Molecular modeling on the identification of potential Janus Kinase 3 (JAK3) inhibitor based on the Indonesian Medicinal Plant Database

Muhammad Arba; Sanang Nur Safitri; Andry Nur Hidayat; Arry Yanuar; Muhammad Sulaiman Zubair; Asmiyenti Djaliasrin Djalil; Daryono Hadi Tjahjono

doi:10.5614/j.math.fund.sci.2020.52.3.2

Authors

Muhammad Arba Faculty of Pharmacy, Universitas Halu Oleo, Kendari 93231
Sanang Nur Safitri Faculty of Pharmacy, Universitas Halu Oleo, Kendari 93231
Andry Nur Hidayat Faculty of Pharmacy, Universitas Halu Oleo, Kendari 93231
Arry Yanuar Faculty of Pharmacy, Universitas Indonesia, Depok 16424
Muhammad Sulaiman Zubair Department of Pharmacy, Tadulako University, Palu
Asmiyenti Djaliasrin Djalil Faculty of Pharmacy, Universitas Muhammadiyah Purwokerto, Purwokerto
Daryono Hadi Tjahjono 5School of Pharmacy, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132

DOI:

https://doi.org/10.5614/j.math.fund.sci.2020.52.3.2

Keywords:

Janus kinase, MM-PBSA, molecular dynamics simulation, pharmacophore modeling, virtual screening

Abstract

The Janus tyrosine kinases (JAKs) have shown great promise as therapeutic protein targets in the treatment of cancer and inflammation diseases. This study used pharmacophore modeling to identify potential inhibitors of Janus kinase 3 (JAK3). A pharmacophore model was developed based on a known JAK3 inhibitor (1NX) and was employed to search for potential JAK3 inhibitors against Indonesian herbal compounds. Among 28 hit molecules that were identified and subjected to a molecular docking protocol against JAK3, the three compounds that had the highest affinities toward JAK3 were camelliaside B, 3-O-galloylepicatechin-(4beta-6)-epicatechin-3-O-gallate, and mesuaferrone B. These were then each subjected to a 50-ns molecular dynamics (MD) simulation. Analysis of RMSD and RMSF values indicated that the three compounds reached stability during the MD simulation. Interestingly, all three compounds had lower binding energies than 1NX against JAK3, as predicted by the MM-PBSA binding energy calculation.

References

O'Shea, J.J. & Gadina, M., Selective Janus Kinase Inhibitors Come of Age, Nat Rev Rheumatol, 15(2), pp. 74-75, 2019.

Wang, Y., Huang, W., Xin, M., Chen, P., Gui, L., Zhao, X., Zhu, X., Luo, H., Cong, X., Wang, J. & Liu, F., Discovery of Potent Anti-inflammatory 4-(4,5,6,7-tetrahydrofuro[3,2-c]pyridin-2-yl) pyrimidin-2-amines for Use as Janus Kinase Inhibitors, Bioorg. Med. Chem., 27(12), pp. 2592-2597, 2019.

Yu, R-N., Chen, C-J., Shu, L., Yin, Y., Wang, Z-J., Zhang, T-T., & Zhang, D-Y., Structure-based Design and Synthesis of Pyrimidine-4,6-diamine Derivatives as Janus Kinase 3 Inhibitors, Bioorg. Med. Chem., 27(8), pp. 1646-1657, 2019.

Yin, Y., Chen, C-J., Yu, R-N., Wang, Z-J., Zhang, T-T. & Zhang, D-Y., Structure-based Design and Synthesis of 1H-pyrazolo[3,4-d]pyrimidin-4-amino Derivatives as Janus Kinase 3 Inhibitors, Bioorg. Med. Chem., 26 (17), pp. 4774-4786, 2018.

Wu, W. & Sun, X-H., Janus Kinase 3: The Controller and The Controlled, Acta Biochim. Biophys. Sin. (Shanghai), 44(3), pp. 187-196, 2011.

Shi, L., Zhong, Z., Li, X., Zhou, Y. & Pan, Z., Discovery of an Orally Available Janus Kinase 3 Selective Covalent Inhibitor, J. Med. Chem., 62, pp. 1054-1066, 2019.

Su, D., Gao, Y., Deng, Y., Zhang, H., Wu, Y., Hu, Y. & Mei, Q., Identification of Chinese Herbal Compounds with Potential as JAK3 Inhibitors, Evid. Based Complement Alternat. Med., 2019, pp. 1-11, 2019.

Tan, L., Akahane, K., McNally, R., Reyskens, K.M.S.E., Ficarro, S.B., Liu, S., Herter-Sprie, G.S., Koyama, S., Pattison, M.J., Labella, K., Johannessen, L., Akbay, E. A., Wong, K-K., Frank, D.A., Marto, J.A., Look, T.A., Arthur, J.S.C., Eck, M.J. & Gray, N.S., Development of Selective Covalent Janus Kinase 3 Inhibitors, J. Med. Chem., 58 (16), pp. 6589-6606, 2015.

Norman, P., Highly Selective Janus Kinase 3 Inhibitors based on a Pyrrolo[2,3-d]pyrimidine Scaffold: Evaluation of WO2013085802, Expert Opin. Ther. Pat., 24 (1), pp. 121-125, 2014.

Jasuja, H., Chadha, N., Singh, P.K., Kaur, M., Bahia, M.S. & Silakari, O., Putative Dual Inhibitors of Janus Kinase 1 and 3 (JAK1/3): Pharmacophore Based Hierarchical Virtual Screening, Comput. Biol. Chem., 76, pp. 109-117, 2018.

