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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Molecular Dynamic Simulation and 3d-pharmacophore Modeling of Alpha Mangostin and Its Derivatives against Estrogen Alpha Receptor

Author(s): Luthfi Utami Setyawati, Fateen Izzah Haziqah Binti Parlan, Nur Kusaira Khairul Ikram, Muhammad Yusuf and Muchtaridi Muchtaridi*

Volume 21, Issue 6, 2024

Published on: 09 March, 2023

Page: [1103 - 1119] Pages: 17

DOI: 10.2174/1570180820666230220122600

Price: $65

Abstract

Background: Human estrogen receptor alpha (ERα), which is known to play a role in mediating cell proliferation, metastasis, and resistance to apoptosis, is one of the targets of breast cancer therapies. Alpha mangostin (AM) is an active xanthone compound from Garcinia mangostana L. which has activity as an ERα inhibitor.

Objective: This research aims to predict the pharmacokinetic and toxicity, and to study the molecular interactions of AM derivatives with the ERα using computer-aided simulation approaches through molecular docking, molecular dynamic, and pharmacophore screening to develop novel anti-breast cancer agents.

Methods: Marvinsketch and Chimera programs were used to design and optimize the structure of AM and its derivatives. For screening the pharmacokinetic and toxicity profiles, the PreADMET web was used. The AutoDockTools 1.5.6 and LigandScout 4.4.3 Advanced software were used to conduct the molecular docking simulation and pharmacophore screening, respectively, while the molecular dynamic simulation was performed using AMBER 16. The results were visualized by Biovia Discovery Studio.

Results: Molecular docking using Autodock showed that FAT10 derivate has lower binding free energy (ΔG) (-12.04 kcal/mol) than AM (-8.45 kcal/mol) when docking to ERα and both performed the same hydrogen bond with Thr347. These support the results of the MMPBSA calculation on dynamic simulation which shows FAT10 (-58.4767 kcal/mol) has lower ΔG than AM (-42.7041 kcal/mol) and 4-OHT (- 49.0821 kcal/mol). The pharmacophore screening results also showed that FAT10 fitted the pharmacophore with a fit score of 47.08.

Conclusion: From the results, it can be suggested that FAT10 has promising activity as ERα antagonist. Further in vitro and in vivo experiments should be carried out to support these in silico studies.

