Abstract
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.
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Introduction
Epigenetic modifications through DNA methylation are essential for the regulation of genome structure and gene expression1,2. This process involves the addition of a methyl group at the C5 position of cytosine to produce 5-methylcytosine; nearly 80% of cytosines in CpG islands, which are composed of numerous CG sequences, undergo methylation3,4. The DNA methyltransferase (DNMT) family catalyzes DNA methylation using the methyl group provided by S-adenosyl-l-methionine5. Two families of DNMTs have been described: DNMT1 is responsible for the maintenance of methylation during DNA replication, and DNMT3A and DNMT3B perform de novo DNA methylation6,7. Hypermethylation of CpG islands in certain promoter sequences results in the inactivation of tumor suppressor genes and is associated with tumor progression8,9. As DNA hypermethylation is common in cancer cells, including excessive methylation of certain tumor suppressor genes, the suppression of DNA hypermethylation via the inhibition of DNMTs may be a useful strategy for the development of anticancer drugs10,11,12.
The DNMT-targeting inhibitors, 5-azacytidine (azacitidine) and 5-aza-2ʹ-deoxycytidine (decitabine), are nucleoside analog FDA-approved inhibitors. However, their selectivity and bioavailability are limited11. Considering that drug selectivity is crucial in compound development to mitigate side effects13,14, several nucleoside analogs and non-nucleoside compounds with various scaffolds have been identified and assessed for their DNMT selectivity15,16,17,18. Achieving selectivity for each DNMT represents a significant challenge due to the structural conservation of active sites within the DNMT family (Fig. S1). Lamiable-Oulaidi F et al. synthesized a nucleoside analog inhibitor that does not inhibit DNMT3b at 100 μM, while exhibiting an IC50 value of 7.2 ± 2.2 μM against DNMT119. Halby et al. designed a non-nucleotide mimetic compound (Fig. 1) based on the substrates S-adenosyl-l-methionine and deoxycytidine. This compound consists of four chemical entities—quinoline, quinazoline, connecting linker, and biphenyl groups and shows 100-fold selectivity (EC50) for DNMT3A compared to DNMT1 (DNMT3A: 1.1 ± 1.2 µM, DNMT1: 100 ± 3 µM)20. However, the mechanism of selectivity for each DNMT by these inhibitors is unclear.
Chemical structure of 4-([1,1′-Biphenyl]-4-ylmethylamino)-7-((1-(2-(quinolin-4-ylamino)ethyl) piperidin-4-yl)methoxy) quinazoline and the substrates SAH and cytosine in DNMT3A. (A) Chemical Structure of the DNMT3A-selective inhibitor. Color boxes represent the constituents of fragment units. With LigPrep, the nitrogen in the linker and quinoline group is protonated. (B) Binding pose of SAH and cytosine in the X-ray structure (PDB ID: 6F57). SAH and cytosine are colored similarly to the mimic in the inhibitor (quinazoline and quinoline group, respectively).
Elucidation of complex structures provides insights into the binding mode, and identification of the mechanism of selectivity is crucial for rational design of DNMT family inhibitors. Computational methods, such as docking and molecular dynamics (MD) simulations, are also efficient tools for drug discovery and design21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41. Docking simulations can predict compound-binding poses and estimate the fit of a compound in the binding pocket of a target protein27,28. MD simulations are used to analyze the atomic-level dynamics of biopolymers in solution based on Newton’s equations of motion and can predict the function of proteins and stability of binding molecules29,30,31,32. Such MD simulations have been used in research on the DNMT family33,34,35,36,37,38. While docking poses have been predicted in published research, the validity of docking poses, binding free energies, and the contribution of interactions with amino acid residues can be evaluated by applying MD simulations to docking poses. Therefore, applying these simulation techniques to inhibitors will identify unique amino acid residue interactions required for selectivity and inform the design of various scaffolds39,40,41.
In the present study, we focused on a DNMT3A-selective inhibitor and performed docking and MD simulations to predict the structure of the selective inhibitor–DNMT3A complex. Using binding free energy calculations, structural comparisons of DNMT3A and DNMT1, and residue scanning calculations, we aimed to determine the key residue of DNMT3A responsible for the selectivity of the inhibitor.
Methods
Protein and ligand preparation
Protein and ligand three-dimensional (3D) structures were prepared as follows: The 3D structure of DNMT3A was accessed from the Protein Data Bank (PDB ID: 6F57)42. DNA and S-adenosyl-homocysteine were removed from the structure and missing residues in the PDB file were complemented using the protein preparation module in Maestro43. The structure of the selective inhibitor was previously published by Halby et al. (Fig. 1A)20. The 3D structure and ionized state were prepared using the LigPrep module in Maestro43. The appropriate ionization state of the inhibitor was generated at pH 7.0 with Epik (Fig. 1)44. The OPLS3e force field was used for protein and ligand preparation45.
