Bangladesh Journal of Pharmacology Research Article Arabinosyl transferase inhibitor de- sign against Mycobacterium tuber- culosis using ligand based drug de- sign approach BJP Introduction Tuberculosis is a common as well as one of the deadly infectious diseases caused by Mycobacterium tuberculosis. It affects most of the world’s population, mainly in developing countries (Harries and Dye, 2006; Lopez and Mathers, 2006) Antibiotics were prescribed but effective treatment is challenging due to the complica- ted structure and chemical composition of the mycobac- terium cell wall. The unusual structure of the bacterial cell wall makes many antibiotics ineffective and check the entry of drugs (Jain and Mondal, 2008). Isoniazid and ethambutol have been used for the decades as frontline drugs to inhibit M. tuberculosis, but the rise of multi-drug resistant and extensively drug resistant strains poses a serious threat to present treatment of tuberculosis (Burris, 2004; McIlleron et al., 2009; Zhang et al., 2014). Ethambutol inhibits the synthesis of essential components of the mycobacterial cell wall. Ethambutol targets the biosynthesis of the cell wall, inhibiting the synthesis of both arabinogalactan and lipoarabinomannan. It is assumed to act via inhibi- tion of arabinosyl transferases (Amin et al., 2008). An arabinosyltransferase is a transferase enzyme acting upon arabinose belongs to the family of glycosyl transferases. Ethambutol has been also reported for several toxic effects such as optic neuritis, color blindness etc (Kumar et al., 1993). Therefore, the need of new and alternative drug candidate for tuberculosis is obvious and current approaches is aim to screened lead A Journal of the Bangladesh Pharmacological Society (BDPS) Bangladesh J Pharmacol 2014; 9: 225-229 Journal homepage: www.banglajol.info Abstracted/indexed in Academic Search Complete, Agroforestry Abstracts, Asia Journals Online, Bangladesh Journals Online, Biological Abstracts, BIOSIS Previews, CAB Abstracts, Current Abstracts, Directory of Open Access Journals, EMBASE/Excerpta Medica, Google Scholar, HINARI (WHO), International Pharmaceutical Abstracts, Open J-gate, Science Citation Index Expanded, SCOPUS and Social Sciences Citation Index ISSN: 1991-0088 Abstract Antibiotic resistance is a major challenge to combat tuberculosis. Several reports of antibiotic resistance strains of Mycobacterium tuberculosis is strongly demanding the need of new and alternative antibiotics for its inhibition. Therefore, current investigation is an attempt to screen few lead molecules for the inhibition of arbinosyl transferase enzyme of M. tuberculosis. The inhibi- tion of this enzyme is an established target of many antibiotics especially ethambutol. Herein, we have considered the structure of ethambutol as a starting point to screen active compound then ethambutol. Similar compounds were searched in chemical database and six compounds were identified and considered as selective arbinosyl transferase inhibitor based on physiochemical properties, bioactivity and ADME with least docking score. The compounds viz. ZINC00388344, ZINC003884, Chemspider2057082, ZINC -00388344, ZINC0038846, Chemspider2057082, Etha17 (analog) and Etha10 (analog) were finally screened and recommended for in vitro investigation. Article Info Received: 7 March 2014 Accepted: 26 March 2014 Available Online: 14 May 2014 DOI: 10.3329/bjp.v9i2.18270 Cite this article: Kolita B, Gogoi D, Dutta PP, Bordoloi M, Bezbaruah RL. Arabinosyl trans- ferase inhibitor design against Myco- bacterium tuberculosis using ligand based drug design approach. Bangla- desh J Pharmacol. 