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Research Article

Integration of ligand and structure-based pharmacophore screening for the identification of novel natural leads against Euchromatic histone lysine methyltransferase 2 (EHMT2/G9a)

, , , ORCID Icon & ORCID Icon
Pages 3535-3562 | Received 14 Feb 2023, Accepted 08 May 2023, Published online: 22 May 2023

References

  • Ahmad, K., Balaramnavar, V. M., Baig, M. H., Srivastava, A. K., Khan, S., & Kamal, M. A. (2014). Identification of potent caspase-3 inhibitors for treatment of multi-neurodegenerative diseases using pharmacophore modeling and docking approaches. CNS & Neurological Disorders Drug Targets, 13(8), 1346–1353. https://doi.org/10.2174/1871527313666141023120843
  • Arooj, M., Sakkiah, S., Kim, S., Arulalapperumal, V., & Lee, K. W. (2013). A combination of receptor-based pharmacophore modeling – QM techniques for identification of human chymase inhibitors. PLoS One, 8(4), e63030. https://doi.org/10.1371/journal.pone.0063030
  • Arrowsmith, C. H., Bountra, C., Fish, P. V., Lee, K., & Schapira, M. (2012). Epigenetic protein families: A new frontier for drug discovery. Nature Reviews Drug Discovery, 11(5), 384–400. https://doi.org/10.1038/nrd3674
  • Baig, M. H., Ahmad, K., Roy, S., Ashraf, J. M., Adil, M., Siddiqui, M. H., Khan, S., Kamal, M. A., Provazník, I., & Choi, I. (2016). Computer aided drug design: success and limitations. Current Pharmaceutical Design, 22(5), 572–581. https://doi.org/10.2174/1381612822666151125000550
  • Banerjee, D. R., Deckard, C. E., Zeng, Y. iii, & Sczepanski, J. T. (2019). Acetylation of the histone H3 tail domain regulates base excision repair on higher-order chromatin structures.Scientific Report, 9, 15972 https://doi.org/10.1038/s41598-019-52340-0
  • Banerjee, D. R., Deckard, C. E., Elinski, M. B., Buzbee, M. L., Wang, W. W., Batteas, J. D., & Sczepanski, J. T. (2018). Plug-and-play approach for preparing chromatin containing site-specific DNA modifications: The influence of chromatin structure on base excision repair. Journal of the American Chemical Society, 140(26), 8260–8267. https://doi.org/10.1021/jacs.8b04063
  • Bannister, A. J., & Kouzarides, T. (2011). Regulation of chromatin by histone modifications. Cell Research, 21(3), 381–395. https://doi.org/10.1038/cr.2011.22
  • Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007). High-resolution profiling of histone methylations in the human genome. Cell, 129(4), 823–837. https://doi.org/10.1016/j.cell.2007.05.009
  • Berger, S. L., Kouzarides, T., Shiekhattar, R., & Shilatifard, A. (2009). An operational definition of epigenetics: Figure 1. Genes & Development, 23(7), 781–783. https://doi.org/10.1101/gad.1787609
  • Bernstein, B. E., Meissner, A., & Lander, E. S. (2007). The mammalian epigenome. Cell, 128(4), 669–681. https://doi.org/10.1016/j.cell.2007.01.033
  • BIOVIA. (2022). Dassault systèmes. Dassault Systèmes.
