search, convert, analyze, or store data which has a wide range of applications in the different fields of molecular modeling, computational chemistry, and so forth. For a common user, it helps to apply chemistry aspects without worrying about the low level details of chemical information. It also converts crystallographic file formats (CIF, ShelX), reaction formats (MDLRXN), molecular dynamics and docking (AutoDock, Amber), 3D viewers (Chem3D, Molden), and chemical kinetics and thermodynamics (ChemKin, Termo) [6, 8].

of the model taken for the structural analysis and also validated the target-ligand binding efficacy of the structure. The Ramachandran plot presents the angle of phipsi torsion of all residues in the structure (except those at the chain termini) which were classified according to their regions in the quadrangle. The most favored regions are colored yellow, additional allowed/generously allowed region, and outlier regions are indicated as blue and pink fields, respectively [6, 8, 32].

Virtual Screening of Sesquiterpenoid Pogostemon herba as Predicted Cyclooxygenase Inhibitor

5. Molecular docking ligand and binding energy interaction

COX-2

PGH2.

49

Ramachandran plot analysis.

DOI: http://dx.doi.org/10.5772/intechopen.85319

patchouli alcohol isomers-COX-1/COX-2 complexes.

this complex will certainly affect its binding free energy.

sesquiterpenoid/sesquiterpenoid alcohols to protein COX-1 and

We used Hex8.0 software (http://hex.loria.fr) for rigid docking to compute possible interaction COX-1 and COX-2 with (alpha-bulnesene (CID94275), alphaguaiene (CID107152), seychellene (CID519743) and sesquiterpenoid alcohols such

CID3080622, CID10955174, and CID56928117) on the interaction site. Output of the docking was refined using Discovery Studio Client 3.5 software. We used Discovery Studio Client 3.5 to perform interactions, ligand binds to COX-1/COX-2 and

The repeat rigid docking used Hex 8.0 software to compute possible interaction COX-1 and COX-2 with sesquiterpenoid/sesquiterpenoid alcohols such as alphabulnesene (CID94275), alpha-guaiene, seychellene (CID519743), and alphapatchouli alcohol isomers (CID442384, CID521903, CID6432585, CID3080622, CID10955174, and CID56928117) on its interaction site and the data are represented by Discovery Studio 3.5 software in (Figure 5(a1–l1)). The interaction site position of COX-1/COX-2-sesquiterpenoid/sesquiterpenoid alcohol complexes were analyzed using Discovery Studio-3.5 Client software to get the receptor-ligand interaction and Ramachandran plot, as shown in Figure 5; some of them are alpha-

In Table 2 and Figure 6, the interactions active site of ligand sesquiterpenoid/ sesquiterpenoid alcohol with COX-1 and COX-2 protein receptor showed the differences in the position active site. The different positions were analyzed and presented in the Ramachandran plot analysis and its amino acid residues in the receptor active site of COX-1 and COX-2 in which hydrogen atoms and hydroxyl groups on each of the 3D-isomers of alpha-patchouli alcohol structure were in different position (Figure 4). The results of docking and analysis of the active site also show that all ligands sesquiterpenoid/sesquiterpenoid alcohol are in the catalytic domain. Thus, all the compounds have the capability of blocking oxygenated reaction and reaction peroxides; currently substrate arachidonic acid becomes

Each ligand, CID521903, was seen interacting with HEM682B group in COX-2- CID521903 complexes. This result proved that it would lead to inhibition of enzymatic reactions occurring COX-1 and COX-2. The analysis of active site showed that there are any difference and similarities of the active site of all ligand alphapatchouli alcohol isomers which is interact with receptor proteins COX-1 and COX-2. This difference is caused by different stereoisomers of hydrogen atoms and hydroxyl group in alpha-patchouli alcohol isomers. The different position active site the complexes have led to interaction types, such as hydrogen bond, van der Waals, electrostatic and covalent bond. The different types of interactions in

as alpha-Patchouli alcohol isomers (CID442384, CID521903, CID6432585,