2014; 9: 225-29. This work is licensed under a Creative Commons Attribution 3.0 License. You are free to copy, distribute and perform the work. You must attribute the work in the manner specified by the author or licensor. Arabinosyl transferase inhibitor design against Mycobacterium tuberculosis using ligand based drug design approach Bhaskor Kolita1, Dhrubajyoti Gogoi2, Partha Pratim Dutta3, Manobjyoti Bordoloi3 and Rajib Lochan Bezbaruah2 1Centre for Bioinformatics Studies, Dibrugarh University, Dibrugarh, Assam, India; 2DBT-Bioinformatics Infrastructure Facility, Biotechnology Division, North East Institute of Science and Technology (Formerly Regional Research Laboratory), CSIR, Government of India, Jorhat, Assam, India; 3Natural Product Chemistry Division, North East Institute of Science and Technology (Formerly Regional Research Laboratory), CSIR, Government of India, Jorhat, Assam, India. molecule from chemical database using Computer aided screening methodology based of known chemical structure of ethambutol. Materials and Methods Receptor and ligand retrieval and analogs design for ethambutol The 3-D structure of arabinosyl transferase (3PTY) was retrieved from Protein Data Bank. The structure of ethambutol was also retrieved from Drug Bank. Struc- tural similarity, sub-structure, identity (70%) search were performed and carried out for ethambutol like compounds using Molsoft ICM Browser 3.5-1p and ChemBioDraw Ultra 12.0 software (Gogoi et al., 2012; Lagunin et al., 2000). Compounds library were collected from ZINC Database, PubChem, Chemspider, Chem- Bank cheminformatics site in sdf format. Ethambutol structure based analogs were also designed manually using Chem Sketch software. Around 100,000 com- pounds were considered and screened for ethambutol like candidate lead compound. Open BabelGUI tool was used for chemical file conversion purposes. Ligand structure optimization and physiochemical properties calculation Screened lignads were optimized before docking using MM2 force field of ChemBio 3D ultra. Physiochemical properties (Hydrogen bond acceptor, hydrogen bond donor, number of rotatable bond, calculated log P, molecular weight, etc) were predicted and checked for non-violation of drug like and Lipinski’s rules using PreADMET server. Potential protein binding sites prediction and mole- cular docking study The potential ligand binding site of arabinosyl transferase receptor was computed at MVD workspace. Volume and Surface of the binding site were computed and optimum binding site was selected to perform docking. The screened compounds were imported in the Molegro Virtual Docker workspace. The bonds flexibility of the ligands was set and the side chain flexibility of the amino acids in the binding cavity was set with a tolerance of 1.1 and strength of 0.9 for docking simulations. RMSD threshold for multiple cluster poses was set at <2.00 Å. The docking algorithm was set at a maximum iteration of 1500 with a simplex evolution size of 50 and a minimum of 20 runs. Mole- cular docking was carried out using Molegro Virtual Docker (MVD) (Molegro APS: MVD 5.0) (Thomsen and Christensen, 2006). MVD is molecular visualization and molecular docking software which is based on a differential evolution algorithm; the solution of the algorithm takes into account the sum of the inter- molecular interaction energy between the ligand and the protein and the intramolecular interaction energy of the ligand. The docking energy scoring function is based on the modified piecewise linear potential (PLP) with new hydrogen bonding and electrostatic terms included. Interaction of Ligands with recetor was studied to know the best binding orientation of receptor -ligand complex in terms of minimum energy score. ADME and toxicity prediction Absorption, distribution, metabolism, excretion and toxicity were studied for top ranking compounds were computed using PASS (Prediction of Activity Spectra for Substances) Inet and Pre ADMET server (Gogoi et al., 2012; Lagunin et al., 2000). PASS Inet predicts 3678 pharmacological effects, mechanisms of action, muta- genicity, carcinogenicity, teratogenicity and embryo- toxicity. MDCK cell permeability, human intestinal absorption, blood-brain barrier penetration and plasma protein binding scores were studied and compared (Norinder and Bergstrom, 2006). Result and Discussion Herein, we have screened out 3148 compounds struc- turally similar with ethambutol from 11,74,583 com- pounds based on chemical similarity (structural) using ZINC database. We have also retrieved ethambutol like 5 compounds from Chemspider on the basic of calculated property, 10 from ChemBank on the basic of substructure and 3 from Pubchem on the basic of property. We calculated physicochemical property for 222 compounds in Molsoft ICM-Browser software and observe that most of the compounds follows Lipinski’s rule of Five as presented in the Table I including few analogues of ethambutol. The compounds with the predicted drug likeness of more than 80% with Lipinski’s qualification were used to study their ADME properties. 