  • Bologa, C. G., Olah, M. M., & Oprea, T. I. (2006). Chemical database preparation for compound acquisition or virtual screening. In Bioinformatics and drug discovery (pp. 375–388). Humana Press. https://doi.org/10.1385/1-59259-964-8:375
  • Bowers, K. J., Chow, D. E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., Klepeis, J. L., Kolossvary, I., Moraes, M. A., Sacerdoti, F. D., Salmon, J. K., Shan, Y., & Shaw, D. E. (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters [Paper presentation]. ACM/IEEE SC 2006 Conference (SC’06), IEEE. pp. 43–43. https://doi.org/10.1109/SC.2006.54
  • Cao, H., Li, L., Yang, D., Zeng, L., Yewei, X., Yu, B., Liao, G., & Chen, J. (2019). Recent progress in histone methyltransferase (G9a) inhibitors as anticancer agents. European Journal of Medicinal Chemistry, 179, 537–546. https://doi.org/10.1016/j.ejmech.2019.06.072
  • Chae, Y.-C., Kim, J.-Y., Park, J. W., Kim, K.-B., Oh, H., Lee, K.-H., & Seo, S.-B. (2019). FOXO1 degradation via G9a-mediated methylation promotes cell proliferation in colon cancer. Nucleic Acids Research, 47(4), 1692–1705. https://doi.org/10.1093/nar/gky1230
  • Chang, Y., Ganesh, T., Horton, J. R., Spannhoff, A., Liu, J., Sun, A., Zhang, X., Bedford, M. T., Shinkai, Y., Snyder, J. P., & Cheng, X. (2010). Adding a lysine mimic in the design of potent inhibitors of histone lysine methyltransferases. Journal of Molecular Biology, 400(1), 1–7. https://doi.org/10.1016/j.jmb.2010.04.048
  • Chang, Y., Zhang, X., Horton, J. R., Upadhyay, A. K., Spannhoff, A., Liu, J., Snyder, J. P., Bedford, M. T., & Cheng, X. (2009). Structural basis for G9a-like protein lysine methyltransferase inhibition by BIX-01294. Nature Structural & Molecular Biology, 16(3), 312–317. https://doi.org/10.1038/nsmb.1560
  • Chen, P.-Y., Tsai, C.-T., Ou, C.-Y., Hsu, W.-T., Jhuo, M.-D., Wu, C.-H., Shih, T.-C., Cheng, T.-H., & Chung, J.-G. (2012). Computational analysis of novel drugs designed for use as acetylcholinesterase inhibitors and histamine H3 receptor antagonists for Alzheimer’s disease by docking, scoring and de novo evolution. Molecular Medicine Reports, 5(4), 1043–1048. https://doi.org/10.3892/mmr.2012.757
  • Cheng, X., Collins, R. E., & Zhang, X. (2005). Structural and sequence motifs of protein (histone) methylation enzymes. Annual Review of Biophysics and Biomolecular Structure, 34, 267–294. https://doi.org/10.1146/annurev.biophys.34.040204.144452
  • Clark, T., Chandrasekhar, J., Spitznagel, G. W., & Schleyer, P. V. R. (1983). Efficient diffuse function-augmented basis sets for anion calculations. III. The 3-21 + G basis set for first-row elements, Li-F. Journal of Computational Chemistry, 4(3), 294–301. https://doi.org/10.1002/jcc.540040303
  • de Smedt, E., Devin, J., Muylaert, C., Robert, N., Requirand, G., Vlummens, P., Vincent, L., Cartron, G., Maes, K., Moreaux, J., & de Bruyne, E. (2021). G9a/GLP targeting in MM promotes autophagy-associated apoptosis and boosts proteasome inhibitor–mediated cell death. Blood Advances, 5(9), 2325–2338. https://doi.org/10.1182/bloodadvances.2020003217
  • Epsztejn-Litman, S., Feldman, N., Abu-Remaileh, M., Shufaro, Y., Gerson, A., Ueda, J., Deplus, R., Fuks, F., Shinkai, Y., Cedar, H., & Bergman, Y. (2008). De novo DNA methylation promoted by G9a prevents reprogramming of embryonically silenced genes. Nature Structural & Molecular Biology, 15(11), 1176–1183. https://doi.org/10.1038/nsmb.1476
  • Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. Journal of Chemical Physics, 103(19), 8577–8593. https://doi.org/10.1063/1.470117
  • Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., Scalmani, G., Barone, V., Mennucci, B., Petersson, G. A., Nakatsuji, H., Caricato, M., Li, X., Hratchian, H. P., Izmaylov, A. F., Bloino, J., Zheng, G., Sonnenberg, J. L., Hada, M., … Fox, D. J. (2009). Gaussian 09, revision E.01. Gaussian.
  • Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., & Overington, J. P. (2012). ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40(Database issue), D1100–D1107. https://doi.org/10.1093/nar/gkr777
  • Gelato, K. A., & Fischle, W. (2008). Role of histone modifications in defining chromatin structure and function. Biological Chemistry, 389(4), 353–363. https://doi.org/10.1515/BC.2008.048
  • Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
  • Greiner, D., Bonaldi, T., Eskeland, R., Roemer, E., & Imhof, A. (2005). Identification of a specific inhibitor of the histone methyltransferase SU(VAR)3-9. Nature Chemical Biology, 1(3), 143–145. https://doi.org/10.1038/nchembio721
  • Grewal, S. I. S., & Moazed, D. (2003). Heterochromatin and epigenetic control of gene expression. Science (New York, N.Y.), 301(5634), 798–802. https://doi.org/10.1126/science.1086887
  • Griñán-Ferré, C., Marsal-García, L., Bellver-Sanchis, A., Kondengaden, S. M., Turga, R. C., Vázquez, S., & Pallàs, M. (2019). Pharmacological inhibition of G9a/GLP restores cognition and reduces oxidative stress, neuroinflammation and β-Amyloid plaques in an early-onset Alzheimer’s disease mouse model. Aging, 11(23), 11591–11608. https://doi.org/10.18632/aging.102558
  • Güner, O. F. (2002). History and evolution of the pharmacophore concept in computer-aided drug design. Current Topics in Medicinal Chemistry. 2, 1321–1332. https://doi.org/10.2174/1568026023392940
  • Halim, S. A., Waqas, M., Asim, A., Khan, M., Khan, A., & Al-Harrasi, A. (2022). Discovering novel inhibitors of P2Y12 receptor using structure-based virtual screening, molecular dynamics simulation and MMPBSA approaches. Computers in Biology and Medicine, 147, 105743. https://doi.org/10.1016/j.compbiomed.2022.105743
  • Han, J. L. T., Pang, K. K. L., Ang, S. R. X., Sharma, M., & Sajikumar, S. (2021). Inhibition of lysine methyltransferase G9a/GLP reinstates long-term synaptic plasticity and synaptic tagging/capture by facilitating protein synthesis in the hippocampal CA1 area of APP/PS1 mouse model of Alzheimer’s disease. Translational Neurodegeneration, 10(1), 23. https://doi.org/10.1186/s40035-021-00247-0
  • Hoover, W. G. (1985). Canonical dynamics: Equilibrium phase-space distributions. Physical Review. A, General Physics, 31(3), 1695–1697. https://doi.org/10.1103/PhysRevA.31.1695
  • Huang, J., Dorsey, J., Chuikov, S., Zhang, X., Jenuwein, T., Reinberg, D., & Berger, S. L. (2010). G9a and Glp methylate lysine 373 in the tumor suppressor p53. The Journal of Biological Chemistry, 285(13), 9636–9641. https://doi.org/10.1074/jbc.M109.062588
  • Humphreys, D. D., Friesner, R. A., & Berne, B. J. (1994). A multiple-time-step molecular dynamics algorithm for macromolecules. The Journal of Physical Chemistry, 98(27), 6885–6892. https://doi.org/10.1021/j100078a035
  • Jacobson, M. P., Friesner, R. A., Xiang, Z., & Honig, B. (2002). On the role of the crystal environment in determining protein side-chain conformations. Journal of Molecular Biology, 320(3), 597–608. https://doi.org/10.1016/S0022-2836(02)00470-9
  • Jana, A., Naga, R., Saha, S., & Banerjee, D. R. (2022a). 3D QSAR pharmacophore based lead identification of G9a lysine methyltransferase towards epigenetic therapeutics. Journal of Biomolecular Structure and Dynamics, 1–19. https://doi.org/10.1080/07391102.2022.2135600
  • Jana, A., Roy, T., Layek, S., Ghosal, S., & Banerjee, D. R. (2022b). Computational investigation on natural quinazoline alkaloids as potential inhibitors of the main protease (Mpro) of SARS-CoV-2. Journal of Computational Biophysics and Chemistry, 21(01), 65–82. https://doi.org/10.1142/S2737416522500053
  • Jenuwein, T., & Allis, C. D. (2001). Translating the histone code. Science (New York, N.Y.), 293(5532), 1074–1080. https://doi.org/10.1126/science.1063127
