Current Computer-Aided Drug DesignCurrent Computer-Aided Drug DesignCurrent Computer-Aided Drug Design1573-40991875-6697Bentham Science64402210.2174/1573409919666230525151933ChemistryResearch ArticlePotential Aromatase Inhibitors from Centella asiatica with Non-synonymous SNPS - A Computational ApproachTemkarSheshadriinfo@benthamscience.netSridharaAmrutainfo@benthamscience.netMallurDhrithiinfo@benthamscience.netShivaprakashDeepakinfo@benthamscience.netIyengarDivyainfo@benthamscience.netDasNritaminfo@benthamscience.netCBenedictinfo@benthamscience.netDepartment of Biotechnology, Sri Ramachandra Institute of Higher Education and Research0104202420434135807012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://rjeid.com/1573-4099/article/view/644022

Background:Aromatase inhibitors are used in the treatment of breast cancer as they are effective in decreasing the concentration of estrogen. As SNPs impact the efficacy or toxicity of drugs, evaluating them with mutated conformations would help in identifying potential inhibitors. In recent years, phytocompounds have been under scrutiny for their activity as potential inhibitors.

Objective:In this study, we have evaluated Centella asiatica compounds for their activity on aromatase with clinically significant SNPs: rs700519, rs78310315 and rs56658716.

Methods:Using AMDock v.1.5.2, which uses the AutoDock Vina engine, molecular docking simulations were carried out, and the docked complexes were analyzed for their chemical interactions such as polar contacts using PyMol v2.5. The mutated conformations of the protein and force field energy differences were computationally derived using SwissPDB Viewer. PubChem, dbSNP and ClinVar databases were used to retrieve the compounds and SNPs. ADMET prediction profile was generated using admetSAR v1.0.

Results:Docking simulations of the C. asiatica compounds with the native and mutated conformations showed that out of the obtained fourteen phytocompounds, Isoquercetin, Quercetin and 9H-Fluorene-2-carboxylic acid were able to dock with best scores in terms of binding affinity (- 8.4kcal/mol), Estimated Ki (0.6 µM) values and Polar Contacts in both native and mutated conformations (3EQM, 5JKW, 3S7S).

Conclusion:Our computational analyses predict that the deleterious SNPs did not impact the molecular interactions of Isoquercetin, Quercetin and 9H-Fluorene-2-carboxylic acid, providing better lead compounds for further evaluation as potential aromatase inhibitors.

&lti&ampgtCentella asiatica&amplt/i&ampgtaromatase inhibitorsbreast cancermolecular docking simulationssingle nucleotide polymorphismforce field energy profileADMET studies.Shen, L.X.; Basilion, J.P.; Stanton, V.P. Jr Single-nucleotide polymorphisms can cause different structural folds of mRNA. Proc. Natl. Acad. Sci. USA, 1999, 96(14), 7871-7876. doi: 10.1073/pnas.96.14.7871 PMID: 10393914Erichsen, H.C.; Chanock, S.J. SNPs in cancer research and treatment. Br. J. Cancer, 2004, 90(4), 747-751. doi: 10.1038/sj.bjc.6601574 PMID: 14970847Available fromhttps://academic.oup.com/edrv/article/26/3/331/2355224(Accessed on: 2020 NOV 3).Gilmour, R. The international plant names index., 2000, 39-88.Babykutty, S.; Padikkala, J.; Sathiadevan, P.P.; Vijayakurup, V.; Azis, T.K.A.; Srinivas, P.; Gopala, S. Apoptosis induction of Centella asiatica on human breast cancer cells. Afr. J. Tradit. Complement. Altern. Med., 2008, 6(1), 9-16. PMID: 20162036Bharadvaja, N. Centella Asiatica: A pharmaceutically important medicinal plant. Curr. Trends Biomed. Eng. Biosci., 2017, 5(3) doi: 10.19080/CTBEB.2017.05.555661Pittella, F.; Dutra, R.; Junior, D.; Lopes, M.T.; Barbosa, N. Antioxidant and cytotoxic activities of Centella asiatica (L). Urb. Int. J. Mol. Sci., 2009, 10(9), 3713-3721. doi: 10.3390/ijms10093713 PMID: 19865514Hashim, P.; Sidek, H.; Helan, M.; Sabery, A.; Palanisamy, U.D.; Ilham, M. Triterpene composition and bioactivities of Centella asiatica. Molecules, 2011, 16(2), 1310-1322. doi: 10.3390/molecules16021310 PMID: 21278681Available from:https://anunaki.com/wp-content/uploads/2021/06/FUNCTIONAL-PROPERTIES-OF-CENTELLA-ASIATICA-L.-A-REVIEW.pdf(Accessed on: 2023 MAR 27).Guo, J.S.; Cheng, C.L.; Koo, M.W. Inhibitory effects of Centella asiatica water extract and asiaticoside on inducible nitric oxide synthase during gastric ulcer healing in rats. Planta Med., 2004, 70(12), 1150-1154. doi: 10.1055/s-2004-835843 PMID: 15643549Prakash, Ved & Jaiswal, Nishita & Srivastava, MrinalA review on medicinal properties of Centella asiatica. Asian J. Pharm. Clin. Res., 2017, 10, 69-74. Available from: https://www.researchgate.net/publication/319881265_A_review_on_medicinal_properties_of_Centella_asiaticaYoo, K.Y.; Park, S.Y. Terpenoids as potential anti-Alzheimer’s disease therapeutics. Molecules, 2012, 17(3), 3524-3538. doi: 10.3390/molecules17033524 PMID: 22430119Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res., 2021, 49(D1), D1388-D1395. doi: 10.1093/nar/gkaa971 PMID: 33151290Ghosh, D. Crystal structure of human placental aromatase cytochrome P450 in complex with androstenedione., Available from:HTTP://DX.DOI.ORG/10.2210/PDB3EQM/PDBGhosh, D. Crystal structure of human placental aromatase complexed with breast cancer drug exemestane.2012. AVAILABLE FROM: HTTP://DX.DOI.ORG/10.2210/PDB3S7S/PDBGhosh, D.; Egbuta, C.; Lo, J. Human placental aromatase cytochrome P450 (CYP19A1) complexed with testosterone. 2017. Available from:HTTP://DX.DOI.ORG/10.2210/PDB5JKW/PDBLombardi, P. Exemestane, a new steroidal aromatase inhibitor of clinical relevance. Biochim. Biophys. Acta Mol. Basis Dis., 2002, 1587(2-3), 326-337. doi: 10.1016/S0925-4439(02)00096-0 PMID: 12084475Burley, S.K.; Bhikadiya, C.; Bi, C.; Bittrich, S.; Chen, L.; Crichlow, G.V.; Christie, C.H.; Dalenberg, K.; Di Costanzo, L.; Duarte, J.M.; Dutta, S.; Feng, Z.; Ganesan, S.; Goodsell, D.S.; Ghosh, S.; Green, R.K. Guranović, V.; Guzenko, D.; Hudson, B.P.; Lawson, C.L.; Liang, Y.; Lowe, R.; Namkoong, H.; Peisach, E.; Persikova, I.; Randle, C.; Rose, A.; Rose, Y.; Sali, A.; Segura, J.; Sekharan, M.; Shao, C.; Tao, Y.P.; Voigt, M.; Westbrook, J.D.; Young, J.Y.; Zardecki, C.; Zhuravleva, M. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res., 2021, 49(D1), D437-D451. doi: 10.1093/nar/gkaa1038 PMID: 33211854Garcia-Casado, Z.; Guerrero-Zotano, A.; Llombart-Cussac, A.; Calatrava, A.; Fernandez-Serra, A.; Ruiz-Simon, A.; Gavila, J.; Climent, M.A.; Almenar, S.; Cervera-Deval, J.; Campos, J.; Albaladejo, C.V.; Llombart-Bosch, A.; Guillem, V.; Lopez-Guerrero, J.A. A polymorphism at the 3′-UTR region of the aromatase gene defines a subgroup of postmenopausal breast cancer patients with poor response to neoadjuvant letrozole. BMC Cancer, 2010, 10(1), 36. doi: 10.1186/1471-2407-10-36 PMID: 20144226Available from: https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000137064.6National Center For Biotechnology Information CLINVAR., Available from:https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000017816.1(Accessed JULY 18, 2022)Guex, N.; Peitsch, M.C. SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling. Electrophoresis, 1997, 18(15), 2714-2723. doi: 10.1002/elps.1150181505 PMID: 9504803Zhao, H.; Huang, D. Hydrogen bonding penalty upon ligand binding. PLoS One, 2011, 6(6), e19923. doi: 10.1371/journal.pone.0019923 PMID: 21698148Valdés-Tresanco, M.S.; Valdés-Tresanco, M.E.; Valiente, P.A.; Moreno, E. AMDock: A versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4. Biol. Direct, 2020, 15(1), 12. doi: 10.1186/s13062-020-00267-2 PMID: 32938494Prabhavathi, H.; Dasegowda, K.R.; Renukananda, K.H.; Karunakar, P.; Lingaraju, K.; Raja Naika, H. Molecular docking and dynamic simulation to identify potential phytocompound inhibitors for EGFR and HER2 as anti-breast cancer agents. J. Biomol. Struct. Dyn., 2022, 40(10), 4713-4724. doi: 10.1080/07391102.2020.1861982 PMID: 33345701Shen, J.; Cheng, F.; Xu, Y.; Li, W.; Tang, Y. Estimation of ADME properties with substructure pattern recognition. J. Chem. Inf. Model., 2010, 50(6), 1034-1041. doi: 10.1021/ci100104j PMID: 20578727O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox. J. Cheminform., 2011, 3(1), 33. doi: 10.1186/1758-2946-3-33 PMID: 21982300Yung-Chi, C.; Prusoff, W.H. Relationship between the inhibition constant (KI) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem. Pharmacol., 1973, 22(23), 3099-3108. doi: 10.1016/0006-2952(73)90196-2Kenny, P.W. The nature of ligand efficiency. J. Cheminform., 2019, 11(1), 8. doi: 10.1186/s13321-019-0330-2 PMID: 30706294Krovat, E.M.; Steindl, T.; Langer, T. Recent advances in docking and scoring. Curr. Comput. Aided. Drug Des., 2005, 1(1), 93-102. doi: 10.2174/1573409052952314Wang, L.; Ellsworth, K.A.; Moon, I.; Pelleymounter, L.L.; Eckloff, B.W.; Martin, Y.N.; Fridley, B.L.; Jenkins, G.D.; Batzler, A.; Suman, V.J.; Ravi, S.; Dixon, J.M.; Miller, W.R.; Wieben, E.D.; Buzdar, A.; Weinshilboum, R.M.; Ingle, J.N. Functional genetic polymorphisms in the aromatase gene CYP19 vary the response of breast cancer patients to neoadjuvant therapy with aromatase inhibitors. Cancer Res., 2010, 70(1), 319-328. doi: 10.1158/0008-5472.CAN-09-3224 PMID: 20048079Landrum, M.J.; Lee, J.M.; Benson, M.; Brown, G.; Chao, C.; Chitipiralla, S.; Gu, B.; Hart, J.; Hoffman, D.; Hoover, J.; Jang, W.; Katz, K.; Ovetsky, M.; Riley, G.; Sethi, A.; Tully, R.; Villamarin-Salomon, R.; Rubinstein, W.; Maglott, D.R. ClinVar: Public archive of interpretations of clinically relevant variants. Nucleic Acids Res., 2016, 44(D1), D862-D868. doi: 10.1093/nar/gkv1222 PMID: 26582918Böhm, H.J.; Klebe, G. What can we learn from molecular recognition in protein–ligand complexes for the design of new drugs? Angew. Chem. Int. Ed. Engl., 1996, 35(22), 2588-2614. doi: 10.1002/anie.199625881Villar, H.O.; Kauvar, L.M. Amino acid preferences at protein binding sites. FEBS Lett., 1994, 349(1), 125-130. doi: 10.1016/0014-5793(94)00648-2 PMID: 8045288