Application of Computational Chemistry Towards ...

Presented at the DLSU Research Congress 2018 De La Salle University, Manila, Philippines June 20 to 22, 2018

Application of Computational Chemistry Towards Understanding the Activation of Isoniazid in Mycobacterium tuberculosis Yves Ira A. Reyes1, and Francisco C. Franco, Jr.1,* 1

Chemistry Department, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines *Corresponding Author: [email protected]

Abstract: The application of computational chemistry for various applications have been increasing, most especially in drug-related studies. We applied computational chemistry techniques to study how Isoniazid is activated by the catalase peroxidase (katG) enzyme of Mycobacterium tuberculosis. Isoniazid geometry structure was optimized at B3LYP level of theory and frozen conformation was docked to the katG protein structure. The binding energy of the top pose was -6.78 kcal/mol and it was stabilized by interaction with 7 amino acid residues and the heme group. Arg104, Tye229, and Val230 were found to be critical residues as they exhibit hydrogen bonding interactions with Isoniazid. Key Words: computational chemistry; Mycobacterium tuberculosis; Isoniazid; catalase peroxidase; drug design

1. INTRODUCTION Given a system placed in known conditions, our understanding of fundamental theories that describe the system enable predictions on how the system will behave. Theoretical calculations has long since been a scientific tool to augment the resources demanded by physical experiments, especially in a highly experimental field like Chemistry. However, on its own, very complex and impractical calculations would be necessary to generate significant scientific findings. With the rapid development of modern computer technology, these complex calculations became possible in lower time frames. Computational Chemistry (Comp Chem), thus, is born as a field devoted to use of computational methods to study chemical systems (F. Jensen, 2007). It has been shown that by combining different computational methods, more insights can

be developed (Rasool et al., 2018). In this study, we applied a combination of DFT and MM in Molecular Docking to study the mechanism of lethal action of Isoniazid to Mycobacterium tuberculosis (M. tb), the causative agent of the disease, Tuberculosis (TB) (Smith, 2003). TB is a global epidemic (World Health Organization, 2017), and Isoniazid or isonicotinyl phenyl hydrazine (INH) is one of the first line drugs used to treat it. INH is a prodrug, delivered in an inactive form and only activated by M. tb’s own enzyme, Catalase Peroxidase (katG) (Kumar Pandey, Bajpai, Baboo, & Dwivedi, 2014; Musser et al., 1996; Unissa, Subbian, Hanna, & Selvakumar, 2016). This explains why INH is selectively lethal to M. tb cells. The katG dependent activation of INH has been a subject of interest among many studies, both computational and experimental, due to its importance in understanding drug resistance and drug development for TB (Argyrou, Vetting, & Blanchard, 2007; Musser et al., 1996; Srivastava,


Tripathi, Kumar, & Sharma, 2017). A previous study has been able to suggest a plausible mechanism katG dependent activation of INH (Pierattelli et al., 2004). However, the mechanism elucidation has been mostly limited to MM methods and yet to be corroborated by experimental or even high-level QM simulations.

2. METHODOLOGY The isoniazid structure was drawn and optimized using MMFF94 (Halgren, 1996) using Avogadro (Hanwell et al., 2012). Geometry optimization in vacuo and energy calculations were done with B3LYP (Becke, 1993) level of theory, using GAMESS computational package (Schmidt et al., 1993) with 6-31G(d) as the basis set. Molecular docking simulations were done using AutoDock 4.0 (Morris et al., 2009). The DFT optimized structure of INH was docked into the INH binding site of the katG. Single bond rotations were not allowed and grid box was specified with center (39.5,5.6,43.6) and dimensions of 90, 90 and 110 in x,y and z, respectively. Lamarckian Genetic Algorithm (LGA) was chosen as the receptor-ligand interaction. Ten GA runs with a population size of 150 and 2500000 energy evaluation and 27000 maximum generations was done. 3D Crystal structure of the katG protein has been previously elucidated and reported (Bertrand et al., 2004). The data for the coordinate structure in pdf format, was retrieved from rcsb.org (PDB: 1SJ2) (Berman et al., 2000).

3. RESULTS AND DISCUSSION

Fig. 1. Chemical structure of INH optimized at the B3LYP/6-31G(d,p) level. The DFT optimized structure is shown in Fig. 1. The DFT optimized structure was analyzed by measuring the bond angles and bond lengths involving non-hydrogen atoms. The values were also compared to the values found in the crystal structure of INH reported in previous studies (L. H. Jensen, 1953, 1954) shown in tables 1 and 2. As shown, the DFT optimized structure has very similar geometric parameters to the experimentally resolved crystal structure. The slight differences are reasonable because the DFT optimization was done in vacuo, void of interaction with other particles while the experimental structure was taken from a molecule stabilized in a crystal lattice. Nevertheless, the geometric similarities with the crystal structure show that the DFT optimization successfully generated a valid stable conformation of INH. Thus, we proceeded with the assumption that the generated optimized structure is the most probable structure of INH, in vacuo. Table 1. Bond lengths (Å) of DFT-B3LYP optimized structure and crystal structure of INH. Atom C1-C2 C2-N3 C4-N3 C4-C5 C5-C6 C6-C1 C6-C11 C11-O13 C11-N12 N12-N14

