Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation.
DNA methyltransferase
DNMT3A
Molecular dynamics simulation
Protein inhibitor
Selective inhibitor
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 06 2024
12 06 2024
Historique:
received:
08
12
2023
accepted:
06
06
2024
medline:
13
6
2024
pubmed:
13
6
2024
entrez:
12
6
2024
Statut:
epublish
Résumé
DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.
Identifiants
pubmed: 38866895
doi: 10.1038/s41598-024-64236-9
pii: 10.1038/s41598-024-64236-9
doi:
Substances chimiques
DNA (Cytosine-5-)-Methyltransferases
EC 2.1.1.37
DNA Methyltransferase 3A
EC 2.1.1.37
Enzyme Inhibitors
0
DNMT3A protein, human
0
DNA (Cytosine-5-)-Methyltransferase 1
EC 2.1.1.37
DNMT1 protein, human
EC 2.1.1.37
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
13508Subventions
Organisme : Japan Society for the Promotion of Science
ID : JP21K06094
Organisme : Japan Society for the Promotion of Science
ID : JP23K16987
Organisme : Japan Agency for Medical Research and Development
ID : JP22ama121029j0002
Organisme : Ministry of Education, Culture, Sports, Science and Technology
ID : JPMXP1020230120
Informations de copyright
© 2024. The Author(s).
Références
Li, L. et al. Epigenetic inactivation of the CpG demethylase TET1 as a DNA methylation feedback loop in human cancers. Sci. Rep. 6, 26591 (2016).
pubmed: 27225590
pmcid: 4880909
doi: 10.1038/srep26591
Gopalakrishnan, S., Van Emburgh, B. O. & Robertson, K. D. DNA methylation in development and human disease. Mutat. Res. 647, 30–38 (2008).
pubmed: 18778722
pmcid: 2647981
doi: 10.1016/j.mrfmmm.2008.08.006
Pan, Y., Liu, G., Zhou, F., Su, B. & Li, Y. DNA methylation profiles in cancer diagnosis and therapeutics. Clin. Exp. Med. 18, 1–14 (2018).
pubmed: 28752221
doi: 10.1007/s10238-017-0467-0
Gros, C. et al. DNA methylation inhibitors in cancer: Recent and future approaches. Biochimie. 94, 2280–2296 (2012).
pubmed: 22967704
doi: 10.1016/j.biochi.2012.07.025
Klutstein, M., Nejman, D., Greenfield, R. & Cedar, H. D. N. A. DNA Methylation in cancer and aging. Can. Res. 76, 3446–3450 (2016).
doi: 10.1158/0008-5472.CAN-15-3278
Daniel, F. I., Cherubini, K., Yurgel, L. S., de Figueiredo, M. A. & Salum, F. G. The role of epigenetic transcription repression and DNA methyltransferases in cancer. Cancer. 117, 677–687 (2011).
pubmed: 20945317
doi: 10.1002/cncr.25482
Okano, M., Bell, D. W., Haber, D. A. & Li, E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 99, 247–257 (1999).
pubmed: 10555141
doi: 10.1016/S0092-8674(00)81656-6
Gasche, J. A. & Goel, A. Epigenetic mechanisms in oral carcinogenesis. Future Oncol. 8, 1407–1425 (2012).
pubmed: 23148615
doi: 10.2217/fon.12.138
Piyathilake, C. J. et al. Pattern of nonspecific (or global) DNA methylation in oral carcinogenesis. Head Neck. 27, 1061–1067 (2005).
pubmed: 16155917
pmcid: 1853326
doi: 10.1002/hed.20288
Subramaniam, D., Thombre, R., Dhar, A. & Anant, S. DNA methyltransferases: A novel target for prevention and therapy. Front. Oncol. 4, 80 (2014).
pubmed: 24822169
pmcid: 4013461
doi: 10.3389/fonc.2014.00080
Erdmann, A., Halby, L., Fahy, J. & Arimondo, P. B. Targeting DNA methylation with small molecules: What’s next?. J. Med. Chem. 58, 2569–2583 (2015).
pubmed: 25406944
doi: 10.1021/jm500843d
Lund, K. et al. DNMT inhibitors reverse a specific signature of aberrant promoter DNA methylation and associated gene silencing in AML. Genome Biol. 15, 406 (2014).
pubmed: 25315154
pmcid: 4165364
doi: 10.1186/s13059-014-0406-2
Hughes, J. P., Rees, S., Kalindjian, S. B. & Philpott, K. L. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249 (2011).
pubmed: 21091654
pmcid: 3058157
doi: 10.1111/j.1476-5381.2010.01127.x
Huggins, D. J., Sherman, W. & Tidor, B. Rational approaches to improving selectivity in drug design. J. Med. Chem. 55, 1424–1444 (2012).