  • Jin, W.-Y., Ma, Y., Li, W.-Y., Li, H.-L., & Wang, R.-L. (2018). Scaffold-based novel SHP2 allosteric inhibitors design using receptor-ligand pharmacophore model, virtual screening and molecular dynamics. Computational Biology and Chemistry, 73, 179–188. https://doi.org/10.1016/j.compbiolchem.2018.02.004
  • Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. Journal of Chemical Physics. 79(2), 926–935. https://doi.org/10.1063/1.445869
  • Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. (1996). Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. Journal of the American Chemical Society, 118(45), 11225–11236. https://doi.org/10.1021/ja9621760
  • Kaniskan, H. Ü., & Jin, J. (2015). Chemical probes of histone lysine methyltransferases. ACS Chemical Biology, 10(1), 40–50. https://doi.org/10.1021/cb500785t
  • Kaniskan, H. Ü., Martini, M. L., & Jin, J. (2018). Inhibitors of protein methyltransferases and demethylases. Chemical Reviews, 118(3), 989–1068. https://doi.org/10.1021/acs.chemrev.6b00801
  • Katayama, K., Ishii, K., Terashima, H., Tsuda, E., Suzuki, M., Yotsumoto, K., Hiramoto, K., Yasumatsu, I., Torihata, M., Ishiyama, T., Muto, T., & Katagiri, T. (2021). Discovery of DS79932728: A potent, orally available G9a/GLP inhibitor for treating β-thalassemia and sickle cell disease. ACS Medicinal Chemistry Letters, 12(1), 121–128. https://doi.org/10.1021/acsmedchemlett.0c00572
  • Katayama, K., Ishii, K., Tsuda, E., Yotsumoto, K., Hiramoto, K., Suzuki, M., Yasumatsu, I., Igarashi, W., Torihata, M., Ishiyama, T., & Katagiri, T. (2020). Discovery of novel histone lysine methyltransferase G9a/GLP (EHMT2/1) inhibitors: Design, synthesis, and structure-activity relationships of 2,4-diamino-6-methylpyrimidines. Bioorganic & Medicinal Chemistry Letters, 30(20), 127475. https://doi.org/10.1016/J.BMCL.2020.127475
  • Kondo, Y., Shen, L., Ahmed, S., Boumber, Y., Sekido, Y., Haddad, B. R., & Issa-Pierre, J. I. (2008). Downregulation of histone H3 lysine 9 methyltransferase G9a induces centrosome disruption and chromosome instability in cancer cells. PLoS One, 3(4), e2037. https://doi.org/10.1371/journal.pone.0002037
  • Koska, J., Spassov, V. Z., Maynard, A. J., Yan, L., Austin, N., Flook, P. K., & Venkatachalam, C. M. (2008). Fully automated molecular mechanics based induced fit protein − ligand docking method. Journal of Chemical Information and Modeling, 48(10), 1965–1973. https://doi.org/10.1021/ci800081s
  • Kouzarides, T. (2007). Chromatin modifications and their function. Cell, 128(4), 693–705. https://doi.org/10.1016/j.cell.2007.02.005
  • Krammer, A., Kirchhoff, P. D., Jiang, X., Venkatachalam, C. M., & Waldman, M. (2005). LigScore: A novel scoring function for predicting binding affinities. Journal of Molecular Graphics & Modelling, 23(5), 395–407. https://doi.org/10.1016/j.jmgm.2004.11.007
  • Krishnan, R., Binkley, J. S., Seeger, R., & Pople, J. A. (1980). Self‐consistent molecular orbital methods. XX. A basis set for correlated wave functions. Journal of Chemical Physics, 72(1), 650–654. https://doi.org/10.1063/1.438955
  • Kubicek, S., O'Sullivan, R. J., August, E. M., Hickey, E. R., Zhang, Q., Teodoro, M. L., Rea, S., Mechtler, K., Kowalski, J. A., Homon, C. A., Kelly, T. A., & Jenuwein, T. (2007). Reversal of H3K9me2 by a small-molecule inhibitor for the G9a histone methyltransferase. Molecular Cell, 25(3), 473–481., https://doi.org/10.1016/j.molcel.2007.01.017
  • Kuhn, B., Gerber, P., Schulz-Gasch, T., & Stahl, M. (2005). Validation and use of the MM-PBSA approach for drug discovery. Journal of Medicinal Chemistry, 48(12), 4040–4048. https://doi.org/10.1021/jm049081q