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[1]
Globocon. Breast Cancer. 2020. Available from: https://gco.iarc.fr/today/fact-sheets-cancers
[2]
NCI. Study Forecasts New Breast Cancer Cases by 2030: National Cancer Institute. 2015. Available from: https://www.cancer.gov/news-events/cancer-currents-blog/2015/breast-forecast
[3]
Ashtekar, S.S.; Bhatia, N.M.; Bhatia, M.S. Development of leads targeting ER-α in breast cancer: An in silico exploration from natural domain. Steroids, 2018, 131, 14-22.
[http://dx.doi.org/10.1016/j.steroids.2017.12.016] [PMID: 29307843]
[4]
Damkier, P.; Kjærsgaard, A.; Barker, K.A.; Cronin-Fenton, D.; Crawford, A.; Hellberg, Y.; Janssen, E.A.M.; Langefeld, C.; Ahern, T.P.; Lash, T.L. CYP2C19*2 and CYP2C19*17 variants and effect of tamoxifen on breast cancer recurrence: Analysis of the international tamoxifen pharmacogenomics consortium dataset. Sci. Rep., 2017, 7(1), 7727.
[http://dx.doi.org/10.1038/s41598-017-08091-x] [PMID: 28798474]
[5]
Ahern, T.P.; Hertz, D.L.; Damkier, P.; Ejlertsen, B.; Hamilton-Dutoit, S.J.; Rae, J.M.; Regan, M.M.; Thompson, A.M.; Lash, T.L.; Cronin-Fenton, D.P. Cytochrome P-450 2D6 (CYP2D6) genotype and breast cancer recurrence in tamoxifen-treated patients: Evaluating the importance of loss of heterozygosity. Am. J. Epidemiol., 2017, 185(2), 75-85.
[http://dx.doi.org/10.1093/aje/kww178] [PMID: 27988492]
[6]
Shagufta; Ahmad, I. Tamoxifen a pioneering drug: An update on the therapeutic potential of tamoxifen derivatives. Eur. J. Med. Chem., 2018, 143, 515-531.
[http://dx.doi.org/10.1016/j.ejmech.2017.11.056] [PMID: 29207335]
[7]
Hultsch, S.; Kankainen, M.; Paavolainen, L.; Kovanen, R.M.; Ikonen, E.; Kangaspeska, S.; Pietiäinen, V.; Kallioniemi, O. Association of tamoxifen resistance and lipid reprogramming in breast cancer. BMC Cancer, 2018, 18(1), 850.
[http://dx.doi.org/10.1186/s12885-018-4757-z] [PMID: 30143015]
[8]
Zheng, Q.; Xu, F.; Nie, M.; Xia, W.; Qin, T.; Qin, G.; An, X.; Xue, C.; Peng, R.; Yuan, Z.; Shi, Y.; Wang, S. Selective estrogen receptor modulator-associated nonalcoholic fatty liver disease improved survival in patients with breast cancer. Medicine, 2015, 94(40), e1718.
[http://dx.doi.org/10.1097/MD.0000000000001718] [PMID: 26448028]
[9]
Kurose, H.; Shibata, M.A.; Iinuma, M.; Otsuki, Y. Alterations in cell cycle and induction of apoptotic cell death in breast cancer cells treated with α-mangostin extracted from mangosteen pericarp. J. Biomed. Biotechnol., 2012, 2012, 1-9.
[http://dx.doi.org/10.1155/2012/672428] [PMID: 22577295]
[10]
Kritsanawong, S.; Innajak, S.; Imoto, M.; Watanapokasin, R. Antiproliferative and apoptosis induction of α-mangostin in T47D breast cancer cells. Int. J. Oncol., 2016, 48(5), 2155-2165.
[http://dx.doi.org/10.3892/ijo.2016.3399] [PMID: 26892433]
[11]
Li, P.; Tian, W.; Ma, X. Alpha-mangostin inhibits intracellular fatty acid synthase and induces apoptosis in breast cancer cells. Mol. Cancer, 2014, 13(1), 138.
[http://dx.doi.org/10.1186/1476-4598-13-138] [PMID: 24894151]
[12]
Won, Y.S. Lee, J.H.; Kwon, S.J.; Kim, J.Y.; Park, K.H.; Lee, M.K.; Seo, K.I. α-Mangostin-induced apoptosis is mediated by estrogen receptor α in human breast cancer cells. Food Chem. Toxicol., 2014, 66, 158-165.
