Home >Current Issue

Volume: 9, Issue: 7, July, 2019
DOI: 10.7324/JAPS.2019.90702



Research Article

Identification of polyketide synthase 13 inhibitor: Pharmacophore-based virtual screening and molecular dynamics simulation

Muhammad Arba1, Andy-Nur Hidayat1, Henny Kasmawati1, Yamin Yamin1, Ida Usman2

  Author Affiliations


Abstract

Polyketide synthase 13 (Pks13) is one of prominent targets to treat Mycobacterium tuberculosis (Mtb). In the present study, pharmacophore features for Pks13, including two hydrogen bond donors, one hydrogen bond acceptor, and one hydrophobic feature, were built using a novel Pks13 inhibitor, TAM16. The pharmacophore features were then used to perform virtual screening on ZINC database to identify small molecules of Pks13 inhibitors. The obtained virtual hits of 107 small molecules were subjected to molecular docking studies employing iDock software to reveal their binding orientation to Pks13. Furthermore, four best hits, each bound to Pks13, were submitted to 40-ns molecular dynamics simulation to explore their conformational changes throughout simulation. The result showed that all hit compounds, i.e., Lig79/ZINC09281113, Lig94/ZINC09584070, Lig95/ZINC09209668, and Lig97/ZINC09216165, have better stabilities than that of TAM16 as indicated by their lower values of root-mean-square-deviation and root-mean-square-fluctuation. In a similar way, prediction of binding free energy using molecular mechanics Poisson– Boltzmann Surface Area method showed that all hit compounds have lower binding free energies than that of TAM16, indicating their potential as novel compounds of Pks13 inhibitors.

Keywords:

Polyketide synthase 13, pharmacophore modeling, molecular docking, molecular dynamics simulation.



Citation: Arba M, Hidayat AN, Kasmawati H, Yamin Y, Usman I. Identification of polyketide synthase 13 inhibitor: Pharmacophore-based virtual screening and molecular dynamics simulation. J Appl Pharm Sci, 2019; 9(07):012–017.


Copyright: The Author(s). This is an open access article distributed under the Creative Commons Attribution Non-Commercial License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

Aggarwal A, Parai MK, Shetty N, Wallis D, Woolhiser L, Hastings C, Dutta NK, Galaviz S, Shakal RC, Shrestha R, Wakabayashi S, Walpole C, Matthews D, Floyd D, Scullion P, Riley J, Epemolu O, Norval S, Sacchettini JC. Development of a novel lead that targets M. tuberculosis polyketide synthase 13. Cell, 2017; 170(2):249-59. https://doi.org/10.1016/j.cell.2017.06.025

Alangaden GJ, Kreiswirth BN, Aouad A, Khetarpal M, Igno FR, Moghazeh SL, Manavathu EK, Lerner SA. Mechanism of resistance to amikacin and kanamycin in mycobacterium tuberculosis. Antimicrob Agents Ch, 1998; 42(5):1295-7. https://doi.org/10.1128/AAC.42.5.1295

Arba M, Nur-Hidayat A, Surantaadmaja SI, Tjahjono DH. Pharmacophore-based virtual screening for identifying β5 subunit inhibitor of 20S proteasome. Comput Biol Chem, 2018a; 77:64-71. https://doi.org/10.1016/j.compbiolchem.2018.08.009

Arba M, Ihsan S, Tjahjono DH. Computational approach toward targeting the interaction of porphyrin derivatives with Bcl-2. J Appl Pharm Sci, 2018b; 8(12):60-6. https://doi.org/10.7324/JAPS.2018.81208

Arba M, Kartasasmita RE, Tjahjono DH. Molecular docking and dynamics simulations on the interaction of cationic porphyrin-anthraquinone hybrids with DNA G-quadruplexes, J Biomol Struct Dyn, 2016; 34(2):427-38. https://doi.org/10.1080/07391102.2015.1033015

Barry CE, Lee RE, Mdluli K, Sampson AE, Schroeder BG, Slayden RA, Yuan Y. Mycolic acids: structure, biosynthesis and physiological functions. Prog Lipid Res, 1998; 37(2):143-79. https://doi.org/10.1016/S0163-7827(98)00008-3

Belardinelli JM, Morbidoni HR. Recycling and refurbishing old antitubercular drugs: the encouraging case of inhibitors of mycolic acid biosynthesis. Expert Rev Anti Infe, 2013; 11(4):429-40. https://doi.org/10.1586/eri.13.24

Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ. The Amber biomolecular simulation programs. J Comput Chem, 2005; 26(16):1668-88. https://doi.org/10.1002/jcc.20290

Cruz JN, Costa JFS, Khayat AS, Kuca K, Barros CAL, Neto AMJC. Molecular dynamics simulation and binding free energy studies of novel leads belonging to the benzofuran class inhibitors of Mycobacterium tuberculosis Polyketide Synthase 13. J Biomol Struct Dyn, 2018; 1-12. https://doi.org/10.1080/07391102.2018.1462734

Dror O, Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. Novel approach for efficient pharmacophore-based virtual screening: method and applications. J Chem Inf Mod, 2009; 49(10):2333-43. https://doi.org/10.1021/ci900263d

Glaziou P, Sismanidis C, Floyd K, Raviglione M. Global epidemiology of tuberculosis. Csh Perspect Med, 2015; 5(2):a017798. https://doi.org/10.1101/cshperspect.a017798