  • Leung, D. C., Dong, K. B., Maksakova, I. A., Goyal, P., Appanah, R., Lee, S., Tachibana, M., Shinkai, Y., Lehnertz, B., Mager, D. L., Rossi, F., & Lorincz, M. C. (2011). Lysine methyltransferase G9a is required for de novo DNA methylation and the establishment, but not the maintenance, of proviral silencing. Proceedings of the National Academy of Sciences of the United States of America, 108(14), 5718–5723. https://doi.org/10.1073/pnas.1014660108
  • Lin, C.-H., Chang, T.-T., Sun, M.-F., Chen, H.-Y., Tsai, F.-J., Chang, K.-L., Fisher, M., & Chen, C. Y.-C. (2011). Potent inhibitor design against H1N1 swine influenza: Structure-based and molecular dynamics analysis for M2 inhibitors from traditional Chinese medicine database. Journal of Biomolecular Structure & Dynamics, 28(4), 471–482. https://doi.org/10.1080/07391102.2011.10508589
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46(1-3), 3–26. https://doi.org/10.1016/s0169-409x(00)00129-0
  • Liu, F., Barsyte-Lovejoy, D., Allali-Hassani, A., He, Y., Herold, J. M., Chen, X., Yates, C. M., Frye, S. V., Brown, P. J., Huang, J., Vedadi, M., Arrowsmith, C. H., & Jin, J. (2011). Optimization of cellular activity of G9a inhibitors 7-aminoalkoxy-quinazolines. Journal of Medicinal Chemistry, 54(17), 6139–6150. https://doi.org/10.1021/jm200903z
  • Liu, F., Chen, X., Allali-Hassani, A., Quinn, A. M., Wasney, G. A., Dong, A., Barsyte, D., Kozieradzki, I., Senisterra, G., Chau, I., Siarheyeva, A., Kireev, D. B., Jadhav, A., Herold, J. M., Frye, S. V., Arrowsmith, C. H., Brown, P. J., Simeonov, A., Vedadi, M., & Jin, J. (2009). Discovery of a 2,4-diamino-7-aminoalkoxyquinazoline as a potent and selective inhibitor of histone lysine methyltransferase G9a. Journal of Medicinal Chemistry, 52(24), 7950–7953. https://doi.org/10.1021/jm901543m
  • Liu, F., Chen, X., Allali-Hassani, A., Quinn, A. M., Wigle, T. J., Wasney, G. A., Dong, A., Senisterra, G., Chau, I., Siarheyeva, A., Norris, J. L., Kireev, D. B., Jadhav, A., Herold, J. M., Janzen, W. P., Arrowsmith, C. H., Frye, S. V., Brown, P. J., Simeonov, A., Vedadi, M., & Jin, J. (2010). Protein lysine methyltransferase G9a inhibitors: design, synthesis, and structure activity relationships of 2,4-diamino-7-aminoalkoxy-quinazolines. Journal of Medicinal Chemistry, 53(15), 5844–5857. https://doi.org/10.1021/jm100478y
  • Ma, D.-L., Chan, D. S.-H., & Leung, C.-H. (2011). Molecular docking for virtual screening of natural product databases. Chemical Science, 2(9), 1656–1665. https://doi.org/10.1039/C1SC00152C
  • Manhas, A., Kumar, S., & Jha, P. C. (2022). Identification of the natural compound inhibitors against Plasmodium falciparum plasmepsin-II via common feature based screening and molecular dynamics simulations. Journal of Biomolecular Structure & Dynamics, 40(1), 31–43. https://doi.org/10.1080/07391102.2020.1806110
  • Martin, C., & Zhang, Y. (2005). The diverse functions of histone lysine methylation. Nature Reviews. Molecular Cell Biology, 6(11), 838–849. https://doi.org/10.1038/nrm1761
  • Martyna, G. J., Tobias, D. J., & Klein, M. L. (1994). Constant pressure molecular dynamics algorithms. Journal of Chemical Physics. 101(5), 4177–4189. https://doi.org/10.1063/1.467468
  • Moreira-Silva, F., Outeiro-Pinho, G., Lobo, J., Guimarães, R., Gaspar, V. M., Mano, J. F., Agirre, X., Pineda-Lucena, A., Prosper, F., Paramio, J. M., Henrique, R., Correia, M. P., & Jerónimo, C. (2022). G9a inhibition by CM-272: Developing a novel anti-tumoral strategy for castration-resistant prostate cancer using 2D and 3D in vitro models. Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie, 150, 113031. https://doi.org/10.1016/j.biopha.2022.113031
  • Opo, F. A. D. M., Rahman, M. M., Ahammad, F., Ahmed, I., Bhuiyan, M. A., & Asiri, A. M. (2021). Author correction: Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Scientific Reports, 11(1), 19106. https://doi.org/10.1038/s41598-021-97945-6