[http://dx.doi.org/10.1016/j.fct.2014.01.040] [PMID: 24480042]
[13]
Dermawan, D.; Muchtaridi, M.; Yusuf, M. Molecular docking, 3D structure-based pharmacophore modeling, and ADME prediction of alpha mangostin and its derivatives against estrogen receptor alpha. J. Young Pharm., 2018, 10(3), 252-259.
[http://dx.doi.org/10.5530/jyp.2018.10.58]
[14]
Mardianingrum, R.; Hariono, M.; Ruswanto, R.; Yusuf, M.; Muchtaridi, M. Synthesis, anticancer activity, structure–activity relationship, and molecular modeling studies of α-mangostin derivatives as hERα inhibitor. J. Chem. Inf. Model., 2021, 62(21), 5305-5316.
[PMID: 34854302]
[15]
Mardianingrum, R. Yusuf, M.; Hariono, M.; Mohd Gazzali, A.; Muchtaridi, M. α-Mangostin and its derivatives against estrogen receptor alpha. J. Biomol. Struct. Dyn., 2022, 40(6), 2621-2634.
[http://dx.doi.org/10.1080/07391102.2020.1841031] [PMID: 33155528]
[16]
Zhao, Y.; Tang, G.; Tang, Q.; Zhang, J.; Hou, Y.; Cai, E.; Liu, S.; Lei, D.; Zhang, L.; Wang, S. A method of effectively improved α-mangostin bioavailability. Eur. J. Drug Metab. Pharmacokinet., 2016, 41(5), 605-613.
[http://dx.doi.org/10.1007/s13318-015-0283-4] [PMID: 25990757]
[17]
Yu, W.; MacKerell, A.D. Jr Computer-aided drug design methods. Methods Mol. Biol., 2017, 1520, 85-106.
[http://dx.doi.org/10.1007/978-1-4939-6634-9_5] [PMID: 27873247]
[18]
de Ruyck, J.; Brysbaert, G.; Blossey, R.; Lensink, M. Molecular docking as a popular tool in drug design, an in silico travel. Adv. Appl. Bioinform. Chem., 2016, 9, 1-11.
[http://dx.doi.org/10.2147/AABC.S105289] [PMID: 27390530]
[19]
Hospital, A.; Goñi, J.R.; Orozco, M.; Gelpí, J.L. Molecular dynamics simulations: Advances and applications. Adv. Appl. Bioinform. Chem., 2015, 8, 37-47.
[PMID: 26604800]
[20]
Santos, L.H.S.; Ferreira, R.S.; Caffarena, E.R. Integrating molecular docking and molecular dynamics simulations. Methods Mol. Biol., 2019, 2053, 13-34.
[http://dx.doi.org/10.1007/978-1-4939-9752-7_2] [PMID: 31452096]
[21]
Lee, S; Lee, IH; Kim, Hj; Chang, GS; Chung, JE; No, KT The PreADME Approach: Web-based program for rapid prediction of physico-chemical, drug absorption and drug-like properties. Euro QSAR 2002 - Designing Drugs and Crop Protectants: Processes Problems and Solutions, 2002, 418-20.
[22]
Ikram, N.K.K.; Durrant, J.D.; Muchtaridi, M.; Zalaludin, A.S.; Purwitasari, N.; Mohamed, N.; Rahim, A.S.A.; Lam, C.K.; Normi, Y.M.; Rahman, N.A.; Amaro, R.E.; Wahab, H.A. A virtual screening approach for identifying plants with anti H5N1 neuraminidase activity. J. Chem. Inf. Model., 2015, 55(2), 308-316.
[http://dx.doi.org/10.1021/ci500405g] [PMID: 25555059]
[23]
Muchtaridi, M.; Syahidah, H.; Subarnas, A.; Yusuf, M.; Bryant, S.; Langer, T. Molecular docking and 3D-pharmacophore modeling to study the interactions of chalcone derivatives with estrogen receptor alpha. Pharmaceuticals, 2017, 10(4), 81.
[http://dx.doi.org/10.3390/ph10040081] [PMID: 29035298]
[24]
Gurung, A.B.; Ali, M.A.; Lee, J.; Farah, M.A.; Al-Anazi, K.M. Molecular docking and dynamics simulation study of bioactive compounds from Ficus carica L. with important anticancer drug targets. PLoS One, 2021, 16(7), e0254035.