Tan, L., Akahane, K., McNally, R., Reyskens, K.M.S.E., Ficarro, S.B., Liu, S., Herter-Sprie, G.S., Koyama, S., Pattison, M.J., Labella, K., Johannessen, L., Akbay, E. A., Wong, K.K., Frank, D.A., Marto, J.A., Look, T.A., Arthur, J.S.C., Eck, M.J. & Gray, N.S., Development of Selective Covalent Janus Kinase 3 Inhibitors, J. Med. Chem., 58 (16), pp. 6589-6606, 2015.

Yanuar, A., Mun'im, A., Lagho, A.B.A., Syahdi, R.R., Rahmat, M. & Suhartanto, H., Medicinal Plants Database and Three Dimensional Structure of the Chemical Compounds from Medicinal Plants in Indonesia, Int. J. Comput. Sci., 8(5), pp. 180-183, 2011.

Jaime-Figueroa, S., De Vicente, J., Hermann, J., Jahangir, A., Jin, S., Kuglstatter, A., Lynch, S. M., Menke, J., Niu, L., Patel, V., Shao, A., Soth, M., Vu, M.D. & Yee, C., Discovery of a Series of Novel 5H-Pyrrolo[2,3-B]Pyrazine-2-Phenyl Ethers, as Potent JAK3 Kinase Inhibitors, Bioorganic Med. Chem. Lett., 23(9), pp. 2522-2526, 2013.

Gilson, M.K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L. & Chong, J., BindingDB in 2015: A Public Database for Medicinal Chemistry, Computational Chemistry and Systems Pharmacology, Nucleic Acids Res., 44, pp. 1045-1053, 2016.

Mysinger, M.M., Carchia, M., Irwin, J.J. & Shoichet, B. K., Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking, J. Med. Chem., 55(14), pp. 6582-6594, 2012.

Wolber, G. & Langer, T., LigandScout:" 3-D Pharmacophores Derived from Protein-Bound Ligands and their Use as Virtual Screening Filters, J. Chem. Inf. Model, 45(1), pp. 160-169, 2005.

Li, H., Leung, K. & Wong, M., Idock: A Multithreaded Virtual Screening Tool for Flexible Ligand Docking, 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 77-84, 2012.

Case, D.A., Cheatham, T.E., Darden, T., Gohlke, H., Luo, R., Merz, K.M., Onufriev, A., Simmerling, C., Wang, B. & Woods, R.J., The AMBER Biomolecular Simulation Programs, J. Comput. Chem., 26(16), pp. 1668-1688, 2005.

Salomon-Ferrer, R., Gtz, A. W., Poole, D., Le Grand, S. & Walker, R.C., Routine Microsecond Molecular Dynamics Simulations with AMBER on Gpus. 2. Explicit Solvent Particle Mesh Ewald, J. Chem. Theory Comput., 9(9), pp. 3878-3888, 2013.

Arba, M., Ruslin, Kalsum, W.U., Alroem, A., Muzakkar, M.Z., Usman, I. & Tjahjono, D.H., QSAR, Molecular Docking and Dynamics Studies of Quinazoline Derivatives as Inhibitor of Phosphatidylinositol 3-Kinase, J. Appl. Pharm. Sci., 8(5), pp. 1-9, 2018.

Arba, M. & Nur-Hidayat, A., Surantaadmaja, S.I. & Tjahjono, D.H., Pharmacophore-Based Virtual Screening for Identifying '5 Subunit Inhibitor of 20S Proteasome, Comput. Biol. Chem., 77, pp. 64-71, 2018.

Maier, J.A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K.E. & Simmerling, C., ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB, J. Chem. Theory Comput., 11(8), pp. 3696-3713, 2015.

Wang, J.M., Wolf, R.M., Caldwell, J.W., Kollman, P.A. & Case, D.A, Development and Testing of a General Amber Force Field, J. Comput. Chem., 25(9), pp. 1157-1174, 2004.

Jakalian, A., Jack, D.B. & Bayly, C.I., Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: II. Parameterization and Validation, J. Comput. Chem., 23(16), pp. 1623-1641, 2002.

Ryckaert, J.P., Ciccotti, G. & Berendsen, H.J.C., Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of N-Alkanes, J. Comput. Phys., 23(3), pp. 327-341, 1977.

Darden, T., York, D. & Pedersen, L., Particle mesh Ewald: An N"log(N) Method for Ewald Sums in Large Systems, J. Chem. Phys., 98(12), pp. 10089-10092, 1993.

Roe, D.R. & Cheatham III, T.E., PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Synamics Trajectory Data, J. Chem. Theory Com., 9(7), pp. 3084-3095, 2013.

Humphrey, W., Dalke, A. & Schulten, K., VMD: Visual Molecular Dynamics, J. Mol. Graph., 14(1), pp. 33-38, 1996.

Kollman, P.A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., Donini, O., Cieplak, P., Srinivasan, J., Case, D.A. & Cheatham, T.E., Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models, Acc. Chem. Res., 33(12), pp. 889-897, 2000.

Miller, B.R., McGee, T.D., Swails, J. M., Homeyer, N., Gohlke, H. & Roitberg, A.E., 2012, MMPBSA.py: An efficient Program for End-State Free Energy Calculations, J. Chem. Theory Comput., 8(9), 3314-3321, 2012.

Arba, M., Yamin, Ihsan, S. & Tjahjono, D.H., Computational approach Toward Targeting the Interaction of Porphyrin Derivatives with Bcl-2, J Appl. Pharm. Sci., 8(12), pp. 60-66, 2018.

Arba, M., Ruslin, Ihsan, S., Tri Wahyudi, S. & Tjahjono, D.H., Molecular Modeling of Cationic Porphyrin-Anthraquinone Hybrids as DNA Topoisomerase II Inhibitors, Comput. Biol. Chem., 71, pp. 129-135, 2017.