In silico discovery of DNA methyltransferase inhibitors.

Angélica M. González-Sánchez[1][2], Khrystall K. Ramos-Callejas[1][2] , Adriana O. Diaz-Quiñones[2] and Héctor M. Maldonado, Ph.D.[3].

[1]RISE students [2]University of Puerto Rico at Cayey [3] Pharmacology Department UCC, Medical School

______________________________________________________________________AbstractDNA Methyltransferases are a type of transferase enzymes that add methyl groups to cyto-sine bases in newly replicated DNA. In mammals this process is necessary for a normal de-velopment of cell’s functions as well as for growth of the organism. Recent studies have shown that, under pathological conditions, there is a close relationship between the methy-lation of tumor suppressor genes and cancer development. This project, which derives from a previous research made by the In silico drug discovery team, was therefore intended to identify specific, high-affinity inhibitors for the DNA Methyltransferase by using an In sil-ico approach. We used several databases and software that allowed us to identify potential new targets in DNA Methyltransferase, to create two pharmacophore models for the identi-fied target and to identify compounds from a database that suited both the size of the target and the features of the model. A total of 182 compounds were obtained in this study with predicted binding energies of more than -9.7 kilocalories per mole. These results are quite significant given the relatively small portion of the database that was evaluated. Therefore, the pharmacophore model that allowed identifying the compounds with the highest bind-ing energies, which was Model 2, will be refined further on.

Keywords: DNA methyltransferase/ methyl group/ In silico/ pharmacophore model/ bind-ing energy.

IntroductionMethyltransferases are a type of

transferase enzyme that transfers a methyl group from a donor molecule to an acceptor. A methyl group is composed of one carbon atom bonded to 3 hydrogen atoms (refer to Figure 1). It is the group that the methyltransferase transfers. By transferring this methyl group from one molecule to another, the methyltrans-ferase is in charge of catalyzing certain re-actions in the body. The transfer of this methyl group from one compound to an-

other is called methylation. In living or-ganisms it mainly occurs in reactions re-lated to the DNA or to proteins. That’s why methylation most often takes place in the nucleic bases in DNA or in amino acids in protein structures.

To function as a methyl group transporter, the methyltransferase carries with itself a compound named S-adeno-

Figure 1: Chemi-cal structure of a Methyl group

In Silico discovery of DNA methyltransferase inhibitors.

sylmethionine, also called SAM, which functions as a methyl donor (Malygin and Hattman, 2012). This donation occurs due to the fact that SAM has a sulfur atom bound to a reactive methyl group that is willing to break off and react (refer to Fig-ure 2).

There are several types of methyl-transferases (Fandy, 2009). For this par-ticular research we decided to focus on DNA’s methyltransferase. DNA methyl-transferase also has several subtypes, from which we chose the DNA methyl-transferase 1, or DNMT1 (refer to Figure 3). This one is in charge of adding methyl groups to cytosine bases in newly repli-cated DNA (Fandy, 2009). This has sev-eral implications. In order for a cell to be capable of doing a specific function it must encode certain genes to produce specific proteins. For this process, methy-lation of the DNA is essential because it adds methyl groups to genes in the DNA, shutting off some and activating others (Goodsell, 2011). In order for cell’s speci-ficity to be maintained, methyltrans-ferases have to methylate DNA strands so that this genetic information will be transmitted as DNA replicates. Therefore, the methyl groups that are added to the DNA strands are important to modify how DNA bases are read during protein syn-thesis and to control expression of genes

in different types of cells (Goodsell, 2011).

In humans, as in other mammals, a normal regulation of DNA Methyltrans-ferases in the cells is essential for embry-onic development, as well as for other processes of growth (Goodsell, 2011). However, in cancer cells, DNA methyl-transferases have been shown to be over-produced, to work faster and to function at greater rates (Perry et al., 2010). A link has also been found between the methyla-tion of the tumor suppressor genes and tumorigenesis, which is the process by which normal cells are transformed into cancer cells, as well as with metastasis, which is the process by which cancer cells spread from one organ to another. This means that the methylation of these tu-mor suppressor genes promotes cancer development (Chik and Szyf, 2010).