[25]
Wang, C.; Nguyen, P.H.; Pham, K.; Huynh, D.; Le, T.B.N.; Wang, H.; Ren, P.; Luo, R. Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis. J. Comput. Chem., 2016, 37(27), 2436-2446.
[http://dx.doi.org/10.1002/jcc.24467] [PMID: 27510546]
[26]
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem., 2012, 55(14), 6582-6594.
[http://dx.doi.org/10.1021/jm300687e] [PMID: 22716043]
[27]
Lipinski, C.A. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today. Technol., 2004, 1(4), 337-341.
[http://dx.doi.org/10.1016/j.ddtec.2004.11.007] [PMID: 24981612]
[28]
Dashti, Y.; Grkovic, T.; Quinn, R.J. Predicting natural product value, an exploration of anti-TB drug space. Nat. Prod. Rep., 2014, 31(8), 990-998.
[http://dx.doi.org/10.1039/C4NP00021H] [PMID: 24881816]
[29]
Zhao, Y.H.; Abraham, M.H.; Le, J.; Hersey, A.; Luscombe, C.N.; Beck, G.; Sherborne, B.; Cooper, I. Rate-limited steps of human oral absorption and QSAR studies. Pharm. Res., 2002, 19(10), 1446-1457.
[http://dx.doi.org/10.1023/A:1020444330011] [PMID: 12425461]
[30]
Yamashita, S.; Furubayashi, T.; Kataoka, M.; Sakane, T.; Sezaki, H.; Tokuda, H. Optimized conditions for prediction of intestinal drug permeability using Caco-2 cells. Eur. J. Pharm. Sci., 2000, 10(3), 195-204.
[http://dx.doi.org/10.1016/S0928-0987(00)00076-2] [PMID: 10767597]
[31]
Irvine, J.D.; Takahashi, L.; Lockhart, K.; Cheong, J.; Tolan, J.W.; Selick, H.E.; Grove, J.R. MDCK (Madin-Darby canine kidney) cells: A tool for membrane permeability screening. J. Pharm. Sci., 1999, 88(1), 28-33.
[http://dx.doi.org/10.1021/js9803205] [PMID: 9874698]
[32]
Ma, X.; Chen, C.; Yang, J. Predictive model of blood-brain barrier penetration of organic compounds1. Acta Pharmacol. Sin., 2005, 26(4), 500-512.
[http://dx.doi.org/10.1111/j.1745-7254.2005.00068.x] [PMID: 15780201]
[33]
Lambrinidis, G.; Vallianatou, T.; Tsantili-Kakoulidou, A. In vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review. Adv. Drug Deliv. Rev., 2015, 86, 27-45.
[http://dx.doi.org/10.1016/j.addr.2015.03.011] [PMID: 25819487]
[34]
Carvalho-Silva, R.; Pereira, A.C.F.; Alves, R.Pd.S. Guecheva, T.N.; Henriques, J.A.P.; Brendel, M.; Pungartnik, C.; Rios-Santos, F. DNA protection against oxidative damage using the hydroalcoholic extract of garcinia mangostana and alpha-mangostin. eCAM, 2016, 2016(3430405), 1-8.
[http://dx.doi.org/10.1155/2016/3430405]
[35]
Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; Chen, X.; Hou, T.; Cao, D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res., 2021, 49(W1), W5-W14.
[http://dx.doi.org/10.1093/nar/gkab255]
[36]
Nelli, G.B.; Kilari, E.K. Antidiabetic effect of α-mangostin and its protective role in sexual dysfunction of streptozotocin induced diabetic male rats. Syst Biol Reproduc Med, 2013, 59(6), 319-328.
[http://dx.doi.org/10.3109/19396368.2013.820369]
[37]
Kumar, V. Bhatt, P.C.; Kaithwas, G.; Rashid, M.; Al-Abbasi, F.; Khan, J.A.; Anwar, F.; Verma, A. α-Mangostin mediated pharmacological modulation of hepatic carbohydrate metabolism in diabetes induced wistar rat. BJBAS, 2016, 5(3), 255-276.