B3LYP 1.39 1.34 1.34 1.40 1.40 1.40 1.51 1.22 1.38 1.40

Crystal 1.39 1.33 1.34 1.38 1.39 1.40 1.48 1.23 1.33 1.41

Table 2. Bond angles (degrees) of DFT-B3LYP optimized structure and crystal structure of INH. Atom

B3LYP

Crystal

C6-C1-C2 C1-C2-N3 C2-N3-C4 N3-C4-C5 C4-C5-C6

118.8 123.8 117.0 123.8 118.7

119 124 116 124 120


C5-C6-C1 C5-C6-C11 C1-C6-C11 C6-C11-O13 C6-C11-N12 O13-C11-N12 C11-N12-N14

117.9 124.2 117.8 121.6 113.7 124.6 119.6

116 125 118 122 116 122 121

The molecular docking simulation predicts the binding pose of the INH (ligand) in the hydrophobic center of the M. tb. katG protein (receptor). Because the binding site can be assumed to be void of solvent particles, the environment experienced by the ligand mostly resembles a vacuum. Thus, the INH will most likely conform to the optimized structure generated by DFT calculations used as input for the molecular docking.

The docking of INH to katG generated 200 binding poses with binding energies ranging from -4.98 to -6.78 kcal/mol. The more negative the binding energy, the better the interaction between ligand and receptor. Thus, the pose with the most negative binding energy (-6.78 kcal/mol), shown in Fig. 2, most probably represents the real pose of the ligand in vivo. Further analysis of the top docking pose using LigPlot (Wallace, Laskowski, & Thornton, 1995). A 2d interaction diagram between INH and the interacting residues was generated and shown in Fig. 3a. The analysis revealed that along with the heme group, 7 amino acid residues directly interact with INH: Arg104, Trp107, His108, Asp137, Ile228, Tyr 229 and Val 230. The residues Arg104, Tyr229, and Val230 were found to form hydrogen bonds with INH, as depicted by green dashed lines, while the rest forms hydrophobic interactions as depicted by the red rays.

Fig. 2. Top docking pose of INH to M. tb. katG By default, AutoDock generates random conformations, by rotating single bonds within the ligand, to search for the conformation that will generate the best interactions with the receptor. However, with the assumption that the input ligand structure is the stable and most probable structure, the rotation of the C6-C11 bond was frozen, allowing only 1 conformation to be docked. Furthermore, the search for the docking site was limited according to the consensus docking site reported by previous studies (Jena, Waghmare, Kashikar, Kumar, & Harinath, 2014; Pierattelli et al., 2004).

Fig. 3. Binding site residues and top INH pose in A) 2D interaction diagram b) 3D representation Though there are many possible binding sites and orientation of INH in each of these sites, the analysis of the top pose reveals interaction with residues that agree with most of the findings of reported docking studies (Jena et al., 2014; Srivastava et al., 2017). However, the hydrogen bond of Val230 with the pyridine ring has not yet been observed in these studies. The hydrogen bond of INH with Val230, revealed in this study, along with previously reported Arg104 and Tyr229 implies the importance of these residues in binding INH. Thus, it


explains why mutations in these residues have been associated with resistance to INH treatment (Sandgren et al., 2009). Furthermore, mutation of S315 residue has been present in many INH resistant strains of M. tb (Srivastava, Tripathi, Kumar, & Sharma, 2017; Unissa, Subbian, Hanna, & Selvakumar, 2016). Yet, the best pose for INH was found to have no interaction with that residue. Mutation in S315 can cause resistance due to increase in interaction leading to irreversible binding of INH to katG or decrease in interaction leading to decreased or loss of binding of INH to katG. Our findings support the former hypothesis since we found no interaction between INH and S315 to begin with.

4. CONCLUSIONS The geometry optimization of Isoniazid using DFT-B3LYP level of theory successfully generated a structure with geometrical parameters similar to that of the elucidated crystal structure. Docking of the optimized INH structure to katG revealed 7 residues along with the heme group, as important residues for the stable complex formation and thus, activation of INH in M. tb. cells. Three amino acid residues are highlighted for being predicted to form hydrogen bonds with INH in the binding site: Arg104, Tyr229, and Val230. S315 was found to not be involved in binding interaction We admit that although the methods presented in this study give a good picture of the overall receptor-ligand complex, it is limited in providing individual importance of the involved amino acid residues. For one, the importance of hydrophobic interactions, which are mainly due to uneven distribution of atoms in neutral molecules, cannot be studied using MM. Furthermore, molecular docking does not give a picture of the complex formation, reaction, and dissociation. Thus, it is important to extend this study using a higher level of computations that are able to account for electron correlation (i.e. ab initio or DFT) or changes in molecular structure with time (i.e. Molecular Dynamics or QM/MM).

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