222 compounds were checked for absorption and distribution in human body using PreADMET as given in Table II. Each compound was checked for carcinogenic, embryo toxin and teratogenic and 31 non-toxic compounds were chosen for molecular docking analysis. Receptor model was exported and potential bindings sites were predicted in the Molegro Virtual Docker workspace as presented in the Table III with their coordinate position in the workspace. Missing coordi- nates of receptor was checked before loading. Amino acid residues around the binding cavity were given in the Table IV. Molecular docking is a novel approach to study small compound inhibition to receptor protein. We docked 31 non toxic compounds with receptor model of M. tuberculosis arabinosyl transferase using Molegro Vir- tual Docker (MVD) software. MVD is molecular 226 Bangladesh J Pharmacol 2014; 9: 225-229 visualization and molecular docking software which is based on a differential evolution algorithm; the solution of the algorithm takes into account the sum of the intermolecular interaction energy between the ligand and the protein and the intramolecular interaction ener- gy of the ligand. The docking energy scoring function is based on the modified piecewise linear potential (PLP) with new hydrogen bonding and electrostatic terms included. The ligands were optimized before docking for proper structural stabilization. We calculated stretch, bend, steth bend, torsion, non-1,4 VDW, 1,4 VDW, total energy (Kcal/mol) using MM2 module of Chembio office tool. Docking computation was done based on the parameters mentioned in the methodology. Bangladesh J Pharmacol 2014; 9: 225-229 227 Table I Physicochemical property of top ranking database compounds Compound ID Formula HBA HBD Rot B MW ClogP ZINC00388344 C7H16NO 1 1 1 130.1 0.8 ZINC20441875 C11H23N2O 1 3 4 199.2 0.6 ZINC01690002 C8H16NO 1 1 4 142.1 0.6 ZINC17316804 C8H16NO 1 1 4 142.1 0.6 ZINC19889071 C18H37N3O 2 1 4 311.3 1.0 ZINC19889073 C15H33N3O 2 1 5 271.3 0.0 ZINC20441963 C16H35N3O 0 1 7 269.3 3.0 ZINCO1688588 C8H18NO2 2 3 3 199.2 0.4 ZINC19976556 C10H21N3O 2 2 5 199.2 0.4 ZINC37049708 C12H29N3O 2 3 6 231.2 -0.1 ZINC37049709 C12H29N3O 2 3 6 231.2 -0.1 Pubchem1793372 C9H2ON203 3 3 10 204.1 1.0 Pubchem18542010 C9H16O5 5 2 9 204.1 0.21 Pubchem21811791 C10H20O4 4 2 10 204.1 -0.0 Chemspider8464931 C12H16N2O3S 4 3 7 268.1 0.6 Chemspider8464933 C12H16N2O3S 4 3 7 268.1 0.6 Chemspider16740754 C11H19N3 1 4 6 193.2 0.1 ChemBank1036 C20H28N2O5 6 2 11 376.2 0.7 ChemBank1176 C10H24N2O2 4 4 9 204.2 0.1 ChemBank1608 C21H31N3O5 7 5 13 405.2 -1.8 ChemBank1000260 C10H24N2O2 4 4 9 204.2 0.1 ChemBank1049255 C14H28N2O2 4 4 5 256.2 1.2 Table II ADME of compounds Compounds Absorption Distribution HIA (%) IVCEL (nm/sec) INVMCM (nm/sec) IVSP (logkp, cm/hour) IVPPB (%) IVBBBP (%) Chem2057082 92.5 53.8 77.5 -2.5 62.11 0.2 Chemspider6763024 91.5 1.4 73.6 -3.2 60.2 0.0 ZINC0568632 99.3 49.7 177.2 -1.8 60.2 0.0 ZINC0038846 99.0 25.8 264.6 -3.6 0.0 0.9 ZINC05105206 99.0 50.9 173.2 -2.6 0.0 1.1 Pubchem1793372 78.8 21.3 8.4 -3.5 30.2 0.1 ZINC17353697 87.4 37.9 227.9 -4.9 7.4 0.4 Etha11 87.2 21.959 30.8 2.3 85.5 5.1 Etha17 70.7 0.410 1.7 -5.0 0.0 0.5 ZINC00388344 99.039 25.8 264.6 -3.0 0.0 0.9 Etha10 82.5 19.3 0.5 -4.2 38.1 0.4 HIA: Human intestinal absorption; IVCEL: In vitro Caco-2 cell permeability; INVMCM: In vitro MDCK cell permeability; IVSP: In vitro skin per- meability; IVPPB: In vitro plasma protein binding; IVBBBP: In vivo blood-brain barrier penetration While docking the receptor was set rigid and docked with the receptor binding site inside the constraint (Figure 1 and Figure 2) where, bond flexibility of lignds was set as “on”. The docking result has predicted two database compounds and two analogues of ethambutol based on least energy score of rerank, moldock and H bond values as presen-ted in