Docking simulation
The selective inhibitor was docked into the catalytic site of DNMT3A using DOCK 6.946. We selected docking spheres within 6 Å of SAH and cytosine in the DNMT3A crystal structure, and the docking grid space was generated around 7 Å from these spheres with grid spacing set to 0.3 Å. With flexible ligand docking, Grid Score was set as the primary scoring to restrict the generated poses, and the Hawkins GB/SA Score was set as the secondary scoring to re-rank these poses with a high Grid Score. The top five poses with the best Hawkins GB/SA scores were used for the initial structure in the MD simulations.
MD simulation
To perform the MD simulations, a system of initial structures from the docking simulation was prepared. For each docking conformation, the RESP charge of the selective inhibitor was calculated using the HF/6-31G in Gaussian 1647. The charge parameter was generated using the antechamber module in AmberTools48. The complex structure was placed in a 120 Å long cubic box, which was filled with water molecules. Cl– ions were added to the box to neutralize the total charge of the system. The system was generated using the tLEaP module in the Amber biomolecular simulation programs48. FF14SB, General Amber Force Field (GAFF), and TIP3P were used as force field parameters for the protein, ligand, and water molecules, respectively49,50,51. The initial structures were subjected to energy minimization, NVT equilibration, and NPT equilibration. For energy minimization, every 200 steps were applied with and without positional restraints (10 kcal/mol/Å2) on the heavy solute atoms. After minimization, NVT equilibration with V-rescaling was performed at 300 K for 200 ps with position restraints (10 kcal/mol/Å2)52. NPT equilibration with a Berendsen barostat was performed at 300 K and 1 bar for 800 ps53. In NPT equilibration, position restraints were gradually reduced to 0 kcal/mol/Å2. The constraint algorithm and time step were set to LINCS and 2 fs, respectively54. MD simulations after energy minimization were performed using GROMACS 2021.555. We performed 200 ns MD simulations five times at different initial velocities under the NPT ensemble to relax the complex structure and assess the inflexibility and stability of each pose. The snapshot recording interval was set at 100 ps. The trajectories were fitted to the Cα atom in the initial step of the production run for each run.
To identify the most inflexible and stable docking pose, we calculated the root-mean-square deviation (RMSD) and binding free energy using the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method56. The reference structure for the RMSD calculation was used as the initial structure of the 200 ns production run. The RMSD of the heavy atoms of the inhibitor was calculated by superimposing the Cα atoms in the protein. For the trajectories of complex structures with an inflexible binding pose, the binding free energy (ΔGbind) was calculated using gmx_MMPBSA57 by summing the four terms: ΔEbonded, ΔEnonbonded, ΔGpolar, and ΔGnonpolar. Each term represents the bonded energy, nonbonded energy such as electrostatic and van der Waals, solvation energy for Poisson − Boltzmann models, and nonpolar constituent modeled as linearly proportional to the solvent-accessible surface area (SASA). The binding free energy was calculated using 100 snapshots recorded during the last 100 ns of each trajectory. The most inflexible and stable binding pose was predicted based on the RMSD and binding free energy results.
We identified the key residue responsible for selectivity between DNMT3A and the inhibitor in several stages. First, we analyzed the energy decomposition of residues within 6 Å of the selective inhibitor, based on the binding free energy results. Subsequently, we performed a structural alignment of DNMT3A and DNMT1 to assess the presence of different side chains for the residues with a high contribution to binding. We performed residue scanning calculations using BioLuminate for residues with a different side chain in the corresponding position in DNMT158. The representative structure for residue scanning was selected from the clusters of the inhibitor’s conformation recorded in the last 100 ns of five MD simulation runs. Clustering was performed using the GROMOS method in GROMACS. In the representative structure, amino acid mutations with overlapping positions in DNMT1 were added to the candidate residues responsible for inhibitor selectivity. ΔGbind for the selective inhibitor was performed using Prime MM-GBSA59, and the difference between post- and pre-mutation ΔGbind was calculated as ΔΔGbind.
Results and discussion
Docking simulation
The five complex structures according to the Hawkins GB/SA Score are shown in Fig. 2A–E and Table 1. No. 1 has a score of – 58.16 kcal/mol, a difference of more than 5 kcal/mol compared to the other poses. Although the score difference between Nos. 2, 3, 4, and 5 was less than 1.0 kcal/mol, the positions of the four groups in the selective inhibitor were at different locations. Figure 1B shows the binding pose of SAH and cytosine in the X-ray structure (PDB ID: 6F57). All docking poses were placed around the SAH and cytosine positions, indicating that these positions covered the catalytic site in DNMT3A. In Nos. 2 and 5 of the docking poses, the quinazoline and quinoline groups were positioned along the SAH and cytosine positions, respectively, conforming to the compound design concept proposed by Halby et al.20.
Top five docking poses based on Hawkins GB/SA scoring. (A)–(E) The four chemical groups of the inhibitor in the model are indicated by different colors (see Fig. 1).
MD simulation
Figure S1 shows RMSD of the Cα atom in the protein. All RMSD values of the Cα atom were lower than 4 Å, indicating that no arbitrary conformational change occurred in the simulations. The RMSD of the heavy atoms in the inhibitor is shown in Fig. 3. The RMSD values of structure Nos. 1, 3, and 4 were higher than those of the other docking poses (Fig. 3A,C,D). An RMSD greater than 15 Å indicates that the pose of the inhibitor has high fluctuation and significantly differs from the initial structure. Most trajectories of Nos. 2 and 5 had lower fluctuations in RMSD compared with those of the other poses, although the RMSD of run 1 in No. 5 diverged after 175 ns (Fig. 3B,E). These results suggest that the poses of Nos. 2 and 5 maintained binding to DNMT3A. For the inflexible complex structures of Nos. 2 and 5, stability was analyzed by performing binding free energy calculations using the MM/PBSA method. Table 2 shows the results of the binding free energy calculation for each production run for Nos. 2 and 5. Averaged ΔGbind of Nos. 2 and 5 were – 28.95 kcal/mol and – 23.96 kcal/mol, respectively. Considering the standard deviation in each run, the binding free energies showed little difference between these poses; the binding poses of the last MD simulations were comparable (Figs. 4, S2, S3). In particular, while the biphenyl group of the inhibitor of Nos. 2 and 5 were located opposite to the initial structure, they were oriented in the same direction in the production run. These results suggest that binding poses of Nos. 2 and 5 are the most inflexible and stable. In particular, the biphenyl group of the inhibitor was stably positioned, as in No. 5.
RMSD results of each MD simulation run for each complex structure. (A)–(E) RMSD calculation based on the heavy atom of the inhibitor in the initial step of the production run. The orange, blue, gray, green, and yellow lines represent the RMSD of MD simulation runs 1, 2, 3, 4, and 5, respectively.
MD models of binding poses with the DNMT3A-selective inhibitor in No. 2. The trajectories of run 3 were used as the representative structure for each docking pose of No. 2. The initial structure of the production run is shown at 0 ns. The binding pose is shown as grayed lines and the docking pose of No. 5 is shown as magenta lines. The four chemical groups of the inhibitor in the model are indicated by different colors (see Fig. 1).
Prediction of residues responsible for the selectivity of the inhibitor
For the binding pose of No. 5, which showed little difference between the initial structure and MD snapshots, we analyzed the binding free energy contribution of the amino acid residues of DNMT3A. Figure S4 shows the energy contribution of residues within 6 Å of the selective inhibitor in the initial structure. Arg790 and Arg792 had high decomposition values of 2.121 and 3.242 kcal/mol, respectively. These residues destabilize the binding between DNMT3A and the selective inhibitor. We identified six residues with decomposition values lower than – 1 kcal/mol (Table 3, Fig. S4), which should have a high contribution to the binding of DNMT3A to the selective inhibitor. The types of these residues were compared to those of the corresponding position in DNMT1, and residues with different side chains between DNMT1 and DNMT3A were identified based on ΔΔGbind (Table 3). The positions of these residues in DNMT3A and DNMT1 are shown in Fig. 5A,B. Although Leu730, Phe640, and Pro709 had low decomposition values, the corresponding residues in DNMT1 are the same type as those in DNMT3A; therefore, these residues were not associated with selectivity. Val665 had the highest contribution to the binding free energy, with a value of –2.437 kcal/mol. The corresponding residue in DNMT1 was Met1169, and the ΔΔGbind of Val665Met was − 4.136 kcal/mol, indicating that Met has a higher affinity than Val for binding. Val687 had the second-highest contribution to the binding free energy, with a value of – 1.441 kcal/mol. The corresponding residue in DNMT1 was Cys1191, and the ΔΔGbind of Val687Cys was 0.044 kcal/mol, indicating that Val687 had little effect on the selectivity of the inhibitor. Arg688 had the third-highest contribution to the binding free energy, with a value of – 1.440 kcal/mol. The corresponding residue in DNMT1 was Asn1192; the ΔΔGbind of Arg688Asn was 8.583 kcal/mol, indicating a decrease in binding affinity associated with the mutation of the Asn residue. This suggests that Arg688 in DNMT3A is the key residue influencing the selectivity of the inhibitor for DNMT3A. Indeed, the biphenyl group of the selective inhibitor was stably positioned around Arg688 in MD simulations (Fig. 5C). Arg has a cationic side chain and has a higher affinity for the aromatic ring than Asn, which has a neutral side chain (Fig. 5D). Therefore, the interaction between Arg688 and the biphenyl group influences the affinity and selectivity between DNMT3A and the inhibitor.
Binding site comparison between DNMT3A and DNMT1. (A) 3D positions of the six residues in DNMT3A with high contributions to binding affinity. (B) 3D positions of the six residues in DNMT1. (C) Structure of the DNMT3A–inhibitor complex. Amino acid residues affecting the selectivity of the inhibitor are shown. The representative structure was selected from a cluster from 500 snapshots of the inhibitor’s conformation recorded at the last 100 ns in five MD simulation runs. (D) Representative structure of the inhibitor in complex with DNMT1 based on the structural alignment of DNMT1 and DNMT3A.
Comparison with previous structure–activity relationship study
As shown above, Arg688 in DNMT3A may be the key residue influencing the selectivity of the inhibitor toward DNMT3A. In the modeled complex structure, the biphenyl group of the selective inhibitor interacts with Arg688. Halby et al.20 identified several other compounds with high affinity to DNMT3A, in addition to the selective inhibitor utilized in our study. For example, EC50 values for DNMT3A of compounds 61, 62, 69, and 70 were 1.0 ± 0.4, 1.2 ± 0.3, 1.9 ± 1.2, and 0.3 ± 0.2 μM, respectively (Fig. 6). However, the DNMT3A selectivity of these compounds is insufficient, as the folding values are N.D., 21-, 8-, and 66-fold, respectively. We hypothesized that the insufficient selectivity of these compounds is due to the flexibility of these substituted groups. While the inhibitor selected for our study has only one carbon atom between the nitrogen atom in quinazoline group and the aromatic ring, other compounds have two or three atoms between these functional groups. This difference affects the flexibility of these substructures. Flexible substructures would stably place at the hydrophobic site around Leu730, avoiding being exposed to solvents. Leu730 is conserved in the DNMT1 and 3A, as shown in Table 3. Consequently, compounds with flexible substituted groups maintain high affinity for DNMT3A and potentially reduce DNMT3A selectivity. In contrast, the selective inhibitor chosen for our study has a relatively rigid substructure and can interact with Arg688, thereby exposing it to solvents. These structural insights into the relationships between substructure flexibility and DNMT3A selectivity would be beneficial for the rational design of new DNMT3A selective inhibitors.
Comparison of substituted groups with high affinity for DNMT3A. EC50 values of compounds are referred to the previous study by Halby et al.20. Each compound was shown using ChemSketch.
Conclusions
We predicted the inhibitor–DNMT3A complex structure, and the interactions and residues associated with the selectivity of the inhibitor for DNMT3A. Docking and MD simulations predicted that complex structure No. 5 had an inflexible RMSD and stable binding free energy. Structure No. 2 also showed inflexible RMSD and stable binding free energy, and the binding poses after MD simulations were similar to those of No. 5. Structural alignment analysis with known DNMT3A-containing substrates suggested that complex structures, such as Nos. 2 and 5, support the compound design concept of Halby et al.20 Amino acid decomposition analysis results showed that Arg688 contributed to the binding between DNMT3A and the inhibitor, which was supported by structural alignment and delta affinity analyses with DNMT1. Our findings using MD simulations could inform drug optimization procedures and support the development of new DNMT-selective inhibitors.
Data availability
All data generated and analyzed during the current study is available from the first and corresponding author on reasonable request.
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Acknowledgements
This work was supported in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI under grant number JP21K06094 and JP23K16987, Program for Promoting Researches on the Supercomputer Fugaku (Simulation and AI-driven next-generation medicine and drug discovery based on “Fugaku,” JPMXP1020230120) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), and the Research Support Project for Life Science and Drug Discovery (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP22ama121029j0002. MD simulations use the Cygnus computational resources provided by the Multidisciplinary Cooperative Research Program at the Center for Computational Sciences (Project Code: CADD) at the University of Tsukuba. G.K. acknowledges the support for Grant-in-Aid for JSPS Fellows (grant numbers 24KJ0483) from the JSPS.
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G.K., T.H., R.H., Y.S., T.H., and R.Y. conceived the study. G.K, T.H., and R.Y. implemented the simulations and analysis protocols. The manuscript was written with contributions from all authors. All the authors approved the final version of the manuscript.
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Kudo, G., Hirao, T., Harada, R. et al. Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation. Sci Rep 14, 13508 (2024). https://doi.org/10.1038/s41598-024-64236-9
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DOI: https://doi.org/10.1038/s41598-024-64236-9
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