[http://dx.doi.org/10.1016/j.bjbas.2016.07.001]
[38]
Bunyong, R.; Chaijaroenkul, W.; Plengsuriyakarn, T.; Na-bangchang, K. Antimalarial activity and toxicity of Garcinia mangostana linn. Asian Pac. J. Trop. Med., 2014, 7(9), 693-698.
[http://dx.doi.org/10.1016/S1995-7645(14)60118-8]
[39]
Jujun, P.; Pootakham, K.; Pongpaibul, Y.; Duangrat, C.; Tharavichitkul, P. Acute and repeated dose 28-day oral toxicity study of Garcinia mangostana linn. rind extract. J. Nat. Sci., 2008, 7(2), 199-208.
[40]
Sunarjo, L. Suharti; Oedijani; Susanto, H.S. The preliminary study on safety of using mangosteen peel extract as natural herbs. J. Med. Sci. Clin. Res., 2017, 5(7), 24851-24856.
[http://dx.doi.org/10.18535/jmscr/v5i7.92]
[41]
Shiau, A.K.; Barstad, D.; Loria, P.M.; Cheng, L.; Kushner, P.J.; Agard, D.A.; Greene, G.L. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell, 1998, 95(7), 927-937.
[http://dx.doi.org/10.1016/S0092-8674(00)81717-1] [PMID: 9875847]
[42]
Eiler, S.; Gangloff, M.; Duclaud, S.; Moras, D.; Ruff, M. Overexpression, purification, and crystal structure of native ER alpha LBD. Protein Expr. Purif., 2001, 22(2), 165-173.
[http://dx.doi.org/10.1006/prep.2001.1409] [PMID: 11437591]
[43]
Srinivasan, S.; Nwachukwu, J.C.; Parent, A.A.; Cavett, V.; Nowak, J.; Hughes, T.S.; Kojetin, D.J.; Katzenellenbogen, J.A.; Nettles, K.W. Ligand-binding dynamics rewire cellular signaling via estrogen receptor-α. Nat. Chem. Biol., 2013, 9(5), 326-332.
[http://dx.doi.org/10.1038/nchembio.1214] [PMID: 23524984]
[44]
Lee, S.; Barron, M.G. Structure-based understanding of binding affinity and mode of estrogen receptor α agonists and antagonists. PLoS One, 2017, 12(1), e0169607.
[http://dx.doi.org/10.1371/journal.pone.0169607] [PMID: 28061508]
[45]
Delfosse, V.; Grimaldi, M.; Cavaillès, V.; Balaguer, P.; Bourguet, W. Structural and functional profiling of environmental ligands for estrogen receptors. Environ. Health Perspect., 2014, 122(12), 1306-1313.
[http://dx.doi.org/10.1289/ehp.1408453] [PMID: 25260197]
[46]
Celik, L.; Lund, J.D.D.; Schiøtt, B. Conformational dynamics of the estrogen receptor α molecular dynamics simulations of the influence of binding site structure on protein dynamics. Biochemistry, 2007, 46(7), 1743-1758.
[http://dx.doi.org/10.1021/bi061656t] [PMID: 17249692]
[47]
Muchtaridi, M.; Yusuf, M.; Diantini, A.; Choi, S.; Al-Najjar, B.; Manurung, J.; Subarnas, A.; Achmad, T.; Wardhani, S.; Wahab, H. Potential activity of fevicordin-A from Phaleria macrocarpa (Scheff) Boerl. seeds as estrogen receptor antagonist based on cytotoxicity and molecular modelling studies. Int. J. Mol. Sci., 2014, 15(5), 7225-7249.
[http://dx.doi.org/10.3390/ijms15057225] [PMID: 24776765]
[48]
Pavlin, M.; Spinello, A.; Pennati, M.; Zaffaroni, N.; Gobbi, S.; Bisi, A.; Colombo, G.; Magistrato, A. A computational assay of estrogen receptor α antagonists reveals the key common structural traits of drugs effectively fighting refractory breast cancers. Sci. Rep., 2018, 8(1), 649.
[http://dx.doi.org/10.1038/s41598-017-17364-4] [PMID: 29330437]
[49]
