Login Register Cart 0
Generic placeholder image

Letters in Drug Design & Discovery

Editor-in-Chief

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

Back
Research Article

Ligand-based Pharmacophore Modeling, Molecular Docking and Simulation Studies for the Exploration of Natural Potent Antiangiogenic Inhibitors Targeting Heat Shock Protein 90

Author(s): Neha Sharma, Mala Sharma, Mohammad Faisal, Abdulrahman A. Alatar, Rajnish Kumar, Saheem Ahmad and Salman Akhtar*

Volume 20, Issue 1, 2023

Published on: 13 October, 2022

Page: [95 - 109] Pages: 15

DOI: 10.2174/1570180819666220921165802

Price: $65

Abstract

Background: HSP90, a critical molecular chaperone, has become a promising molecular target to be involved in multiple signaling pathways of tumor progression and metastasis.

Objective: This study intends to find a novel phytolead targeting HSP90.

Methods: In this scenario, we employed an in silico combinatorial approach incorporating 3D-QSAR, pharmacophore generation, pharmacokinetics, docking, MD simulation and metabolism studies.

Results: To find a natural novel compound targeting HSP90, a ligand-based pharmacophore model was developed, exploiting 17 diversely classified training set molecules with known experimental activity exhausting the pharmacophore generation (HypoGen algorithm) module of Discovery Studio. The bestdeveloped hypothesis (Hypo1) was employed against the UNPD database to screen lead compounds targeting HSP90. Pterodontoside G (Asteraceae family)became a potent compound with the fit value of 8.80 and an estimated activity of 3.28 nM. Pterodontoside G was taken forward for analog design and pharmacokinetics studies, followed by docking and MD simulation studies. UNPD1 came out to be the best analog following all pharmacokinetics properties with the highest binding energy in comparison with the parent compound and the standard drug (Ganetespib). It mapped all the features of Hypo1 with a fit value of 8.68 and an estimated activity of 4.314 nM, exhibiting greater binding stability inside the active site of HSP90 causing no conformational changes in the protein-ligand complex during MD analysis.

Conclusion: The result was further supported by PASS analysis and xenosite reactivity data proposing UNPD1 to hold potent antiangiogenic potential targeting HSP90.

Keywords: Angiogenesis, HSP90, Pharmacophore modeling, Docking, Molecular dynamics, xenosite reactivity

« Previous Next »
Graphical Abstract

[1]
Bohonowych, J.E.; Gopal, U.; Isaacs, J.S. Hsp90 as a gatekeeper of tumor angiogenesis: Clinical promise and potential pitfalls. J. Oncol., 2010, 2010, 1-17.
[http://dx.doi.org/10.1155/2010/412985] [PMID: 20628489]
[2]
Moser, C.; Lang, S.A.; Stoeltzing, O. Heat-shock protein 90 (Hsp90) as a molecular target for therapy of gastrointestinal cancer. Anticancer Res., 2009, 29(6), 2031-2042.
[PMID: 19528462]
[3]
Lee, H.; Saini, N.; Howard, E.W.; Parris, A.B.; Ma, Z.; Zhao, Q.; Zhao, M.; Liu, B.; Edgerton, S.M.; Thor, A.D.; Yang, X. Ganetespib targets multiple levels of the receptor tyrosine kinase signaling cascade and preferentially inhibits ErbB2-overexpressing breast cancer cells. Sci. Rep., 2018, 8(1), 6829.
[http://dx.doi.org/10.1038/s41598-018-25284-0] [PMID: 29717218]
[4]
Ochiana, S.O.; Taldone, T.; Chiosis, G. Designing drugs against Hsp90 for cancer therapy. In: The Molecular Chaperones Interaction Networks in Protein Folding and Degradation; Houry, W., Ed.; Springer: New York, NY, 2014; pp. 151-183.
[http://dx.doi.org/10.1007/978-1-4939-1130-1_7]
[5]
Neckers, L.; Workman, P. Hsp90 molecular chaperone inhibitors: Are we there yet? Clin. Cancer Res., 2012, 18(1), 64-76.
[http://dx.doi.org/10.1158/1078-0432.CCR-11-1000] [PMID: 22215907]
[6]
Miyata, Y.; Nakamoto, H.; Neckers, L. The therapeutic target Hsp90 and cancer hallmarks. Curr. Pharm. Des., 2013, 19(3), 347-365.
[http://dx.doi.org/10.2174/138161213804143725] [PMID: 22920906]
[7]
Yuno, A.; Lee, M.J.; Lee, S.; Tomita, Y.; Rekhtman, D.; Moore, B.; Trepel, J.B. Clinical evaluation and biomarker profiling of Hsp90 inhibitors. In: Chaperones; Calderwood, S.; Prince, T., Eds.; vol 1709. Humana Press: New York, NY., 2018; 1709, pp. 423-441.
[http://dx.doi.org/10.1007/978-1-4939-7477-1_29]
[8]
Sakkiah, S.; Thangapandian, S.; John, S.; Kwon, Y.J.; Lee, K.W. 3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors. Eur. J. Med. Chem., 2010, 45(6), 2132-2140.
[http://dx.doi.org/10.1016/j.ejmech.2010.01.016] [PMID: 20206418]
[9]
Pathak, G.; Singh, S.; Kumari, P.; Raza, W.; Hussain, Y.; Meena, A. Cirsimaritin, a lung squamous carcinoma cells (NCIH-520) proliferation inhibitor. J. Biomol. Struct. Dyn., 2020, 1-12.
[http://dx.doi.org/10.1080/07391102.2020.1763198] [PMID: 32362196]
[10]
Dai, S.X.; Li, W.X.; Han, F.F.; Guo, Y.C.; Zheng, J.J.; Liu, J.Q.; Wang, Q.; Gao, Y.D.; Li, G.H.; Huang, J.F. In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database. Sci. Rep., 2016, 6(1), 25462.
[http://dx.doi.org/10.1038/srep25462] [PMID: 27145869]
[11]
Barh, D. Dietary phytochemicals: A promise to chemoprevention. Adv. Biotech., 2008, 20058, 21-23.
[12]
Sharma, N.; Sharma, M.; Shakeel, E.; Jamal, Q.M.S.; Kamal, M.A.; Sayeed, U.; Khan, M.K.A.; Siddiqui, M.H.; Arif, J.M.; Akhtar, S. Molecular interaction and computational analytical studies of pinocembrin for its antiangiogenic potential targeting VEGFR-2: A persuader of metastasis. Med. Chem., 2018, 14(6), 626-640.
[http://dx.doi.org/10.2174/1573406414666180416125121] [PMID: 29663896]
[13]
Kumar, R.B.; Suresh, M.X. Pharmacophore mapping based inhibitor selection and molecular interaction studies for identification of potential drugs on calcium activated potassium channel blockers, tamulotoxin. Pharmacogn. Mag., 2013, 9(34), 89-95.
[http://dx.doi.org/10.4103/0973-1296.111239] [PMID: 23772102]
[14]
Kandakatla, N.; Ramakrishnan, G. Ligand based pharmacophore modeling and virtual screening studies to design novel HDAC2 inhibitors. Adv. Bioinforma., 2014, 2014, 1-11.
[http://dx.doi.org/10.1155/2014/812148] [PMID: 25525429]
[15]
Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 2016, 44(D1), D1045-D1053.
[http://dx.doi.org/10.1093/nar/gkv1072] [PMID: 26481362]
[16]
Gupta, C.L.; Babu Khan, M.; Ampasala, D.R.; Akhtar, S.; Dwivedi, U.N.; Bajpai, P. Pharmacophore-based virtual screening approach for identification of potent natural modulatory compounds of human Toll-like receptor 7. J. Biomol. Struct. Dyn., 2019, 37(18), 4721-4736.
[http://dx.doi.org/10.1080/07391102.2018.1559098] [PMID: 30661449]
[17]
Jia, J.; Xu, X.; Liu, F.; Guo, X.; Zhang, M.; Lu, M.; Xu, L.; Wei, J.; Zhu, J.; Zhang, S.; Zhang, S.; Sun, H.; You, Q. Identification, design and bio-evaluation of novel Hsp90 inhibitors by ligand-based virtual screening. PLoS One, 2013, 8(4), e59315.
[http://dx.doi.org/10.1371/journal.pone.0059315] [PMID: 23565147]
[18]
Ponnan, P.; Gupta, S.; Chopra, M.; Tandon, R.; Baghel, A.S.; Gupta, G.; Prasad, A.K.; Rastogi, R.C.; Bose, M.; Raj, H.G. 2D-QSAR, docking studies, and in silico ADMET prediction of polyphenolic acetates as substrates for protein acetyltransferase function of glutamine synthetase of Mycobacterium tuberculosis. ISRN Struct. Biol., 2013, 2013, 1-12.
[http://dx.doi.org/10.1155/2013/373516]
[19]
Kumar, R.B.; Suresh, M.X.; Priya, B.S. Pharmacophore modeling, in silico screening, molecular docking and molecular dynamics approaches for potential alpha-delta bungarotoxin-4 inhibitors discovery. Pharmacogn. Mag., 2015, 11(42)(Suppl. 1), 19.
[http://dx.doi.org/10.4103/0973-1296.157670] [PMID: 26109766]
[20]
Rudik, A.V.; Dmitriev, A.V.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. PASS-based prediction of metabolites detection in biological systems. SAR QSAR Environ. Res., 2019, 30(10), 751-758.
[http://dx.doi.org/10.1080/1062936X.2019.1665099] [PMID: 31542944]
[21]
Braga, R.C.; Alves, V.M.; Silva, M.F.B.; Muratov, E.; Fourches, D.; Lião, L.M.; Tropsha, A.; Andrade, C.H. Pred‐hERG: A novel web‐accessible computational tool for predicting cardiac toxicity. Mol. Inform., 2015, 34(10), 698-701.
[http://dx.doi.org/10.1002/minf.201500040] [PMID: 27490970]
[22]
Braga, R.; Alves, V.; Silva, M.; Muratov, E.; Fourches, D.; Tropsha, A.; Andrade, C. Tuning HERG out: Antitarget QSAR models for drug development. Curr. Top. Med. Chem., 2014, 14(11), 1399-1415.
[http://dx.doi.org/10.2174/1568026614666140506124442] [PMID: 24805060]
[23]
Nachiappan, M.; Jain, V.; Sharma, A.; Manickam, Y.; Jeyakanthan, J. Conformational changes in glutaminyl-tRNA synthetases upon binding of the substrates and analogs using molecular docking and molecular dynamics approaches. J. Biomol. Struct. Dyn., 2019, 38(6), 1-15.
[http://dx.doi.org/10.1080/07391102.2019.1617787] [PMID: 31084346]
[24]
Sharma, N.; Sharma, M.; Rahman, Q.I.; Akhtar, S.; Muddassir, M. Quantitative structure activity relationship and molecular simulations for the exploration of natural potent VEGFR-2 inhibitors: An in silico anti-angiogenic study. J. Biomol. Struct. Dyn., 2020, 1-18.
[http://dx.doi.org/10.1080/07391102.2020.1754916] [PMID: 32363995]
[25]
Matlock, M.K.; Hughes, T.B.; Swamidass, S.J. XenoSite server: A web-available site of metabolism prediction tool. Bioinformatics, 2015, 31(7), 1136-1137.
[http://dx.doi.org/10.1093/bioinformatics/btu761] [PMID: 25411327]
[26]
Paramashivam, S.K.; Elayaperumal, K.; Natarajan, B.; Ramamoorthy, M.; Balasubramanian, S.; Dhiraviam, K. In silico pharmacokinetic and molecular docking studies of small molecules derived from Indigofera aspalathoides vahl targeting receptor tyrosine kinases. Bioinformation, 2015, 11(2), 73-84.
[http://dx.doi.org/10.6026/97320630011073] [PMID: 25848167]
[27]
Bharath, E.N.; Manjula, S.N.; Vijaychand, A. In silico drug design tool for overcoming the innovation deficit in the drug discovery process. Int. J. Pharm. Pharm. Sci., 2011, 18, 8-12.
