• Fauzan Zein Mutaqqin Fakultas Farmasi, Universitas Bakti Kencana
  • Wayan Ayu Puje Astuti Fakultas Farmasi, Universitas Bakti Kencana
  • La Ode Aman Departemen Farmasi, Universitas Negeri Gorontalo
  • Ellin Febrina Fakultas Farmasi, Universitas Padjadjaran
  • Aiyi Asnawi Sekolah Farmasi, Institut Teknologi Bandung
Keywords: Bruton’s tyrosine kinase, BTK inhibitors, Docking, Pharmacophore, Virtual screening


Bruton’s tyrosine Kinase (BTK) plays a critical role in many cellular signalling pathways making it a potential target to treat autoimmune diseases and cancer. In this study, we have implemented structure-based virtual screening against natural product ZINC database by using pharmacopore model followed by molecular docking to identified the inhibitor of BTK (PDB ID 6E4F). By using structure based pharmacophore, a four-point pharmacophore hypothesis was derived, with three hydropobic, one aromatic rings, four hydrogen bond acceptor and nine hydrogen bond donor. Screening of 12 natural product ZINC databases (151,837 compounds) against pharmacophore returned 1,345 hits with matching chemical features of 58.81. Docking these hits against the ATP-binding site of the BTK kinase domain through a virtual screening docking-based by using vina wizard and autodock wizard (PyRx 8.0) returned 148 and 75 hits, respectively. Three hit compounds with high affinity towards BTK were identified, and it could be used as a potent lead molecule for designing BTK inhibitor.


Adrià, C.M., Garcia-Vallvé, S. and Pujadas, G., 2012. DecoyFinder, a tool for finding decoy molecules. Journal of cheminformatics, 4(S1), p.P2.


Berman, H.M., Bhat, T.N., Bourne, P.E., Feng, Z., Gilliland, G., Weissig, H. and Westbrook, J., 2000. The Protein Data Bank and the challenge of structural genomics. Nature Structural & Molecular Biology, 7(11s), p.957.

Braga, R.C. and Andrade, C.H., 2013. Assessing the performance of 3D pharmacophore models in virtual screening: how good are they?. Current topics in medicinal chemistry, 13(9), pp.1127-1138.

Chasani, M., Iswanto, P., Vaulina, E., Putra, W.S. and Hanafi, M., 2011. SEMI SINTESIS SENYAWA 2, 4, 6-TRINITROFENILHIDRAZON KALANON DAN UJI AKTIVITAS TERHADAP SEL LEUKIMIA L1210. Molekul, 6(2), pp.66-73.

Chiron, D., Di Liberto, M., Martin, P., Huang, X., Sharman, J., Blecua, P., Mathew, S., Vijay, P., Eng, K., Ali, S. and Johnson, A., 2014. Cell-cycle reprogramming for PI3K inhibition overrides a relapse-specific C481S BTK mutation revealed by longitudinal functional genomics in mantle cell lymphoma. Cancer discovery, 4(9), pp.1022-1035.

Choi, M.Y. and Kipps, T.J., 2012. Inhibitors of B-cell receptor signaling for patients with B-cell malignancies. Cancer journal (Sudbury, Mass.), 18(5), p.404.

Dallakyan, S. and Olson, A.J., 2015. Small-molecule library screening by docking with PyRx. In Chemical biology (pp. 243-250). Humana Press, New York, NY.

Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F., Bellis, L. and Overington, J.P., 2015. ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic acids research, 43(W1), pp.W612-W620.

Empereur-Mot, C., Zagury, J.F. and Montes, M., 2016. Screening explorer–An interactive tool for the analysis of screening results. Journal of chemical information and modeling, 56(12), pp.2281-2286.

Hevener, K.E., Zhao, W., Ball, D.M., Babaoglu, K., Qi, J., White, S.W. and Lee, R.E., 2009. Validation of molecular docking programs for virtual screening against dihydropteroate synthase. Journal of chemical information and modeling, 49(2), pp.444-460.