Zheng, L.; Meng, J.; Jiang, K.; Lan, H.; Wang, Z.; Lin, M.; Li, W.; Guo, H.; Wei, Y.; Mu, Y. Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term. Brief. Bioinform., 2022, 23(3), bbac051.
[http://dx.doi.org/10.1093/bib/bbac051] [PMID: 35289359]
[50]
Su, M.; Yang, Q.; Du, Y.; Feng, G.; Liu, Z.; Li, Y.; Wang, R. Comparative assessment of scoring functions: The CASF-2016 update. J. Chem. Inf. Model., 2019, 59(2), 895-913.
[http://dx.doi.org/10.1021/acs.jcim.8b00545] [PMID: 30481020]
[51]
Kirchmair, J.; Markt, P.; Distinto, S.; Wolber, G.; Langer, T. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? J. Comput. Aided Mol. Des., 2008, 22(3-4), 213-228.
[http://dx.doi.org/10.1007/s10822-007-9163-6] [PMID: 18196462]
[52]
C, S.; S, D.K.; Ragunathan, V.; Tiwari, P.; A, S.; P, B.D. Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease. J. Biomol. Struct. Dyn., 2022, 40(2), 585-611.
[http://dx.doi.org/10.1080/07391102.2020.1815584] [PMID: 32897178]
[53]
Zheng, X.; Polli, J. Identification of inhibitor concentrations to efficiently screen and measure inhibition Ki values against solute carrier transporters. Eur. J. Pharm. Sci., 2010, 41(1), 43-52.
[http://dx.doi.org/10.1016/j.ejps.2010.05.013] [PMID: 20553862]
[54]
Celik, L.; Lund, J.D.; Schiøtt, B. Conformational dynamics of the estrogen receptor alpha: molecular dynamics simulations of the influence of binding site structure on protein dynamics. Biochemistry, 2007, 46(7), 1743-1758.
[55]
Case, D.A.; Cheatham, T.E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688.
[http://dx.doi.org/10.1002/jcc.20290] [PMID: 16200636]
[56]
Li, W.M.; Li, X.B.; Sun, S.X.; Liang, J.; Wang, R.L.; Wang, S.Q. Agonist and antagonist recognition studies for oestrogen receptor by molecular dynamics simulation. Mol. Simul., 2013, 39(3), 228-233.
[http://dx.doi.org/10.1080/08927022.2012.717281]
[57]
Qing, X-Y.; Lee, X.Y.; De Raeymaecker, J.; Tame, J.; Zhang, K.; De Maeyer, M. Pharmacophore modeling: Advances, limitations, and current utility in drug discovery. J. Receptor Ligand Channel Res., 2014, 7, 81-92.
[58]
Choudhury, C.; Narahari Sastry, G. Pharmacophore Modelling and Screening: Concepts, Recent Developments and Applications in Rational Drug Design. In: Structural Bioinformatics: Applications in Preclinical Drug Discovery Process; Mohan, C.G., Ed.; Springer International Publishing: Cham, 2019; pp. 25-53.
[http://dx.doi.org/10.1007/978-3-030-05282-9_2]
[59]
Drwal, M.N.; Griffith, R. Combination of ligand- and structure-based methods in virtual screening. Drug Discov. Today. Technol., 2013, 10(3), e395-e401.
[http://dx.doi.org/10.1016/j.ddtec.2013.02.002] [PMID: 24050136]
[60]
Giordano, D.; Biancaniello, C.; Argenio, M.A.; Facchiano, A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals, 2022, 15(5), 646.
[http://dx.doi.org/10.3390/ph15050646] [PMID: 35631472]
[61]
Luo, L.; Zhong, A.; Wang, Q.; Zheng, T. Structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET, and Molecular Dynamics (MD) simulation of potential inhibitors of PD-L1 from the library of marine natural products. Mar. Drugs, 2021, 20(1), 29.
[http://dx.doi.org/10.3390/md20010029] [PMID: 35049884]

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