pubmed: 22239221
pmcid: 3285144
doi: 10.1021/jm2010332
Fahy, J., Jeltsch, A. & Arimondo, P. B. DNA methyltransferase inhibitors in cancer: A chemical and therapeutic patent overview and selected clinical studies. Expert Opin. Ther. Pat. 22, 1427–1442 (2012).
pubmed: 23033952
doi: 10.1517/13543776.2012.729579
Stresemann, C. & Lyko, F. Modes of action of the DNA methyltransferase inhibitors azacytidine and decitabine. Int. J. Cancer. 123, 8–13 (2008).
pubmed: 18425818
doi: 10.1002/ijc.23607
Irimie, A. I. et al. Current insights into oral cancer epigenetics. Int. J. Mol. Sci. 19, 670 (2018).
pubmed: 29495520
pmcid: 5877531
doi: 10.3390/ijms19030670
Zhou, Z., Li, H. Q. & Liu, F. DNA methyltransferase inhibitors and their therapeutic potential. Curr. Top. Med. Chem. 18, 2448–2457 (2018).
pubmed: 30465505
doi: 10.2174/1568026619666181120150122
Lamiable-Oulaidi, F. et al. Synthesis and characterization of transition-state analogue inhibitors against human DNA methyltransferase 1. J. Med. Chem. 65, 5462–5494 (2022).
pubmed: 35324190
pmcid: 9205225
doi: 10.1021/acs.jmedchem.1c01869
Halby, L. et al. Rational design of bisubstrate-type analogues as inhibitors of DNA methyltransferases in cancer cells. J. Med. Chem. 60, 4665–4679 (2017).
pubmed: 28463515
doi: 10.1021/acs.jmedchem.7b00176
Patel, L., Shukla, T., Huang, X., Ussery, D. W. & Wang, S. Machine learning methods in drug discovery. Molecules. 25, 5277 (2020).
pubmed: 33198233
pmcid: 7696134
doi: 10.3390/molecules25225277
Shaker, B. et al. In silico methods and tools for drug discovery. Comput. Biol. Med. 137(10), 4851 (2021).
Rachman, M., Piticchio, S., Majewski, M. & Barril, X. Fragment-to-lead tailored in silico design. Drug Discov. Today Technol. 40, 44–57 (2021).
pubmed: 34916022
doi: 10.1016/j.ddtec.2021.08.005
Ou-Yang, S. S. et al. Computational drug discovery. Acta Pharmacol. Sin. 33, 1131–1140 (2012).
pubmed: 22922346
pmcid: 4003107
doi: 10.1038/aps.2012.109
Jorgensen, W. L. The many roles of computation in drug discovery. Science. 303, 1813–1818 (2004).
pubmed: 15031495
doi: 10.1126/science.1096361
Kuntz, I. D. Structure-based strategies for drug design and discovery. Science. 257, 1078–1082 (1992).
pubmed: 1509259
doi: 10.1126/science.257.5073.1078
Schneider, G. & Böhm, H. J. Virtual screening and fast automated docking methods. Drug Discov. Today. 7, 64–70 (2002).
pubmed: 11790605
doi: 10.1016/S1359-6446(01)02091-8
Grinter, S. Z. & Zou, X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules. 19, 10150–10176 (2014).
pubmed: 25019558
pmcid: 6270832
doi: 10.3390/molecules190710150
Doruker, P., Atilgan, A. R. & Bahar, I. Dynamics of proteins predicted by molecular dynamics simulations and analytical approaches: Application to alpha-amylase inhibitor. Proteins. 40, 512–524 (2000).
pubmed: 10861943
doi: 10.1002/1097-0134(20000815)40:3<512::AID-PROT180>3.0.CO;2-M
Shan, Y. et al. How does a drug molecule find its target binding site?. J. Am. Chem. Soc. 133, 9181–9183 (2011).
pubmed: 21545110
pmcid: 3221467
doi: 10.1021/ja202726y
Genheden, S. & Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 10, 449–461 (2015).
pubmed: 25835573
pmcid: 4487606
doi: 10.1517/17460441.2015.1032936
Kollman, P. A. et al. Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Acc. Chem. Res. 33, 889–897 (2000).
pubmed: 11123888
doi: 10.1021/ar000033j
Liang, Z., Zhu, Y., Long, J., Ye, F. & Hu, G. Both intra and inter-domain interactions define the intrinsic dynamics and allosteric mechanism in DNMT1s. Comput. Struct. Biotechnol. J. 18, 749–764 (2020).
pubmed: 32280430
pmcid: 7132064
doi: 10.1016/j.csbj.2020.03.016
Ye, F. et al. Biochemical studies and molecular dynamic simulations reveal the molecular basis of conformational changes in DNA Methyltransferase-1. ACS Chem. Biol. 13, 772–781 (2018).
