Pharmacophore and Molecular Docking-Based Virtual Screening of B-Cell Lymphoma 2 (BCL 2) Inhibitor from Zinc Natural Database as Anti-Small Cell Lung Cancer

  • Fauzan Zein Muttaqin Bandung School of Pharmacy, Bandung, West Java, 40161, Indonesia
  • Dina Kharisma Bandung School of Pharmacy, Bandung, West Java, 40161, Indonesia
  • Aiyi Asnawi School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, 40132, Indonesia
  • Fransiska Kurniawan School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, 40132, Indonesia

Abstract

Cancer is a disease involving genetic factors in its pathogenesis. The increase of cell survival as a result of genetic changes, which prevent apoptosis such as Bcl2 (B-cell lymphoma-2) activation, will cause the tumor to grow. The overexpression of Bcl2 in small cell lung cancer should be inhibited. This study aims to screen natural products that can inhibit Bcl2 overexpression in lung cancer using pharmacophore- and molecular docking-based virtual screening to ZINC Natural Product database. The validation of pharmacophore-based virtual screening to the three features of the pharmacophore model (2 hydrophobic interactions and 1 hydrogen bond donor) showed that the AUC, EF, Se, Sp, ACC, and GH values were 0.57, 3.8, 0.101, 0.957, 0.936, and 0.149, respectively. On the other hand, the validation of molecular docking-based virtual screening showed that the RMSD values of Vina Wizard and AutoDock Wizard were 1.3Å and 1.9Å, respectively. The pharmacophore model virtual screening first obtained 6,615 compounds, and then the molecular docking-based virtual screening finally gained 255 compounds whose values of ΔG and Ki were lower than those of the native ligand. It was concluded that the virtual screening could yield as many as 255 potential anti-lung cancer drug candidates.


Keywords: B-cell lymphoma 2 inhibitors, molecular docking, pharmacophore modeling, virtual screening

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Author Biographies

Fauzan Zein Muttaqin, Bandung School of Pharmacy, Bandung, West Java, 40161, Indonesia

Bandung School of Pharmacy, Bandung, West Java, 40161, Indonesia

Dina Kharisma, Bandung School of Pharmacy, Bandung, West Java, 40161, Indonesia

Bandung School of Pharmacy, Bandung, West Java, 40161, Indonesia

Aiyi Asnawi, School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, 40132, Indonesia

School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, 40132, Indonesia

Fransiska Kurniawan, School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, 40132, Indonesia

School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, 40132, Indonesia

References

1. Engelberts PJ, Voorhorst M, Schuurman J, van Meerten T, Bakker JM, Vink T, et al., Type I CD20 Antibodies Recruit the B Cell Receptor for Complement-Dependent Lysis of Malignant B Cells, J Immunol, 2016, December 15, 2016, 197 (12): 4829-4837.
2. WHO, Media Center: Fact Sheet Cancer, 2017. Available in http://www.who.int/mediacentre/factsheets/fs297/en/ accessed on Oct 15, 2017
3. Ministry of Health of the Republic of Indonesia, Hasil Riskesdas 2013 [Results of Basic Health Research of 2013], 2013: 85-87. Accessed on October 14, 2017
4. Data and Information Center, Ministry of Health of the Republic of Indonesia, 2015. Available at: http://www.pusdatin.kemkes.go.id/folder/view/01/structure-publikasi-pusdatin-profil-kesehatan.html [accessed 02 July 2017]
5. Semenova EA, Nagel R, Berns A, Origins, genetic landscape, and emerging therapies of small cell lung cancer, Cold Spring Harbor Laboratory Press. Genes & Development, 2015, 29: 1447–1462.
6. Muttaqin FZ, Fakih TM, Muhammad HN, Molecular Docking, Molecular Dynamics, And In Silico Toxicity Prediction Studies Of Coumarin, N-Oxalylglycine, Organoselenium, Organosulfur, And Pyridine Derivatives As Histone Lysine Demethylase Inhibitors, Asian Journal of Pharmaceutical and Clinical Research, 2017, 10(12): 212-215.
7. Yadav M, Gurmith S, Virtual Screening of Ligand molecules for target protein CYP26A1 by using AutoDock-Vina, IJIRSET, 2013, 2(9): 4917.
8. Braga RC, Andrade CH, Assessing the performance of 3D pharmacophore models in virtual screening: how good are they?, 2013, 13(9):1127-38.
9. Zhou H, Chen J, Meagher JL, Yang CY, Aguilar A, Liu L., et al., Design of Bcl-2 and Bcl-xL Inhibitors with Subnanomolar Binding Affinities Based upon a New Scaffold. J.Med.Chem., 2012, 55: 4664-4682.
10. Adrià CM, Garcia-Vallvé S, Pujadas G, DecoyFinder, a tool for finding decoy molecules, J Cheminform. 2012, 4(1): 2.
11. Wolber G, Langer T, LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters, J Chem Inf Model, 2005, 45(1): 160–169.
12. Langer T, Hoffmann RD, Pharmacophores and Pharmacophore Searches; 1st ed.; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, FRG, 2006, 395.
13. Triballeau N, Acher F, Brabet I, Pin JP, Bertrand HO, Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4, J. Med. Chem., 2005, 48: 2534-47.
14. Gao H, Williams C, Labute P, Bajorath J, Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands. J. Chem. Inf. Comput. Sci., 1999, 39: 164-8.
15. Jacobsson M, Lidén P, Stjernschantz E, Boström H, Norinder U, Improving structure-based virtual screening by multivariate analysis of scoring data, J. Med. Chem., 2003, 46: 5781-9.
16. Shepherd AJ, Gorse D, Thornton JM, Prediction of the location and type of beta-turns in proteins using neural networks, Protein Sci., 1999, 8: 1045-55.
17. Guner OF, Pharmacophore Perception, Development, and Use in Drug Design, 1st ed.; Intl Univ Line: La Jolla, 2000, 560.
18. Kurogi Y, Guner OF, Pharmacophore modeling and three-dimensional database searching for drug design using catalyst, Curr. Med. Chem., 2001, 8: 1035-55.
19. Dallakyan S, Olson AJ, Small-Molecule Library Screening by Docking with PyRx. Methods Mol Biol., 2015, 1263:243-50
20. Morris MG, Ruth H, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al., AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility, J. Comput. Chem., 2009, 30: 2785-2791.
21. Sing T, Sander O, Beerenwinkel N, Lengauer T, ROCR: visualizing classifier performance in R. Bioinformatics, 2005, 21(20):3940-3941.
22. Yanuar A, Syahdi RR, Aryati WD, Parameter Optimization and Virtual Screening Indonesian Herbal Database as Human Immunodeficiency Virus -1 Integrase Inhibitor Using Autodock And Vina, International Journal of Applied Pharmaceutics, 2017, 9(Special Issue): 90-93.
23. Sotriffer C, Virtual Screening: Principles, Challenges, and Practical Guidelines, Wiley-Vch Verlag & Co. KgaA, Weinheim, Germany, 2011, 115-119.
24. Lyne PD, Structure-based virtual screening: an overview, DDT, 2002, 7(20): 202.
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Muttaqin FZ, Kharisma D, Asnawi A, Kurniawan F. Pharmacophore and Molecular Docking-Based Virtual Screening of B-Cell Lymphoma 2 (BCL 2) Inhibitor from Zinc Natural Database as Anti-Small Cell Lung Cancer. JDDT [Internet]. 15Mar.2020 [cited 8Jul.2020];10(2):143-7. Available from: http://jddtonline.info/index.php/jddt/article/view/3923