Docking studies on select DNA methyltransferases with novel inhibitors to treat cancer Sonia Advani1, Saurabh Kumar Gautam2, Vandana Pathak3, Shweta Singh4, Vineeta Verma5 and Sushil Pal6 1
Department of Biotechnology Engineering, College of Engineering and Technology, IILM Academy of Higher Learning, Greater Noida, Uttar Pradesh, INDIA sonia.advani@iilm.ac.in www.iilm.ac.in
ABSTRACT Changes in the pattern of DNA methylation, either increased (hypermethylation) or decreased (hypomethylation), have been identified in all types of cancer cells examined so far. The reversibility of epigenetic modifications renders them attractive targets for therapeutic interventions. In contrast to genetic mutations, which are inherited passively through DNA replication, epigenetic mutations must be actively maintained because they are reversible. Consequently, pharmacologic inhibition of certain epigenetic modifications could correct faulty modification patterns and thus directly change gene expression patterns and the corresponding cellular characteristics The reversibility of epimutations by small-molecule inhibitors provides the foundation for the use of such inhibitors in novel cancer therapy strategies. Among the compounds that inhibit epigenetic processes, the most extensively studied are DNA methyltransferase inhibitors. DNA methyltransferase activity has an important role in tumor growth, and the activity of DNA methyltransferase inhibitors can be analyzed directly at the DNA level. DNA methyltransferase inhibitors are at a more clinically advanced stage of development having been extensively tested in phase I窶的II clinical trials. In this work, we have performed docking studies of three selected DNA methyltransferases with known inhibitors to predict the binding modes and affinities of the distinct inhibitors. Computational structure prediction of enzyme inhibitor complexes using the GOLD docking method was done in combination with emperical scoring function to predict ligand orientation in binding sites and binding affinity of inhibitor to enzyme.
Keywords-docking, cancer, methyletransferases,hypermethylation,tumor, DNA methylation , ligand
1. INTRODUCTION Epigenetic mechanisms regulate the expression of genetic information. Epigenetic modifications of DNA and histones are stable and heritable but are also reversible. They include covalent modifications of bases in the DNA and of amino acid residues in the histones. DNA methyltransferases are a family of enzymes that methylate DNA at the carbon-5 position of cytosine residues. Methylated DNA can then be bound by methyl-binding proteins that function as adaptors between methylated DNA and chromatin-modifying enzymes (e.g., histone deacetylases and histone methyltransferases) by recruiting histone-modifying enzymes to stretches of methylated DNA. Histone-modifying enzymes then covalently modify the amino-terminal residues of histones to induce the formation of chromatin structures that repress gene transcription. DNA methylation triggers chromatin reorganization that is mediated by methyl-binding proteins, it has a comparatively simple binary pattern (i.e., methylated versus nonmethylated bases) compared with the highly complex pattern of histone modifications, and it is particularly amenable to experimental analysis. Changes in the pattern of DNA methylation, either increased (hypermethylation) or decreased (hypomethylation), have been identified in all types of cancer cells examined so far. In addition, it has also been shown that genetic lesions in human cancer cells can promote epigenetic alterations. Genomic tumor DNA is generally characterized by distinct methylation changes that have also been termed epimutations. At the global level, the DNA is often hypomethylated, particularly at centromeric repeat sequences, and this hypomethylation has been linked to genomic instability. Another class of epimutations is characterized by the local hypermethylation of individual genes, which has been associated with aberrant gene silencing. Epimutations have been described in many types of cancer and appear to play an important role in tumorigenesis.
The reversibility of epigenetic modifications renders them attractive targets for therapeutic interventions. In contrast to genetic mutations, which are inherited passively through DNA replication, epigenetic mutations must be actively maintained. Consequently, pharmacologic inhibition of certain epigenetic modifications could correct faulty modification patterns and thus directly change gene expression patterns and the corresponding cellular characteristics. A goal of many biophysical studies is to determine the molecular forces that control biological interactions and to use this information to rationally manipulate protein function by modifying the protein, the interacting ligand, or both. The forces that control protein behavior and their physical chemical origins are inferred from equilibrium binding kinetic measurements or are calculated with molecular models. Calculated energies are used to identify the role of the physical and chemical interactions in protein function and behavior. Although detailed calculations are feasible for small molecules, such calculations become prohibitive as the size and complexity of the biological macromolecules increase. Time dependent forces between soft or mobile species add yet another degree of complexity, while static models of interactions do not describe the full range of parameters that influence biological behavior. [I][2][3]4]. Virtual ligand screening using in silico methods can provide prospective leads and is a practical alternative to high-throughput screening of large compound libraries provided the binding modes and affinities of the distinct ligands can be predicted correctly. The docking and scoring problems countered in this endeavor are central to the theory of bimolecular interactions and are ultimately determined by the nature of the underlying binding energy landscape. However, the desired synergy of adequate conformational sampling combined with accurate evaluation of energetics has been difficult to achieve with any computational model. Computational structure prediction of ligand-protein complexes using docking methods, like DOCK, FLEXx, and GOLD (Ewing et al., 2001; Jones et al., 1997; Kramer et al., 1999), in combination with empirical scoring functions are used to predict ligand orientations in binding sites and binding affinities of ligands to proteins. While the binding geometries depend on the docking methods, binding energy estimates rely heavily on the potential functions used to calculate them. Knowledge based potentials follow rules based on statistical analysis of binding affinities and geometries of experimentally determined protein-ligand complexes. These rules are converted to “pseudo-potentials� which are then applied to score computer generated ligand orientations (Gohlke et al., 2000; Muegge & Martin, 1999).
2. METHODOLOGY Three dimensional structures of DNA methyltransferases were retrieved from Protein Databank for studying the interaction with the two ligands SAH and sinefungin. Ligand explorer was used for finding the 3D structures of the ligands and to study the interaction of the methyltransferase with the ligand the structures were docked on Gold server to find Gold fitness for each of the interaction. They were visualized the combined structures. And then these structures were visualized in the 3D visualization software like pymol, rasmol and some of the views were rendered in the form of images. 2.1 Structural analysis of DNMTases Sequence searches of DNMTases were carried out using a locally online version of REBASE (URL: http://rebase.neb.com/rebase.html) [7]. The protein sequences of DNMTases under study [5] were retrieved in FASTA format using crystal structures available at REBASE (URL: http://rebase.neb.com/cgi-bin/crylist). [6] Protein structure prediction was carried out using (PS)2 server, a gateway for a variety of methods for making predictions and analyzing their results (URL: http://ps2.life.nctu.edu.tw/ ). [7] After generating a predicted model with no other refinements, the program PROCHECK was used to evaluate the quality of this model based on the G-factor. Finally, the predicted model was displayed by Chime and automatically sent to users. Three-dimensional models obtained for all MTases using RCSB ligand explorer were built and distance of inter-atomic interactions viz.hydrogen bonding, hydrophobic bonding and metallic interactions from neighboring amino acid residues was noted. By selecting the type of inter-atomic distance required, the distance was auto-computed and displayed on the screen pointing out at the ligand. The image assigned with the inter-atomic distances and dihedral angles was viewed in the ligand explorer, thus displaying the position and site of atomic interactions and their active site. 2.2 Docking analysis with Inhibitors The empirical parameters used in the Gold Fitness Function were obtained from the Gold parameter file. The empirical parameters were customized by copying the Gold parameter file, editing it and instructing Gold server to use the edited file.
The output files les contained a single internal energy term S(int) which is the sum of internal torsional energy terms and internal Van der Waals energy term. The fitness score was taken as the negative of the sum of the component energy terms facilitating large fitness scores. The external Van der Waals score was multiplied by a factor of 1.375 when the total fitness scores were computed.
2. RESULTS The crystal structures of three methyltransferases were downloaded from REBASE and three three-dimensional dimensional models obtained for above ove using RCSB ligand explorer were built. (Table I). Two inhibitors, SAH and sinefungin were used for docking studies with each enzyme. The output files contained a single internal energy term S(int) which is the sum of internal torsional energy terms and d internal Van der Waals energy term. The fitness score was taken as the negative of the sum of the component energy terms facilitating large fitness scores. The external Van der Waals score was multiplied by a factor of 1.375 when the total fitness scores were computed. computed
Table 1. GOLD docking results of the top 3 inhibitors for each docking and their GOLD fitness score score.
1-SAH 2-Sinefungin
3A1B 20.06 -9.39
Figure-1: 1: 3A1B with SAH
Figure-3: 3: 3AV6 with SAH
3AV6 56.67 60.18
Figure-2: 3A1B with Sinefungin
Figure-4: 3AV6 with Sinefungin
3Q87 42.97 41.16
Figure-5: 5: 3Q87 with SAH
Figure-6: 3Q87 with Sinefugin
Acknowledgements: We are thankful to Nancy Manchanda, M , Research scholor JNU,New Delhi
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5. KEY 1. 3A1B Molecule: DNA (cytosine-5)-methyltran methyltransferase 3A, Histone H3.1u 2. 3AV6 Molecule: (DNA (cytosine-5)-methyltransferase methyltransferase 1 3. 3Q87 Molecule: (Putative uncharacterized protein ECU08_1170)