Honigberg, L.A., Smith, A.M., Sirisawad, M., Verner, E., Loury, D., Chang, B., Li, S., Pan, Z., Thamm, D.H., Miller, R.A. and Buggy, J.J., 2010. The Bruton tyrosine kinase inhibitor PCI-32765 blocks B-cell activation and is efficacious in models of autoimmune disease and B-cell malignancy. Proceedings of the National Academy of Sciences, 107(29), pp.13075-13080.

Melville, J.L. and Hirst, J.D., 2007. TMACC: Interpretable Correlation Descriptors for Quantitative Structure− Activity Relationships. Journal of chemical information and modeling, 47(2), pp.626-634.

Muchtaridi, M., Syahidah, H., Subarnas, A., Yusuf, M., Bryant, S. and Langer, T., 2017. Molecular docking and 3D-pharmacophore modeling to study the interactions of chalcone derivatives with estrogen receptor alpha. Pharmaceuticals, 10(4), p.81.

Pan, Z., Scheerens, H., Li, S.J., Schultz, B.E., Sprengeler, P.A., Burrill, L.C., Mendonca, R.V., Sweeney, M.D., Scott, K.C., Grothaus, P.G. and Jeffery, D.A., 2007. Discovery of selective irreversible inhibitors for Bruton’s tyrosine kinase. ChemMedChem: Chemistry Enabling Drug Discovery, 2(1), pp.58-61.

Papadatos, G. and Overington, J.P., 2014. The ChEMBL database: a taster for medicinal chemists. Future medicinal chemistry, 6(4), pp.361-364.

Reiff, S.D., Mantel, R., Smith, L.L., McWhorter, S., Goettl, V.M., Johnson, A.J., Eathiraj, S., Abbadessa, G., Schwartz, B., Byrd, J.C. and Woyach, J.A., 2016. The Bruton's tyrosine kinase (BTK) inhibitor ARQ 531 effectively inhibits wild type and C481S mutant BTK and is superior to ibrutinib in a mouse model of chronic lymphocytic leukemia.

Sakthivel, S. and Habeeb, S.K.M., 2018. Combined pharmacophore, virtual screening and molecular dynamics studies to identify Bruton’s tyrosine kinase inhibitors. Journal of Biomolecular Structure and Dynamics, 36(16), pp.4320-4337.

Saputri, K.E., Fakhmi, N., Kusumaningtyas, E., Priyatama, D. and Santoso, B., 2016. Docking Molekular Potensi Anti Diabetes Melitus Tipe 2 Turunan Zerumbon Sebagai Inhibitor Aldosa Reduktase Dengan Autodock-Vina. Chimica et Natura Acta, 4(1), pp.16-20.

Seidel, T., Ibis, G., Bendix, F. and Wolber, G., 2010. Strategies for 3D pharmacophore-based virtual screening. Drug Discovery Today: Technologies, 7(4), pp.e221-e228.

Spowage, B.M., Bruce, C.L. and Hirst, J.D., 2009. Interpretable correlation descriptors for quantitative structure-activity relationships. Journal of cheminformatics, 1(1), p.22.

Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet‐Tieulent, J. and Jemal, A., 2015. Global cancer statistics, 2012. CA: a cancer journal for clinicians, 65(2), pp.87-108.

Wolber, G. and Langer, T., 2005. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of chemical information and modeling, 45(1), pp.160-169.

Woyach, J.A., Furman, R.R., Liu, T.M., Ozer, H.G., Zapatka, M., Ruppert, A.S., Xue, L., Li, D.H.H., Steggerda, S.M., Versele, M. and Dave, S.S., 2014. Resistance mechanisms for the Bruton's tyrosine kinase inhibitor ibrutinib. New England Journal of Medicine, 370(24), pp.2286-2294.

Yang, S.Y., 2010. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug discovery today, 15(11-12), pp.444-450.

How to Cite
Mutaqqin, F., Astuti, W., Aman, L. O., Febrina, E., & Asnawi, A. (2019). PENAPISAN VIRTUAL BERBASIS STRUKTUR DARI DATABASE BAHAN ALAM ZINC SEBAGAI INHIBITOR BRUTON TYROSINE KINASE. Jurnal Ilmiah Ibnu Sina, 4(2), 400-409.