Ioerger TR, O'Malley T, Liao R, Guinn KM, Hickey MJ, Mohaideen N, Murphy KC, Boshoff HIM, Mizrahi V, Rubin EJ, Sassetti CM, Barry III CE, Sherman DR, Parish T, Sacchettini JC. Identification of new drug targets and resistance mechanisms in mycobacterium tuberculosis. PLoS One, 2013; 8(9):e75245. https://doi.org/10.1371/journal.pone.0075245

Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model, 2012; 52(7):1757-68. https://doi.org/10.1021/ci3001277

Jakalian A, Jack DB, Bayly CI. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem, 2002; 23(16):1623-41. https://doi.org/10.1002/jcc.10128

Koes DR, Camacho CJ. ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res, 2012; 40(W1):W409-14. https://doi.org/10.1093/nar/gks378

Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong, L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res, 2000; 33(12):889-97. https://doi.org/10.1021/ar000033j

Kothandan S, Sasikala RP, Meena KS. Structure based pharmacophore modeling, virtual screening and molecular docking of potential phytochemicals against HSP70. J Appl Pharm Sci, 2017; 7(02):137-41.

Krüüner A, Jureen P, Levina K, Ghebremichael S, Hoffner S. Discordant resistance to kanamycin and amikacin in drug-resistant mycobacterium tuberculosis; Antimicrob Agents Ch, 2003; 47(9):2971-3. https://doi.org/10.1128/AAC.47.9.2971-2973.2003

Li H, Leung K-S, Wong M-H. idock: a multithreaded virtual screening tool for flexible ligand docking. Proceedings of the 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 77-84, 2012. https://doi.org/10.1109/CIBCB.2012.6217214

Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput, 2015; 11(8):3696-713. https://doi.org/10.1021/acs.jctc.5b00255

Manabe YC, Hermans SM, Lamorde M, Castelnuovo B, Mullins CD, Kuznik A. Rifampicin for continuation phase tuberculosis treatment in Uganda: a cost-effectiveness analysis. PLoS One, 2012; 7(6):e39187. https://doi.org/10.1371/journal.pone.0039187

Maus CE, Plikaytis BB, Shinnick TM. Molecular analysis of cross-resistance to capreomycin, kanamycin, amikacin, and viomycin in mycobacterium tuberculosis. Antimicrob Agents Ch, 2005; 49(8):3192-7. https://doi.org/10.1128/AAC.49.8.3192-3197.2005

Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem, 1998; 19(14):1639-62. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14< 1639::AID-JCC10>3.0.CO;2-B

Padiadpu J, Mukherjee S, Chandra N. Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs. J Biomol Struct Dyn, 2013; 31(1):44-58. https://doi.org/10.1080/07391102.2012.691361

Pandey B, Grover S, Goyal S, Jamal S, Singh A, Kaur J, Grover A. Novel missense mutations in gidB gene associated with streptomycin resistance in Mycobacterium tuberculosis: insights from molecular dynamics. J Biomol Struct Dyn, 2019; 37(1):20-35. https://doi.org/10.1080/07391102.2017.1417913

Portevin D, de Sousa-D-D'Auria C, Houssin C, Grimaldi C, Chami M, Daffé M, Guilhot C. A polyketide synthase catalyzes the last condensation step of mycolic acid biosynthesis in mycobacteria and related organisms. Proc Natl Acad Sci USA, 2004; 101(1):314-9. https://doi.org/10.1073/pnas.0305439101

Ryu YJ. Diagnosis of pulmonary tuberculosis: recent advances and diagnostic algorithms. Tuberc Respir Dis, 2015; 78(2):64-71. https://doi.org/10.4046/trd.2015.78.2.64

Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh ewald. J Chem Theory Comput, 2013; 9(9):3878-88. https://doi.org/10.1021/ct400314y

Thanna S, Sucheck SJ. Targeting the trehalose utilization pathways of Mycobacterium tuberculosis. Med Chem Commun, 2016; 7(1):69-85. https://doi.org/10.1039/C5MD00376H

Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general amber force field. J Comput Chem, 2004; 25(9):1157-74. https://doi.org/10.1002/jcc.20035

Wilson R, Kumar P, Parashar V, Vilchèze C, Veyron-Churlet R, Freundlich JS, Barnes SW, Walker JR, Szymonifka MJ, Marchiano E, Shenai S, Colangeli R, Jacobs Jr WR, Neiditch MB, Kremer L, Alland D. Antituberculosis thiophenes define a requirement for Pks13 in mycolic acid biosynthesis. Nat Chem Biol, 2013; 9:499-506. https://doi.org/10.1038/nchembio.1277

World Health Organization. Global tuberculosis report 2017. Available via http://www.who.int/tb/publications/global_report/en/

Article Metrics

Similar Articles

Structure based Pharmacophore modeling, Virtual screening and Molecular Docking of Potential Phytochemicals against HSP70
Sangeetha Kothandan, R. P. Sasikala, K. S. Meena

Cytotoxicity Studies of Xanthorrhizol and Its Mechanism Using Molecular Docking Simulation and Pharmacophore Modelling
Ida Musfiroh, Muchtaridi Muchtaridi, Ahmad Muhtadi, Ajeng Diantini, Aliya Nur Hasanah, Linar Zalinar Udin, Yasmiwar Susilawati, Resmi Mustarichie, Rahmana E Kartasasmita, Slamet Ibrahim

Molecular construction of NADH-cytochrome b5 reductase inhibition by flavonoids and chemical basis of difference in inhibition potential: Molecular dynamics simulation study
Sharad Verma, Amit Singh, Abha Mishra