UNRAVELLING THE INTERACTION BETWEEN GARCINISIDONE-A AND HER2 PROTEIN IN BREAST CANCER: A COMPUTATIONAL STUDY

MAINAL FURQAN; DACHRIYANUS; MERI SUSANTI; PURNAWAN PONTANA PUTRA; FATMA SRI WAHYUNI

doi:10.22159/ijap.2024.v16s1.24

Authors

MAINAL FURQAN Postgraduate Pharmacy Study Programme, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia
DACHRIYANUS Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0000-0002-1857-9001
MERI SUSANTI Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0000-0002-1857-9001
PURNAWAN PONTANA PUTRA Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0000-0002-1857-9001
FATMA SRI WAHYUNI Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0000-0002-1857-9001

DOI:

https://doi.org/10.22159/ijap.2024.v16s1.24

Keywords:

Garcinia cowa, ERBB2, Docking simulation, Molecular dynamics, ADME prediction

Abstract

Objective: One substance found in the leaves of Garcinia cowa Roxb that has anticancer properties is garcinisidone-A. The study aims to simulate the docking of garcinisidone-A (Gar-A), molecular dynamics, and predict the ADME by predicting the binding of the HER2 protein in breast cancer cells and developing new drug candidate options for cancer treatment, often starting with computational analysis.

Methods: The research method involves computational utilization of pkCSM applications, Gar-A docking simulation with the HER2 protein using Gnina software version 1.0.2, and molecular dynamics conducted with GROMACS 2022.2 and CHARMMGUI applications.

Results: Gar-A has a molecular weight of less than 500, a Log P value of greater than 5, a limited amount of water solubility, a low level of skin permeability, good intestinal permeability, and a Convolutional Neural Network (CNN) pose score on the HER2 protein of 0.6178. It also does not readily cross the blood-brain barrier, and total clearance values indicate rapid elimination via other excretory routes or enzyme metabolism. Gar-A is thought to have interactions with HER2. There are hydrogen bond interactions with amino acids Lys753 and Asp863, carbon-hydrogen bonds with amino acids Leu785, Ser783, Thr862, and alkyl bonds with amino acids Leu726, Leu852, and Ile767. The stability of the Gar-A-substrate interaction could have been more evident during 100 ns molecular dynamics simulation.

Conclusion: The physicochemical properties of Gar-A align with Lipinski's rule for drug candidates. ADME predictions indicate good intestinal permeability for Gar-A; however, it suggests it cannot penetrate the blood-brain barrier. The docking results reveal that Gar-A has a value close to one which indicates similar action to its natural ligand and molecular dynamics simulations that Gar-A is less stable. The results illustrate that Gar-A has the potential as a breast anticancer.

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References

Observatory GC. Cancer today. International Agency for Research on Cancer; 2023. Available from: https://gco.fr:iarc/today/fact-sheets-populations. [Last accessed on 09 Sep 2023]

Welcsh PL, Owens KN, King MC. Expression of BRCA1 and BRCA2 cellular levels of BRCA1 and BRCA2 proteins increases as cells progress through G1, peak during the S phase, and remain elevated during the G2-M transition. BRCA1, BRCA2, RAD51, and BARD1 co-localize during the S phase. S G2 M. 2000;16(2):G1.

Kong X, Zhang K, Wang X, Yang X, Li Y, Zhai J. Mechanism of trastuzumab resistance caused by HER-2 mutation in breast carcinomas. Cancer Manag Res. 2019;11:5971-82. doi: 10.2147/CMAR.S194137, PMID 31308740.

Husni E, Nahari F, Wirasti Y, Wahyuni FS, Dachriyanus. Cytotoxicity study of ethanol extract of the stem bark of asam kandis (Garcinia cowa Roxb.) on T47D breast cancer cell line. Asian Pacific Journal of Tropical Biomedicine. 2015;5(3):249-52. doi: 10.1016/S2221-1691(15)30013-7.

Wahyuni FS, Sutma S, Aldi Y. Uji efek sitotoksik ekstrak etanol kulit buah asam kandis (Garcinia cowa Roxb.) terhadap sel kanker payudara T47D dengan metoda MTT (Microtetrazolium). J Sains Teknol Farmasi. 2011;16(2):209-15.

Susanti M, Pratama AR, Suryani MI, Suryati, Dachriyanus. Development and validation of TLC-densitometry method for quantification of tetraprenyltoluquinone in the stem bark hexane extract of Garcinia cowa roxb. Heliyon. 2022;8(9):e10437. doi: 10.1016/j.heliyon.2022.e10437, PMID 36091948.

Wahyuni FS, Febria S, Arisanty D. Apoptosis induction of cervical carcinoma HeLa cells line by dichloromethane fraction of the rinds of Garcinia cowa Roxb. Phcog J. 2017;9(4):475-8. doi: 10.5530/pj.2017.4.76.

Wahyuni FS, Shaari K, Stanislas J, Lajis NH, Hamidi D. Cytotoxic xanthones from the stem bark of Garcinia cowa roxb. J Chem Pharm. 2015;7(1):227-36.

Wahyuni FS, Triastuti DH, Arifin H. Cytotoxicity study of ethanol extract of the leaves of Asam Kandis (Garcinia cowa Roxb.) on T47D breast cancer cell line. Phcog J. 2015;7(6):369-71. doi: 10.5530/pj.2015.6.9.

Jhofi M, Husni E, Hamidi D. Anticancer and antioxidant activity of Asam Kandis (Garcinia cowa Roxb) leaf extract and fraction. Advances in Health Sciences Research. 2nd International Conference on Contemporary Science and Clinical Pharmacy; 2021 Nov 17. p. 214-21. doi: 10.2991/ahsr.k.211105.032.

Wahyuni FS, Shaari K, Stanslas J, Lajis N, Hamidi D. Cytotoxic compounds from the leaves of Garcinia cowa Roxb. J App Pharm Sci. 2015 Feb 27;5(2):6-11. doi: 10.7324/JAPS.2015.50202.

Pyne N, Paul S. Screening of medicinal plants unraveled the leishmanicidal credibility of Garcinia cowa; highlighting norcowanin, a novel anti-leishmanial phytochemical through in-silico study. J Parasit Dis. 2022;46(1):202-14. doi: 10.1007/s12639-021-01441-7, PMID 35299910.

Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773-80. doi: 10.1016/j.drudis.2018.11.014, PMID 30472429.

Zhavoronkov A, Vanhaelen Q, Oprea TI. Will artificial intelligence for drug discovery impact clinical pharmacology? Clin Pharmacol Ther. 2020;107(4):780-5. doi: 10.1002/cpt.1795, PMID 31957003.

Talele TT, Khedkar SA, Rigby AC. Successful applications of computer-aided drug discovery: moving drugs from concept to the clinic. Curr Top Med Chem. 2010;10(1):127-41. doi: 10.2174/156802610790232251, PMID 19929824.

Sliwoski GR, Meiler J, Lowe EW. Computational methods in drug discovery prediction of protein structure and ensembles from limited experimental data View project antibody modelling, antibody design, and antigen-antibody interactions view project. Comp Methods Drug Discov. 2014;66(1):334-95.

An J, Lee DCW, Law AHY, Yang CLH, Poon LLM, Lau ASY. A novel small-molecule inhibitor of the avian influenza H5N1 virus determined through computational screening against the neuraminidase. J Med Chem. 2009;52(9):2667-72. doi: 10.1021/jm800455g, PMID 19419201.

Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58(9):4066-72. doi: 10.1021/acs.jmedchem.5b00104, PMID 25860834.

Zhang ZM, Liu S, Lin K, Luo Y, Perry JJ, Wang Y. Crystal structure of human DNA methyltransferase 1. J Mol Biol. 2015;427(15):2520-31. doi: 10.1016/j.jmb.2015.06.001, PMID 26070743.

McNutt AT, Francoeur P, Aggarwal R, Masuda T, Meli R, Ragoza M. GNINA 1.0: molecular docking with deep learning. J Cheminform. 2021;13(1):43. doi: 10.1186/s13321-021-00522-2, PMID 34108002.

Bannwarth C, Ehlert S, Grimme S. GFN2-xTB-an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J Chem Theory Comput. 2019;15(3):1652-71. doi: 10.1021/acs.jctc.8b01176, PMID 30741547.

Abraham MJ, Murtola T, Schulz R, Pall S, Smith JC, Hess B. Gromacs: high-performance molecular simulations through multi-level parallelism from laptops to supercomputers. Software X. 2015;1-2:19-25. doi: 10.1016/j.softx.2015.06.001.

Gao Y, Lee J, Smith IPS, Lee H, Kim S, Qi Y. Charmm-gui supports hydrogen mass repartitioning and different protonation states of phosphates in lipopolysaccharides. J Chem Inf Model. 2021;61(2):831-9. doi: 10.1021/acs.jcim.0c01360, PMID 33442985.

Tian C, Kasavajhala K, Belfon KAA, Raguette L, Huang H, Migues AN. Ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J Chem Theory Comput. 2020;16(1):528-52. doi: 10.1021/acs.jctc.9b00591, PMID 31714766.

Sousa Da Silva AW, Vranken WF, ACPYPE-Ante Chamber P. Ython parser interface. BMC Res Notes. 2012;5:1-8.

Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. 20035_Ftp. J Comp Chem. 2004;56531(9):1157-74.

Lee J, Hitzenberger M, Rieger M, Kern NR, Zacharias M, Im W. CHARMM-GUI supports the Amber force fields. J Chem Phys. 2020;153(3):035103. doi: 10.1063/5.0012280, PMID 32716185.

Mark P, Nilsson L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A. 2001;105(43):9954-60. doi: 10.1021/jp003020w.

Darden T, York D, Pedersen L. Particle mesh Ewald: an N⋅ log(N) method for Ewald sums in large systems. J Chem Phys. 1993;98(12):10089-92. doi: 10.1063/1.464397.

Parrinello M, Rahman A. Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys. 1981;52(12):7182-90. doi: 10.1063/1.328693.

Rahim F, Putra PP, Ismed F, Putra AE, Lucida H, Molecular Dynamics. Docking and prediction of absorption, distribution, metabolism, and excretion of lycopene as protein inhibitor of Bcl2 and DNMT1. Trop J Nat Prod Res. 2023;7(7):3439-44.

Lipinski CA. Lead and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1(4):337-41. doi: 10.1016/j.ddtec.2004.11.007, PMID 24981612.

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3-26. doi: 10.1016/s0169-409x(00)00129-0, PMID 11259830.

Pajouhesh H, Lenz GR. Medicinal chemical properties of successful central nervous system drugs. Neurorx. 2005;2(4):541-53. doi: 10.1602/neurorx.2.4.541, PMID 16489364.

Dahlgren D, Lennernas H. Intestinal permeability and drug absorption: predictive experimental, computational and in vivo approaches. Pharmaceutics. 2019;11(8). doi: 10.3390/pharmaceutics11080411, PMID 31412551.

Agustina DW, Wahyuningsih MD, Widyarti S. Rifa’i M. Molecular docking study to reveal Morinda citrifolia fruits as a novel EGFR inhibitor for anticancer therapy. IOP Conf S Earth Environ Sci. 2021;743(1).

Bayat Z, Movaffagh J, Noruzi S. Development of a computational approach to predict blood-brain permeability on anti-viral nucleoside analogues. Russ J Phys Chem. 2011;85(11):1923-30. doi: 10.1134/S0036024411110021.

Teo YL, Ho HK, Chan A. Metabolism-related pharmacokinetic drug-drug interactions with tyrosine kinase inhibitors: current understanding, challenges and recommendations. Br J Clin Pharmacol. 2015 Feb 1;79(2):241-53. doi: 10.1111/bcp.12496, PMID 25125025.

Halimi M, Bararpour P. Natural inhibitors of SARS-CoV-2 main protease: structure based pharmacophore modeling, molecular docking and molecular dynamic simulation studies. J Mol Model. 2022;28(9):279. doi: 10.1007/s00894-022-05286-6, PMID 36031629.

Pieroni M, Madeddu F, Di Martino J, Arcieri M, Parisi V, Bottoni P. MDligand–receptor: a high-performance computing tool for characterizing ligand-receptor binding interactions in molecular dynamics trajectories. Int J Mol Sci. 2023 Jul 1;24(14). doi: 10.3390/ijms241411671, PMID 37511429.