  • Pal, S., Kumar, V., Kundu, B., Bhattacharya, D., Preethy, N., Reddy, M. P., & Talukdar, A. (2019). Ligand-based pharmacophore modeling, virtual screening and molecular docking studies for discovery of potential topoisomerase I inhibitors. Computational and Structural Biotechnology Journal, 17, 291–310. https://doi.org/10.1016/j.csbj.2019.02.006
  • Peng, J., Li, Y., Zhou, Y., Zhang, L., Liu, X., & Zuo, Z. (2018). Pharmacophore modeling, molecular docking and molecular dynamics studies on natural products database to discover novel skeleton as non-purine xanthine oxidase inhibitors. Journal of Receptor and Signal Transduction Research, 38(3), 246–255. https://doi.org/10.1080/10799893.2018.1476544
  • Puthanveedu, V., & Muraleedharan, K. (2022). Phytochemicals as potential inhibitors for COVID-19 revealed by molecular docking, molecular dynamic simulation and DFT studies. Structural Chemistry, 33(5), 1423–1443. https://doi.org/10.1007/s11224-022-01982-4
  • Raafat, A., Mowafy, S., Abouseri, S., M., Fouad, M. A., & Farag, N. A. (2022). Lead generation of cysteine based mesenchymal epithelial transition (c-Met) kinase inhibitors: Using structure-based scaffold hopping, 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics simulation. Computers in Biology and Medicine, 146, 105526. https://doi.org/10.1016/j.compbiomed.2022.105526
  • Ramya Chandar Charles, M., Hsieh, H.-P., & Selvaraj Coumar, M. (2019). Delineating the active site architecture of G9a lysine methyltransferase through substrate and inhibitor binding mode analysis: a molecular dynamics study. Journal of Biomolecular Structure & Dynamics, 37(10), 2581–2592. https://doi.org/10.1080/07391102.2018.1491422
  • Ramya Chandar Charles, M., Li, M.-C., Hsieh, H.-P., & Coumar, M. S. (2021). Mimicking H3 substrate arginine in the design of G9a lysine methyltransferase inhibitors for cancer therapy: A computational study for structure-based drug design. ACS Omega, 6(9), 6100–6111. https://doi.org/10.1021/acsomega.0c04710
  • Ramya Chandar Charles, M., Mahesh, A., Lin, S.-Y., Hsieh, H.-P., Dhayalan, A., & Coumar, M. S. (2020). Identification of novel quinoline inhibitor for EHMT2/G9a through virtual screening. Biochimie, 168, 220–230. https://doi.org/10.1016/j.biochi.2019.11.006
  • Sakkiah, S., Arullaperumal, V., Hwang, S., & Lee, K. W. (2014). Ligand-based pharmacophore modeling and Bayesian approaches to identify c-Src inhibitors. Journal of Enzyme Inhibition and Medicinal Chemistry, 29(1), 69–80. https://doi.org/10.3109/14756366.2012.753881
  • Sakkiah, S., Thangapandian, S., John, S., Kwon, Y. J., & Lee, K. W. (2010). 3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors. European Journal of Medicinal Chemistry, 45(6), 2132–2140. https://doi.org/10.1016/j.ejmech.2010.01.016
  • Salam, N. K., Nuti, R., & Sherman, W. (2009). Novel method for generating structure-based pharmacophores using energetic analysis. Journal of Chemical Information and Modeling, 49(10), 2356–2368. https://doi.org/10.1021/ci900212v
  • Sanchez, N. A., Kallweit, L. M., Trnka, M. J., Clemmer, C. L., & Al-Sady, B. (2021). Heterodimerization of H3K9 histone methyltransferases G9a and GLP activates methyl reading and writing capabilities. The Journal of Biological Chemistry, 297(5), 101276. https://doi.org/10.1016/j.jbc.2021.101276
  • Sepay, N., Mondal, R., Al-Muhanna, M. K., & Saha, D. (2022). Identification of natural flavonoids as novel EGFR inhibitors using DFT, molecular docking, and molecular dynamics. New Journal of Chemistry, 46(20), 9735–9744. https://doi.org/10.1039/D2NJ00389A