the Table V. Dock poses were further inspected for hydrogen bonding interaction with the receptor. The compound Chemspider20572082 and Zinc00388344 showed highest rerank score of -117.9 and -107.3 with optimum hydrogen bonding with the receptor including two analogue of ethambutol as shown in the Table VI. The compound Chemspider20572082 interacts with the amino acid residue Gly1058, Asp1052 and Asp1056 and forming three hydrogen bond interaction at the distance 228 Bangladesh J Pharmacol 2014; 9: 225-229 Table III Predicted binding sites of the receptor Cavity Position Volume (Å3) Surface (Å2) X Y Z 1 95.1 -6.4 6.5 97.3 368.6 2 90.3 -18.4 1.2 74.2 240.6 3 70.9 -0.7 16.2 25.6 111.4 4 70.9 -0.7 16.2 20.0 84.5 Table IV Amino acid residues around the potential binding site Ser739 Asn740 Leu743 Ala743 Leu744 Ala745 Lys747 Gly750 Leu751 Ala752 Glu753 Asp754 Val755 Leu756 Lys1050 Asp1051 Asp1052 Arg1055 Trp1057 Table VI Hydrogen bonding between ligands and receptor Ligands Ligands Distance (Å) Protein Protein Protein Ligand name Atom name Atom ID Distance (Å) Atom Name Atom ID Amino Acid Etha 9 H(1) 17 3.25 O(8) 1934 Val 1054 Etha 9 H(1) 17 2.54 N(7) 1943 Scr 1047 Etha 9 H(1) 17 2.24 O(8) 1948 Scr 1047 Etha 9 H(1) 23 2.24 O(8) 2036 Asp1056 Chem2057082 H(1) 25 2.43 O(8) 2055 Gly1058 Chem2057082 H(1) 30 2.35 O(8) 1989 Asp1052 Chem2057082 H(1) 26 1.99 O(8) 2036 Asp1056 ZINCOO388344 H(1) 22 2.12 O(8) 1934 Val1045 ZINCOO388344 H(1) 22 2.22 O(8) 2061 Leu1060 ZINCOO388344 H(1) 22 2.26 O(8) 2064 Leu1060 Ethambutol H(1) 20 2.03 O(8) 1948 Ser1047 Ethambutol H(1) 29 2.32 O(8) 2055 Gly1058 Ethambutol H(1) 29 3.01 O(8) 2036 Asp1056 Figure 1: Predicted cavities of arabinosyl transferase Figure 2: Receptor-ligand H bond interaction of 2.43, 2.35 and 1.99 Å respectively. ZINCOO388344 is forming three hydrogen bonds with Val1045 and Leu1060 and clearly reflecting its novelty as a inhibitor of M. tuberculosis arabinosyl transferase in compared with ethambutol having poor reranking score of - 39.980. Screening for alternative and effective drug is urgently needed to combat the drug resistance straings of M. tuburculosis (Burris, 2004; McIlleron et al., 2009; Zhang et al., 2014). The failure of ethambutol is another challenge. Therefore to meet the present challenges for inhibition of M. tuberculosis arabinosyl transferase by these compounds would be a useful starting point to design better therapeutics of M. tuberculosis and in vitro experiment on compounds, viz. Chem2057082 and ZINCOO388344 is recommended. In this investigation, virtual screening has been perfor- med using various filters. The screened compounds are subjected to molecular docking and result are analysis on the basic of rerank score and hydrogen bond interaction and it was found that 3 compounds showed better result out of 31 docked compound than the control drug. Further ADME and pharmacological effects of these compounds observed comparatively better bio-availability, distribution, absorption, drug likeness, and pharmacological effects than ethambutol. Hence, it could be concluded that these three com- pounds could be considered as potent drug candidate of M. tuberculosis. Acknowledgement The authors thankfully acknowledge the Department of Biotechnology (DBT), Govt. of India for providing the Bioinformatics Infrastructure Facility (BIF) to CSIR-NEIST, Jorhat under the project “Creation of BIF for the promotion of biology teaching through bioinformatics, 2008”. References Amin AG, Goude R, Shi L, Zhang J, Chatterjee D, Parish T. EmbA is an essential arabinosyltransferase in Mycobacterium tuberculosis. 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Author Info Dhrubajyoti Gogoi (Principal contact) e-mail: dhruba.bio.du@gmail.com Bangladesh J Pharmacol 2014; 9: 225-229 229 Table V Docking result Ligand MolDock score Rerank score HBond Chemspi- der20572082 -117.9 -93.1 -22.4 Zinc00388344 -107.3 -80.3 -09.0 Etha9 -104.1 -76.7 -05.5 Etha17 -95.0 -45.8 -05.7 Etha10 -61.5 -44.8 -04.2 Ethambutol -55.6 -40.0 -12.4 Materials and Methods: Result and Discussion: References: Acknowledgement: 2014 52 63841: Author Info Dhrubajyoti Gogoi Principal contact email dhrubabiodugmailcom: DatePrinted: