Virtual Screening of potential inhibitors from Herbs for the treatment of Breast Cancer


  • Faizy Khan
  • Shantanu Bafna
  • Tanu Gupta
  • Arnold Emerson Isaac School of Bio Sciences and Technology, VIT University, Vellore-632014



Objectives: Cancer is a disease which results in uncontrollable abnormal cells division and destruction of body tissues. Breast cancer occurs when malignant tumors develop in the breast. Breast cancer is the second leading cause of death among women. To study the role of herbs used in the treatment for breast cancer. To investigate the anti-breast cancer activity of compounds present on most common herbs and to analyse their interaction with amino acids in the active sites.

Methods: Complementary and alternative medicine is often used for curing cancer mainly the breast cancer. Also certain studies support the benefits of herbal medicines over others among Complementary and alternative medicine. Herbal treatments are more popular due to less complications and more safety. We selected a dataset of 38 compounds and performed virtual screening to identify the potential inhibitor against the known protein target BRCA1 involved in breast cancer using AutoDock4 as docking software. The binding site analyses were carried out using Discovery studio.

Results: From our study, we deduced that cimigenol (black cohosh) and glycyrrhetinic acid (licorice) were found to have the highest affinity with the target protein. The amino acid interactions with the top five compounds were also analysed.

Conclusion: During the course of our research we explored over common herbs used globally in treatment for breast cancer. Virtual screening was performed using AutoDock to search ligands to identify those structures which are most likely to bind to the protein. The high affinity compounds can bind more efficiently to the BRCA1 receptor and, hence, has potential to emerge as lead compound in the treatment of breast cancer.

Keywords: Protein, Ligands, AutoDock, Virtual screening, Visualization, BRCA1.


Ford D, Easton DF, Bishop DT, Narod SA, Goldgar DE. Risks of cancer in BRCA1-mutation carriers. Breast Cancer Linkage Consortium. Lancet 1994;343(8899):692-5.

Antoniou AC, Spurdle AB, Sinilnikova OM, Healey S, Pooley KA, Schmutzler RK, et al. Common breast cancer-predisposition alleles are associated with breast cancer risk in BRCA1 and BRCA2 mutation carriers. Am J Hum Genet 2008;82(4):937-48.

Schneider G, Bohm HJ. Virtual screening and fast automated docking methods. Drug Discov Today 2002;7(1):64-70.

Walters WP, Stahl MT, Murcko MA. Virtual screening - An overview. Drug Discov Today 1998;3(4):160-78.

Bernstein FC, Koetzle TF, Williams GJ, Meyer EF Jr, Brice MD, Rodgers JR, et al. The Protein Data Bank. A computer-based archival file for macromolecular structures. Eur J Biochem 1977;80(2):319-24.

Lyne PD. Structure-based virtual screening: An overview. Drug Discov Today 2002;7(20):1047-55.

Olsak M, Filipovic J, Prokop M. FastGrid--The accelerated AutoGrid potential maps generation for molecular docking. Comput Inform 2012;29(6):1325-36.

Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: Integrating information about genes, proteins and diseases. Trends Genet 1997;13(4):163.

Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K, et al. The protein data bank. Acta Crystallogr D Biol Crystallogr 2002;58(6):899-907.

Available from: http://www.

Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, et al. PubChem’s BioAssay Database. Nucleic Acids Res 2012;40:D400-12.

O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011;3:33.

Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS, Olson AJ. Virtual screening with AutoDock: Theory and practice. Expert Opin Drug Discov 2010;5(6):597-607.

Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31(2):455-61.

Park H, Lee J, Lee S. Critical assessment of the automated AutoDock as a new docking tool for virtual screening. Proteins Struct Funct Bioinform 2006;65(3):549-54.

Morris GM, Huey R, Olson AJ. Using AutoDock for ligand-receptor docking. Curr Protoc Bioinformatics 2008.

Ghosh S, Nie A, An J, Huang Z. Structure-based virtual screening of chemical libraries for drug discovery. Curr Opin Chem Biol 2006;10(3):194-202.

Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov 2004;3(11):935-49.

Studio D. Version 3.1. San Diego, CA: Accelrys; 2011.

Sakurai N, Kozuka M, Tokuda H, Mukainaka T, Enjo F, Nishino H, et al. Cancer preventive agents. Part 1: Chemopreventive potential of cimigenol, cimigenol-3, 15-dione, and related compounds. Bioorg Med Chem 2005;13(4):1403-8.

Wang XF, Zhou QM, Lu YY, Zhang H, Huang S, Su SB. Glycyrrhetinic acid potently suppresses breast cancer invasion and metastasis by impairing the p38 MAPK-AP1 signaling axis. Expert Opin Ther Targets 2015;19(5):577-87.

Jiang F, Li Y, Mu J, Hu C, Zhou M, Wang X, et al. Glabridin inhibits cancer stem like properties of human breast cancer cells: An epigenetic regulation of 148a/SMAd2 signaling. Mol Carcinog 2016;55(5):929-40.

O’Hara M, Kiefer D, Farrell K, Kemper K. A review of 12 commonly used medicinal herbs. Arch Fam Med 1998;7(6):523-36.

Jia L, Zhao Y, Liang XJ. Current evaluation of the millennium phytomedicine - Ginseng (II): Collected chemical entities, modern pharmacology, and clinical applications emanated from traditional



How to Cite

Khan, F., S. Bafna, T. Gupta, and A. E. Isaac. “Virtual Screening of Potential Inhibitors from Herbs for the Treatment of Breast Cancer”. Asian Journal of Pharmaceutical and Clinical Research, vol. 10, no. 4, Apr. 2017, pp. 62-67, doi:10.22159/ajpcr.2017.v10i4.14959.



Original Article(s)