  • Sessions, Z., Sánchez-Cruz, N., Prieto-Martínez, F. D., Alves, V. M., Santos, H. P., Muratov, E., Tropsha, A., & Medina-Franco, J. L. (2020). Recent progress on cheminformatics approaches to epigenetic drug discovery. Drug Discovery Today, 25(12), 2268–2276. https://doi.org/10.1016/j.drudis.2020.09.021
  • Shivanika, C., Deepak Kumar, S., Ragunathan, V., Tiwari, P., Sumitha, A., & Brindha Devi, P. (2022). Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease. Journal of Biomolecular Structure & Dynamics, 40(2), 585–611. https://doi.org/10.1080/07391102.2020.1815584
  • Singh, R., Bhardwaj, V. K., Das, P., Bhattacherjee, D., Zyryanov, G. V., & Purohit, R. (2022). Benchmarking the ability of novel compounds to inhibit SARS-CoV-2 main protease using steered molecular dynamics simulations. Computers in Biology and Medicine, 146, 105572. https://doi.org/10.1016/j.compbiomed.2022.105572
  • Sirous, H., Campiani, G., Calderone, V., & Brogi, S. (2021). Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening. Computers in Biology and Medicine, 137, 104808. https://doi.org/10.1016/j.compbiomed.2021.104808
  • Sorokina, M., Merseburger, P., Rajan, K., Yirik, M. A., & Steinbeck, C. (2021). COCONUT online: Collection of open natural products database. Journal of Cheminformatics, 13(1), 2. https://doi.org/10.1186/s13321-020-00478-9
  • Spassov, V. Z., Yan, L., & Flook, P. K. (2007). The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: A side-chain prediction algorithm based on side-chain backbone interactions. Protein Science: A Publication of the Protein Society, 16(3), 494–506. https://doi.org/10.1110/ps.062447107
  • Srimongkolpithak, N., Sundriyal, S., Li, F., Vedadi, M., & Fuchter, M. J. (2014). Identification of 2,4-diamino-6,7-dimethoxyquinoline derivatives as G9a inhibitors. Medchemcomm, 5(12), 1821–1828. https://doi.org/10.1039/C4MD00274A
  • Strahl, B. D., & Allis, C. D. (2000). The language of covalent histone modifications. Nature, 403(6765), 41–45. https://doi.org/10.1038/47412
  • Sutter, J., Li, J., Maynard, A. J., Goupil, A., Luu, T., & Nadassy, K. (2011). New features that improve the pharmacophore tools from Accelrys. Current Computer-Aided Drug Design, 7(3), 173–180. https://doi.org/10.2174/157340911796504305
  • Sweis, R. F., Pliushchev, M., Brown, P. J., Guo, J., Li, F., Maag, D., Petros, A. M., Soni, N. B., Tse, C., Vedadi, M., Michaelides, M. R., Chiang, G. G., & Pappano, W. N. (2014). Discovery and development of potent and selective inhibitors of histone methyltransferase G9a. ACS Medicinal Chemistry Letters, 5(2), 205–209. https://doi.org/10.1021/ml400496h
  • Tanwar, G., Mazumder, A. G., Bhardwaj, V., Kumari, S., Bharti, R., Damanpreet Singh, Y., Das, P., & Purohit, R. (2019). Target identification, screening and in vivo evaluation of pyrrolone-fused benzosuberene compounds against human epilepsy using Zebrafish model of pentylenetetrazol-induced seizures. Scientific Reports, 9(1), 7904. https://doi.org/10.1038/s41598-019-44264-6
  • Thangapandian, S., John, S., Sakkiah, S., & Lee, K. W. (2010). Ligand and structure based pharmacophore modeling to facilitate novel histone deacetylase 8 inhibitor design. European Journal of Medicinal Chemistry, 45(10), 4409–4417. https://doi.org/10.1016/j.ejmech.2010.06.024
  • Thangapandian, S., John, S., Sakkiah, S., & Lee, K. W. (2011). Potential virtual lead identification in the discovery of renin inhibitors: Application of ligand and structure-based pharmacophore modeling approaches. European Journal of Medicinal Chemistry, 46(6), 2469–2476. https://doi.org/10.1016/j.ejmech.2011.03.035
  • Thienpont, B., Aronsen, J. M., Robinson, E. L., Okkenhaug, H., Loche, E., Ferrini, A., Brien, P., Alkass, K., Tomasso, A., Agrawal, A., Bergmann, O., Sjaastad, I., Reik, W., & Roderick, H. L. (2017). The H3K9 dimethyltransferases EHMT1/2 protect against pathological cardiac hypertrophy. The Journal of Clinical Investigation, 127(1), 335–348. https://doi.org/10.1172/JCI88353
  • Vasilopoulou, F., Bellver-Sanchis, A., Companys-Alemany, J., Jarne-Ferrer, J., Irisarri, A., Palomera-Ávalos, V., Gonzalez-Castillo, C., Ortuño-Sahagún, D., Sanfeliu, C., Pallàs, M., & Griñán-Ferré, C. (2022). Cognitive decline and BPSD are concomitant with autophagic and synaptic deficits associated with G9a alterations in aged SAMP8 mice. Cells, 11(16), 2603. https://doi.org/10.3390/cells11162603
  • Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615–2623. https://doi.org/10.1021/jm020017n
  • Vedadi, M., Barsyte-Lovejoy, D., Liu, F., Rival-Gervier, S., Allali-Hassani, A., Labrie, V., Wigle, T. J., DiMaggio, P. A., Wasney, G. A., Siarheyeva, A., Dong, A., Tempel, W., Wang, S.-C., Chen, X., Chau, I., Mangano, T. J., Huang, X., Simpson, C. D., Pattenden, S. G., … Jin, J. (2011). A chemical probe selectively inhibits G9a and GLP methyltransferase activity in cells. Nature Chemical Biology, 7(8), 566–574. https://doi.org/10.1038/nchembio.599
  • Vilhais-Neto, G. C., Fournier, M., Plassat, J.-L., Sardiu, M. E., Saraf, A., Garnier, J.-M., Maruhashi, M., Florens, L., Washburn, M. P., & Pourquié, O. (2017). The WHHERE coactivator complex is required for retinoic acid-dependent regulation of embryonic symmetry. Nature Communications, 8(1), 728. https://doi.org/10.1038/s41467-017-00593-6
  • Watanabe, H., Soejima, K., Yasuda, H., Kawada, I., Nakachi, I., Yoda, S., Naoki, K., & Ishizaka, A. (2008). Deregulation of histone lysine methyltransferases contributes to oncogenic transformation of human bronchoepithelial cells. Cancer Cell International, 8(1), 15. https://doi.org/10.1186/1475-2867-8-15
  • Wu, G., Robertson, D. H., Brooks, C. L., & Vieth, M. (2003). Detailed analysis of grid-based molecular docking: A case study of CDOCKER? A CHARMm-based MD docking algorithm. Journal of Computational Chemistry, 24(13), 1549–1562. https://doi.org/10.1002/jcc.10306
  • Xiong, Y., Li, F., Babault, N., Dong, A., Zeng, H., Wu, H., Chen, X., Arrowsmith, C. H., Brown, P. J., Liu, J., Vedadi, M., & Jin, J. (2017a). Discovery of potent and selective inhibitors for G9a-like protein (GLP) lysine methyltransferase. Journal of Medicinal Chemistry, 60(5), 1876–1891. https://doi.org/10.1021/ACS.JMEDCHEM.6B01645/SUPPL_FILE/JM6B01645_SI_001.PDF
  • Xiong, Y., Li, F., Babault, N., Wu, H., Dong, A., Zeng, H., Chen, X., Arrowsmith, C. H., Brown, P. J., Liu, J., Vedadi, M., & Jin, J. (2017b). Structure-activity relationship studies of G9a-like protein (GLP) inhibitors. Bioorganic & Medicinal Chemistry, 25(16), 4414–4423. https://doi.org/10.1016/j.bmc.2017.06.021
  • Yang, Q., Zhu, Q., Lu, X., Du, Y., Cao, L., Shen, C., Hou, T., Li, M., Li, Z., Liu, C., Wu, D., Xu, X., Wang, L., Wang, H., Zhao, Y., Yang, Y., & Zhu, W.-G. (2017). G9a coordinates with the RPA complex to promote DNA damage repair and cell survival. Proceedings of the National Academy of Sciences, 114(30), E6054-E6063. https://doi.org/10.1073/pnas.1700694114
  • Yankulov, K. (2015). Book review: Epigenetics (second edition, eds. Allis, Caparros, Jenuwein, Reinberg). Frontiers in Genetics, 6, 315. https://doi.org/10.3389/fgene.2015.00315
  • Zhang, Z., & Pugh, B. F. (2011). High-resolution genome-wide mapping of the primary structure of chromatin. Cell, 144(2), 175–186. https://doi.org/10.1016/j.cell.2011.01.003
  • Zhong, S., Hou, Y., Zhang, Z., Guo, Z., Yang, W., Dou, G., Lv, X., Wang, X., Ge, J., Wu, B., Pan, X., Wang, H., Yang, Q., & Mou, Y. (2022). Identification of novel natural inhibitors targeting AKT serine/threonine kinase 1 (AKT1) by computational study. Bioengineered, 13(5), 12003–12020. https://doi.org/10.1080/21655979.2021.2011631

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