Given this, it has been decided to investigate about a way of finding specific inhibitors to decrease this type of methy-lation that can lead to cancer develop-ment. That’s the reason why we have de-rived the hypothesis that specific, high-

May 2012. 2

Figure 2: Chemical structure of the methyl donor S-adenosylmethionine.

Figure 3: Struc-ture of human

DNMT1 (residues 600-1600) in complex with

Sinefungin.

Pdb: 3SWR

In Silico discovery of DNA methyltransferase inhibitors.

affinity inhibitors of DNA methyltrans-ferase (DNMT1) can be identified via an In Silico approach.

Materials and MethodsIn order to reach our objectives

and test our hypothesis, we followed an In silico approach. Therefore, our materials were mainly databases and software that will be described further on. First, the structure of the methyltransferase DNMT1 was downloaded from the data-base www.pdb.org by entering the acces-sion code of the desired protein (3SWR.pdb). The structure of the DNMT1 was then opened with the software Py-MOL Molecular Grpahics System v1.3 (www.pymol.org). There, the protein was cleaned from drugs and water molecules that were not useful for this study (refer to Figure 4).

Further on, by using the software AutoDock (protein docking software) we were able to make a grid and configura-tion file, that allowed us to identify a po-

tential new target (or site of interaction) in that protein. For this, a compound that was downloaded with the structure of the protein, called Sinefungin, was very useful because it served as a guide to identify where there is more probability of inter-action of that protein with other com-pounds. Then, by using the server NanoBio and the software AutoDock Vina we started to make a benzene mapping by identifying benzenes that had a high bind-ing energy in their interaction with the protein. From this benzene mapping we were supposed to develop a pharma-cophore model, but by recommendation of our mentor, we decided to develop it by using a different strategy. Therefore, we took 2 compounds that have already been studied in a research made by the In silico drug discovery team about Dengue’s Methyltransferase (refer to Figure 5). In that previous research these compounds showed a great binding energy with the DNA Methyltransferase. Two pharma-cophore models were created by combin-ing the most prominent features of those two compounds. For the generation of this model we took advantage of the unique features of the software Ligand-Scout (www.inteligand.com). We came up with two pharmacophore models that are hybrids of the two compounds previ-ously identified and which have 3 basic features: hydrophobic centroids, an aro-matic ring and exclusion volumes (refer to Figure 6).

Those two pharmacophore models generated were then used to "filter" rela-tively large databases of small chemical

May 2012. 3

Figure 4: Clean structure of the DMNT1 (pdb: 3SWR)

In Silico discovery of DNA methyltransferase inhibitors.

compounds (drug-like or lead-like) by us-ing the Terminal of the server NanoBio and LigandScout. A smaller database with

the compounds presenting characteristics imposed by the model was generated. Therefore, the developed pharmacophore models helped to reduce significantly the results of compounds from the database to be evaluated. This smaller database of compounds was screened by docking analysis against the originally selected target. This docking analysis consisted of separating the smaller filtered database into files of individual drugs to then be able to observe the characteristics of each drug, including their affinity with the pro-tein. This was also done by using Ligand-Scout. Further on, results were combined and ranked according to predicted bind-ing energies, from the greatest affinity to the weakest one. From this, drugs with the greatest affinity, also called potential top-hits, were identified. Finally, results were analyzed by observing the interac-tions of each of the top hit drugs with the protein and identifying which sites of in-teraction, or features, were more com-mon, whether the ones of Model 1 or the ones of Model 2. These results will also be used for further refinement of the pharmacophore model.

ResultsLead-like compounds are mole-

cules that serve as the starting point for the development of a drug, typically by variations in structure for optimal effi-cacy. From a database of about 1.7 mil-lion lead-like compounds we evaluated more than 150,000 of them, divided into 5 pieces of the database, each one with more than twenty five thousand drugs. Twenty-seven thousand two hundred and eighty four drugs which were suitable with the features of the first model were obtained. The average binding energy for these drugs in the first hundred top hits was 9.86 kilocalories per mole. On the other hand, we also acquired thirty-nine thousand five hundred and thirty-five drugs that suited the features of the sec-ond model. The average binding energy for the first hundred top hits of this model was 9.94 kilocalories per mole. This is quite significant for a relatively small piece of the database evaluated. A total of 182 compounds with predicted binding energies equal or higher than -9.7 kilo-calories per mol were found between the two models used in this pilot project (re-fer to Figure 7).

May 2012. 4

Figure 5: Compounds that showed great affinity with the DNA Methyltransferase on a previous

Dengue’s Methyltransferase research.

Figure 6: The two generated pharmacophore models.

In Silico discovery of DNA methyltransferase inhibitors.

Along with the great binding ener-gies that these models evidenced, there was also a very significant finding that demonstrated that 27% of the chosen drugs fulfilled requirements of both mod-els. These results are outstanding in terms of the drugs’ affinity for the methyl-transferase, which was higher mostly on drugs from the second model (refer to Figure 8).

DiscussionFrom these results we are able to

develop several conclusions. First of all, we generated two Pharmacophore mod-els by using information obtained from the interaction of two previously identi-fied compounds with the DNA methyl-transferase as target. This pharma-cophore models allowed us to identify compounds that had a significant interac-tion with the DNA methyltransferase 1. Also, from analysis of the results and ranking of predicted top-hits, it can be concluded that results obtained by Model

2 are superior to the results obtained with Model 1. This is because they show higher affinity with the protein and also because many drugs identified by the first model resulted to be suitable with the second one as well. Although close to

27% of the compounds obtained where selected by both models, a significant number of compounds with predicted high binding energies were also obtained with Model 1. Therefore, it can be con-cluded that Model 1 was noteworthy as well. As a whole, we proved our hypothe-

May 2012. 5

Figure 7: Distribution of selected compounds with predicted binding energies equal or higher

than -9.7 kcal/mol.

In Silico discovery of DNA methyltransferase inhibitors.

sis because we demonstrated that by us-ing an In Silico approach we were able to identify several drugs, which are potential candidates for the development of a spe-cific, high affinity inhibitor of DNA Methyltransferase.

Furthermore, the acquired results will definitely be useful for future studies. On these future studies, the In silico drug discovery team will complete the analysis of the interactions between the top-hits and the target and evaluate the possibility of refining the pharmacophore model. The sample of the evaluated compound database should also be broaden to in-clude a larger number of drugs. The goal would be to evaluate 1.7 million lead-like compounds, which represent the whole database. After several refinements of the model along with their respective screen-ings we should identify top-hits and test a group of these compounds in a bioassay.

AcknowledgementsWe would like to acknowledge the

great contribution of our mentor Dr. Hec-tor Maldonado, our student assistant Adriana Diaz and the whole In Silico drug

discovery team for guiding us in this in-credible journey. We would also like to thank the RISE Program for giving us the opportunity of participating in this re-search experience.

Literature Cited

Chik F, Szyf M. 2010. Effects of specific DMNT gene depletion on cancer cell trans-formation and breast cancer cell invasion; toward selective DMNT inhibitors. Carcino-genesis. 32(2):224-232.

Fandy T. 2009. Development of DNA Methyltransferase Inhibitors for the Treat-ment of Neoplastic Diseases. Current Medicinal Chemistry. 16(17):2075-2085.

Goodsell, D. 2011. Molecule of the month: DNA Methyltransferases. RCBS Protein DataBank.http://www.pdb.org/pdb/101/motm.do?momID=139

Malygin EG, Hattman S. 2012. DNA methyltransferases: mechanistic models de-rived from kinetic analysis. Critical reviews in Biochemistry and Molecular Biology.

Perry A, Watson W, Lawler M, Hollywood D. 2010. The epigenome as a therapeutic tar-get in prostate cancer. Nature Reviews on Urology. 7(1):668-680.

May 2012. 6