pubmed: 29381856
pmcid: 6913882
doi: 10.1021/acschembio.7b00890
Zhu, Y. et al. Insights into conformational dynamics and allostery in DNMT1-H3Ub/USP7 interactions. Molecules. 26, 5153 (2021).
pubmed: 34500587
pmcid: 8434485
doi: 10.3390/molecules26175153
Yasuda, T., Morita, R., Shigeta, Y. & Harada, R. Histone H3 inhibits ubiquitin-ubiquitin intermolecular interactions to enhance binding to DNA methyl transferase 1. J. Mol. Biol. 434, 167371 (2022).
pubmed: 34838519
doi: 10.1016/j.jmb.2021.167371
Yang, W., Zhuang, J., Li, C., Bai, C. & Cheng, G. Insights into the inhibitory mechanisms of the covalent drugs for DNMT3A. Int. J. Mol. Sci. 24(16), 12652 (2023).
pubmed: 37628829
pmcid: 10454219
doi: 10.3390/ijms241612652
Yang, W., Zhuang, J., Li, C. & Cheng, G. J. Unveiling the methyl transfer mechanisms in the epigenetic machinery DNMT3A-3L: A comprehensive study integrating assembly dynamics with catalytic reactions. Comput. Struct. Biotechnol. J. 21, 2086–2099 (2023).
pubmed: 36968013
pmcid: 10034213
doi: 10.1016/j.csbj.2023.03.002
Wakui, N., Yoshino, R., Yasuo, N., Ohue, M. & Sekijima, M. Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: A molecular dynamics simulation approach. J. Mol. Graph. Model. 79, 166–174 (2018).
pubmed: 29197725
doi: 10.1016/j.jmgm.2017.11.011
Yoshino, R., Yasuo, N. & Sekijima, M. Molecular dynamics simulation reveals the mechanism by which the influenza cap-dependent endonuclease acquires resistance against Baloxavir marboxil. Sci. Rep. 9, 17464 (2019).
pubmed: 31767949
pmcid: 6877583
doi: 10.1038/s41598-019-53945-1
Yoshino, R., Yasuo, N. & Sekijima, M. Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Sci. Rep. 10, 12493 (2020).
pubmed: 32719454
pmcid: 7385649
doi: 10.1038/s41598-020-69337-9
Zhang, Z. M. et al. Structural basis for DNMT3A-mediated de novo DNA methylation. Nature. 554, 387–391 (2018).
pubmed: 29414941
pmcid: 5814352
doi: 10.1038/nature25477
Schrödinger, Release, Maestro, Schrödinger, LLC, New York, 2019–2.
Shelley, J. C. et al. Epik: A software program for pK(a) prediction and protonation state generation for drug-like molecules. J. Comput. Aided Mol. Des. 21, 681–691 (2007).
pubmed: 17899391
doi: 10.1007/s10822-007-9133-z
Roos, K. et al. OPLS3e: Extending force field coverage for drug-like small molecules. J. Chem. Theory Comput. 15, 1863–1874 (2019).
pubmed: 30768902
doi: 10.1021/acs.jctc.8b01026
Allen, W. J. et al. DOCK 6: Impact of new features and current docking performance. J. Comput. Chem. 36, 1132–1156 (2015).
pubmed: 25914306
pmcid: 4469538
doi: 10.1002/jcc.23905
M. J. Frisch et al., Gaussian16, revision, C. 01, Gaussian, Inc., Wallingford CT (2016).
Case, D. A., et al. (University of California, San Francisco, Amber, 2021).
Maier, J. A. et al. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).
pubmed: 26574453
pmcid: 4821407
doi: 10.1021/acs.jctc.5b00255
Wang, J., 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, 1157–1174 (2004).
pubmed: 15116359
doi: 10.1002/jcc.20035
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).
doi: 10.1063/1.445869
Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007).
pubmed: 17212484
doi: 10.1063/1.2408420
Berendsen, H. J. C., Postma, J. P. M., Van Gunsteren, W. F., Dinola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).
doi: 10.1063/1.448118
Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. Lincs: A linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).
doi: 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
Van Der Spoel, D. et al. GROMACS: Fast, flexible, and free. J. Comput. Chem. 26, 1701–1718 (2005).
pubmed: 16211538
doi: 10.1002/jcc.20291
Massova, I. & Kollman, P. A. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. Des. 18, 113–135 (2000).
doi: 10.1023/A:1008763014207
Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A. & Moreno, E. gmx_MMPBSA: A New tool to perform end-state free energy calculations with GROMACS. J. Chem. Theory Comput. 17, 6281–6291 (2021).
pubmed: 34586825
doi: 10.1021/acs.jctc.1c00645
R. Schrödinger 2020–2 (BioLuminate, Schrödinger, LLC, New York, 2020).
Lyne, P. D., Lamb, M. L. & Saeh, J. C. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J. Med. Chem. 49, 4805–4808 (2006).
pubmed: 16884290
doi: 10.1021/jm060522a