[28]
Sak, K. Chemotherapy and dietary phytochemical agents. Chemother. Res. Pract., 2012, 2012, 1-11.
[http://dx.doi.org/10.1155/2012/282570] [PMID: 23320169]
[29]
Kujawski, J.; Popielarska, H.; Myka, A.; Drabinska, B.; Bernard, M.K. The log P parameter as a molecular descriptor in the computer-aided drug design-An overview. Comput. Methods Sci. Technol., 2012, 18(2), 81-88.
[30]
Filimonov, D.A.; Druzhilovskiy, D.S.; Lagunin, A.A.; Gloriozova, T.A.; Rudik, A.V.; Dmitriev, A.V.; Pogodin, P.V.; Poroikov, V.V. Computer-aided prediction of biological activity spectra for chemical compounds: Opportunities and limitation. Biomed. Chem.: Res. Methods, 2018, 1(1), e00004-e00004.
[http://dx.doi.org/10.18097/BMCRM00004]
[31]
Lamothe, S.M.; Guo, J.; Li, W.; Yang, T.; Zhang, S. The human ether-a-go-go-related gene (hERG) potassium channel represents an unusual target for protease-mediated damage. J. Biol. Chem., 2016, 291(39), 20387-20401.
[http://dx.doi.org/10.1074/jbc.M116.743138] [PMID: 27502273]
[32]
Tahir ul Qamar, M.; Maryam, A.; Muneer, I.; Xing, F.; Ashfaq, U.A.; Khan, F.A.; Anwar, F.; Geesi, M.H.; Khalid, R.R.; Rauf, S.A.; Siddiqi, A.R. Computational screening of medicinal plant phytochemicals to discover potent pan-serotype inhibitors against dengue virus. Sci. Rep., 2019, 9(1), 1433.
[http://dx.doi.org/10.1038/s41598-018-38450-1]
[33]
Sławiński, J.; Grzonek, A.; Żołnowska, B.; Kawiak, A. Synthesis of novel pyrido[4,3-e][1,2,4]triazino[3,2-c][1,2,4]thiadiazine 6,6-dioxide derivatives with potential anticancer activity. Molecules, 2015, 21(1), 41.
[http://dx.doi.org/10.3390/molecules21010041] [PMID: 26729078]
[34]
Hughes, T.B.; Swamidass, S.J. Deep learning to predict the formation of quinone species in drug metabolism. Chem. Res. Toxicol., 2017, 30(2), 642-656.
[http://dx.doi.org/10.1021/acs.chemrestox.6b00385] [PMID: 28099803]
[35]
Hughes, T.B.; Dang, N.L.; Miller, G.P.; Swamidass, S.J. Modeling reactivity to biological macromolecules with a deep multitask network. ACS Cent. Sci., 2016, 2(8), 529-537.
[http://dx.doi.org/10.1021/acscentsci.6b00162] [PMID: 27610414]
[36]
Hughes, T.B.; Miller, G.P.; Swamidass, S.J. Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione. Chem. Res. Toxicol., 2015, 28(4), 797-809.
[http://dx.doi.org/10.1021/acs.chemrestox.5b00017] [PMID: 25742281]
[37]
Dang, N.L.; Hughes, T.B.; Krishnamurthy, V.; Swamidass, S.J. A simple model predicts UGT-mediated metabolism. Bioinformatics, 2016, 32(20), 3183-3189.
[http://dx.doi.org/10.1093/bioinformatics/btw350] [PMID: 27324196]
[38]
DeZwaan, D.C.; Freeman, B.C. HSP90 manages the ends. Trends Biochem. Sci., 2010, 35(7), 384-391.
[http://dx.doi.org/10.1016/j.tibs.2010.02.005] [PMID: 20236825]
[39]
Schultz, T.W.; Yarbrough, J.W.; Hunter, R.S.; Aptula, A.O. Verification of the structural alerts for Michael acceptors. Chem. Res. Toxicol., 2007, 20(9), 1359-1363.
[http://dx.doi.org/10.1021/tx700212u] [PMID: 17672510]
[40]
Attia, S.M. Deleterious effects of reactive metabolites. Oxid. Med. Cell. Longev., 2010, 3(4), 238-253.
[http://dx.doi.org/10.4161/oxim.3.4.13246] [PMID: 20972370]
[41]
Testa, B.; Pedretti, A.; Vistoli, G. Reactions and enzymes in the metabolism of drugs and other xenobiotics. Drug Discov. Today, 2012, 17(11-12), 549-560.
[http://dx.doi.org/10.1016/j.drudis.2012.01.017] [PMID: 22305937]
[42]
Hughes, T.B.; Miller, G.P.; Swamidass, S.J. Modeling epoxidation of drug-like molecules with a deep machine learning network. ACS Cent. Sci., 2015, 1(4), 168-180.
[http://dx.doi.org/10.1021/acscentsci.5b00131] [PMID: 27162970]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy