In silico discovery of DNA methyltransferase inhibitors.Angélica M. González-Sánchez[1][2], Khrystall K. Ramos-Callejas[1]...
In Silico discovery of DNA methyltransferase inhibitors.adenosylmethionine, also called SAM,               trol expression...
In Silico discovery of DNA methyltransferase inhibitors.affinity inhibitors of DNA methyltransfe-        tential new targe...
In Silico discovery of DNA methyltransferase inhibitors.compounds (drug-like or lead-like) by us-         Figure 6: The tw...
In Silico discovery of DNA methyltransferase inhibitors.                                                   Model 2 are sup...
In Silico discovery of DNA methyltransferase inhibitors.sis because we demonstrated that by us-           discovery team f...
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In silico discovery of dna methyltransferase inhibitors (1)

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In silico discovery of dna methyltransferase inhibitors (1)

  1. 1. In silico discovery of DNA methyltransferase inhibitors.Angélica M. González-Sánchez[1][2], Khrystall K. Ramos-Callejas[1][2] , Adriana O. Diaz-Quiñones[2] andHéctor 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 haveshown that,under pathological conditions,there is a close relationship between the methy-lation of tumor suppressor genes and cancer development. Thisproject, which derives froma previous research made by the In silico drug discovery team, was therefore intended toidentify specific, high-affinity inhibitors for the DNA Methyltransferase by using an In silicoapproach. We used several databases and software that allowed us to identify potentialnew 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 targetand the features of the model. A total of 182 compounds were obtained in this studywithpredicted binding energies of more than -9.7 kilocalories per mole. These results are quitesignificant 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 refinedfurther on.Keywords: DNA methyltransferase/ methyl group/ In silico/ pharmacophore model/ bind-ing energy.Introduction another is called methylation. In living Methyltransferases are a type of organisms it mainly occurs in reactionstransferase enzyme that transfersa me- related to the DNA or to proteins. That’sthyl group from a donor molecule to an why methylation most often takes placeacceptor. A methyl group is composed of in the nucleic bases in DNA or in aminoone carbon atom bonded to 3 hydrogen acids in protein structures.atoms (refer to Figure 1). It is the group Figure 1: Chem-that the methyltransferase transfers. By ical structure oftransferring this methyl group from one a Methyl groupmolecule to another, the methyltransfe-rase is in charge of catalyzing certain To function as a methyl groupreactions in the body.The transfer of this transporter, the methyltransferasecarriesmethyl group from one compound to with itself a compound named S-
  2. 2. In Silico discovery of DNA methyltransferase inhibitors.adenosylmethionine, also called SAM, trol expression of genes in different typeswhich functions as a methyl donor (Maly- of cells(Goodsell, 2011).gin and Hattman, 2012).This donation oc- In humans, as in other mammals, acurs due to the fact that SAM has a sulfur normal regulation of DNA Methyltransfe-atom bound to a reactive methyl group rases in the cells is essential for embryo-that is willing to break off and react (refer nic development, as well as for otherto Figure 2). processes of growth(Goodsell,Figure 2: Chemical structure of the methyl donor 2011).However, in cancer cells, DNA me- S-adenosylmethionine. thyltransferases have been shown to be overproduced, to work faster and to func- tion at greater rates (Perry et al., 2010). A link has also been found between the me- thylation of the tumor suppressor genes There are several types of methyl- and tumorigenesis, which is the processtransferases(Fandy, 2009). For this par- by which normal cells are transformedticular research we decided to focus on into cancer cells, as well as with metasta-DNA’s methyltransferase. DNA methyl- sis, which is the process by which cancertransferase also has several subtypes, cells spread from one organ to another.from which we chose the DNA methyl- This means that the methylation of thesetransferase 1, or DNMT1 (refer to Figure tumor suppressor genes promotes cancer3). This one is in charge of adding methyl development (Chik and Szyf, 2010).groups to cytosine bases in newly repli-cated DNA(Fandy, 2009). This has several Figure 3: Struc-implications. In order for a cell to be ca- ture of humanpable of doing a specific function it must DNMT1 (residues 600-1600) inencode certain genes to produce specific complex withproteins. For this process, methylation of Sinefungin.the DNA is essential because it adds me-thyl groups to genes in the DNA, shutting Pdb: 3SWRoff some and activating others (Goodsell,2011). In order for cell’s specificity to bemaintained, methyltransferases have tomethylate DNA strands so that this genet-ic information will be transmitted as DNA Given this, it has been decided toreplicates.Therefore, the methyl groups investigate about a way of finding specificthat are added to the DNA strands are im- inhibitors to decrease this type of methy-portant to modify how DNA bases are lation that can lead to cancer develop-read during protein synthesis and to con- ment. That’s the reason why we have derived the hypothesis that specific, high-May 2012. 2
  3. 3. In Silico discovery of DNA methyltransferase inhibitors.affinity inhibitors of DNA methyltransfe- tential new target (or site of interaction)rase (DNMT1) can be identified via an In in that protein. For this, a compound thatSilico approach. was downloaded with the structure of the protein, called Sinefungin, was very usefulMaterials and Methods because it served as a guide to identify In order to reach our objectives where there is more probability of inte-and test our hypothesis, we followed an In raction of that protein with other com-silico approach. Therefore, our materials pounds. Then, by using the serverwere mainly databases and software that NanoBio and the software AutoDockVinawill be described further on. First, the we started to make a benzene mapping bystructure of the methyltransferase identifying benzenes that had a high bind-DNMT1 was downloaded from the data- ing energy in their interaction with thebase www.pdb.org by entering the acces- protein. From this benzene mapping wesion code of the desired protein were supposed to develop a pharmaco-(3SWR.pdb). The structure of the DNMT1 phore model, but by recommendation ofwas then opened with the software Py- our mentor, we decided to develop it byMOL Molecular Grpahics System v1.3 using a different strategy. Therefore,we(www.pymol.org). There, the protein was took 2 compounds that have already beencleaned from drugs and water molecules studied in a research made by the In silicothat were not useful for this study(refer drug discovery team about Dengue’s Me-to Figure 4). thyltransferase (refer to Figure 5).In that Figure 4: Clean structure of the DMNT1 previous research these compounds (pdb: 3SWR) showed a great binding energy with the DNA Methyltransferase. Two pharmaco- phore modelswere created by combining the most prominent features of those two compounds.For the generation of this model we took advantage of the unique features of the software Li- gandScout(www.inteligand.com). We came up with two pharmacophore models that are hybrids of the two compounds previously identified and which have 3 basic features: hydrophobic centroids, an aromatic ring and exclusion volumes (re- fer to Figure 6). Further on, by using the softwareAutoDock (protein docking software) we Those two pharmacophore modelswere able to make a grid and configura- generated were then used to "filter" rela-tion file, that allowed us to identify a po- tively large databases of small chemicalMay 2012. 3
  4. 4. In Silico discovery of DNA methyltransferase inhibitors.compounds (drug-like or lead-like) by us- Figure 6: The two generated pharmacophore models.ing the Terminal of the server NanoBioand LigandScout. A smaller database withFigure 5: Compounds that showed great affinity with the DNA Methyltransferase on a previous Dengue’s Methyltransferase research. Results Lead-like compounds are mole- cules that serve as the starting point for the development of a drug, typically bythe compounds presenting characteristics variations in structure for optimal effica-imposed by the model was generated. cy. From a database of about 1.7 millionTherefore, the developed pharmacophore lead-like compounds we evaluated moremodels helped to reducesignificantly the than 150,000 of them, divided into 5 piec-results of compounds from the database es of the database, each one with moreto be evaluated. This smaller database of than twenty five thousand drugs. Twenty-compounds was screened by docking seven thousand two hundred and eightyanalysis against the originally selected four drugs which were suitable with thetarget. This docking analysis consisted of features of the first model were obtained.separating the smaller filtered database The average binding energy for theseinto files of individual drugs to then be drugs in the first hundred top hits wasable to observe the characteristics of each 9.86 kilocalories per mole. On the otherdrug, including their affinity with the pro- hand, we also acquired thirty-nine thou-tein. This was also done by using Li- sand five hundred and thirty-five drugsgandScout. Further on, results that suited the features of the secondwerecombined and ranked according to model. The average binding energy forpredicted binding energies, from the the first hundred top hits of this modelgreatest affinity to the weakest one.From was 9.94 kilocalories per mole.This isthis, drugs with the greatest affinity, also quite significant for a relatively smallcalled potential top-hits,were identified. piece of the database evaluated.A total ofFinally, results were analyzed by observ- 182 compounds with predicted bindinging the interactions of each of the top hit energies equal or higher than -9.7 kiloca-drugs with the protein and identifying lories per mol were found between thewhich sites of interaction, or features, two models used in this pilotwere more common, whether the ones of project(refer to Figure 7).Model 1 or the ones of Model 2. These re-sults will also be used for further refine-ment of the pharmacophore model.May 2012. 4
  5. 5. In Silico discovery of DNA methyltransferase inhibitors. Model 2 are superior to the results ob- Figure 7: Distribution of selected compoundswith predicted binding energies equal or higher tained with Model 1.This is because they than -9.7 kcal/mol. 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 Along with the great binding ener-gies that these models evidenced, therewas also a very significant finding thatdemonstrated that 27% of the chosendrugs fulfilled requirements of both mod-els. These results are outstanding interms of the drugs’ affinity for the methyl-transferase, which was higher mostly ondrugs from the second model (refer toFigure 8).Discussion From these results we are able todevelop several conclusions. First of all,we generated two Pharmacophore mod-els by using information obtained fromthe interaction of two previously identi-fied compounds with the DNA methyl-transferase as target. This 27% of the compounds obtained wherepharmacophore models allowed us to selected by both models, a significantidentify compounds that had a significant number of compounds with predictedinteraction with the DNA methyltransfe- high binding energies were also obtainedrase 1. Also, from analysis of the results with Model 1. Therefore, it can be con-and ranking of predicted top-hits, it can cluded that Model 1 was noteworthy asbe concluded that results obtained by well. As a whole, we proved our hypothe-May 2012. 5
  6. 6. In Silico discovery of DNA methyltransferase inhibitors.sis because we demonstrated that by us- discovery team for guiding us in this in-ing an In Silico approach we were able to credible journey.We would also like toidentify several drugs, which are potential thank the RISE Program for giving us thecandidates for the development of a spe- opportunity of participating in this re-cific, high affinity inhibitor of DNA Me- search experience.thyltransferase. Furthermore, the acquired results Literature Citedwill definitely be useful for future studies. Chik F, Szyf M. 2010. Effects of specificOn these future studies, the In silico drug DMNT gene depletion on cancer cell trans-discovery team will complete the analysis formation and breast cancer cell invasion;of the interactions between the top-hits toward selective DMNT inhibitors. Carcino-and the target and evaluate the possibility genesis. 32(2):224-232.of refining the pharmacophore model.The Fandy T. 2009. Development of DNA Me-sample of the evaluated compound data- thyltransferase Inhibitors for the Treatment of Neoplastic Diseases. Current Medicinalbase should also be broaden to include a Chemistry. 16(17):2075-2085.larger number of drugs. The goal would Goodsell, D. 2011. Molecule of the month:be to evaluate 1.7 million lead-like com- DNA Methyltransferases. RCBS Protein Da-pounds, which represent the whole data- ta-base.After several refinements of the Bank.http://www.pdb.org/pdb/101/motm.domodel along with their respective screen- ?momID=139ings we should identify top-hits and test a Malygin EG, Hattman S. 2012.DNA methyl-group of these compounds in a bioassay. transferases: mechanistic models derived from kinetic analysis. Critical reviews inAcknowledgements Biochemistry and Molecular Biology. We would like to acknowledge the Perry A, Watson W, Lawler M, Hollywoodgreat contribution of our mentor Dr. Hec- D. 2010. The epigenome as a therapeutictor Maldonado, our student assistant target in prostate cancer. Nature Reviews onAdriana Diaz and the whole In Silico drug Urology. 7(1):668-680.May 2012. 6

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