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,               and to control ...
In Silico discovery of DNA methyltransferase inhibitors.affinity inhibitors of DNA methyltransfer-       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 supe...
In Silico discovery of DNA methyltransferase inhibitors.sis because we demonstrated that by us-           discovery team f...
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Angelica and khrystall written report research project

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Angelica and khrystall written report research project

  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] 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 haveshown that, under pathological conditions, there is a close relationship between the meth-ylation of tumor suppressor genes and cancer development. This project, which derivesfrom a previous research made by the In silico drug discovery team, was therefore intendedto identify specific, high-affinity inhibitors for the DNA Methyltransferase by using an Insilico approach. We used several databases and software that allowed us to identify poten-tial new targets in DNA Methyltransferase, to create two pharmacophore models for theidentified target and to identify compounds from a database that suited both the size of thetarget and the features of the model. A total of 182 compounds were obtained in this studywith predicted binding energies of more than -9.7 kilocalories per mole. These results arequite significant given the relatively small portion of the database that was evaluated.Therefore, the pharmacophore model that allowed identifying the compounds with thehighest binding energies, which was Model 2, will be refined further on.Keywords: DNA methyltransferase/ methyl group/ In silico/ pharmacophore model/ bind-ing energy.Introduction other is called methylation. In living or- Methyltransferases are a type of ganisms it mainly occurs in reactions re-transferase enzyme that transfers a me- lated 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 methyltransfer-ase is in charge of catalyzing certain reac- To function as a methyl grouptions in the body. The transfer of this transporter, the methyltransferase carriesmethyl group from one compound to an- with itself a compound named S-
  2. 2. In Silico discovery of DNA methyltransferase inhibitors.adenosylmethionine, also called SAM, and to control expression of genes in dif-which functions as a methyl donor ferent types of cells (Goodsell, 2011).(Malygin and Hattman, 2012). This dona- In humans, as in other mammals, ation occurs due to the fact that SAM has a normal regulation of DNA Methyltrans-sulfur atom bound to a reactive methyl ferases in the cells is essential for embry-group that is willing to break off and react onic development, as well as for other(refer to Figure 2). processes of growth (Goodsell, 2011). Figure 2: Chemical structure of the methyl do- However, in cancer cells, DNA methyl- nor S-adenosylmethionine. 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 There are several types of methyl- tumorigenesis, which is the process bytransferases (Fandy, 2009). For this par- which normal cells are transformed intoticular research we decided to focus on cancer cells, as well as with metastasis,DNA’s methyltransferase. DNA methyl- which is the process by which cancer cellstransferase also has several subtypes, spread from one organ to another. Thisfrom which we chose the DNA methyl- means that the methylation of these tu-transferase 1, or DNMT1 (refer to Figure mor 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 sever- Figure 3: Struc-al implications. In order for a cell to be ture of humancapable of doing a specific function it DNMT1 (residues 600-1600) inmust encode certain genes to produce complex withspecific proteins. For this process, meth- Sinefungin.ylation of the DNA is essential because itadds methyl groups to genes in the DNA, Pdb: 3SWRshutting off some and activating others(Goodsell, 2011). In order for cell’s speci-ficity to be maintained, methyltransferas-es have to methylate DNA strands so thatthis genetic information will be transmit- Given this, it has been decided toted as DNA replicates. Therefore, the me- investigate about a way of finding specificthyl groups that are added to the DNA inhibitors to decrease this type of methyl-strands are important to modify how DNA ation that can lead to cancer develop-bases are read during protein synthesis 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 methyltransfer- tential new target (or site of interaction)ase (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 inter-and test our hypothesis, we followed an In action 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 AutoDock Vinawill 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 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 our mentor, we decided to develop it byPyMOL Molecular Grpahics System v1.3 using a different strategy. Therefore, we( 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 models were 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 LigandScout ( We came up with two pharmacophore models that are hy- brids of the two compounds previously identified and which have 3 basic fea- tures: hydrophobic centroids, an aromatic ring and exclusion volumes (refer to Fig- ure 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 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 reduce significantly 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. Twen-compounds was screened by docking ty-seven thousand two hundred andanalysis against the originally selected eighty four drugs which were suitabletarget. This docking analysis consisted of with the features of the first model wereseparating the smaller filtered database obtained. The average binding energy forinto files of individual drugs to then be these drugs in the first hundred top hitsable to observe the characteristics of each was 9.86 kilocalories per mole. On thedrug, including their affinity with the pro- other hand, we also acquired thirty-ninetein. This was also done by using Lig- thousand five hundred and thirty-fiveandScout. Further on, results were drugs that suited the features of the se-combined and ranked according to pre- cond model. The average binding energydicted binding energies, from the greatest for the first hundred top hits of this modelaffinity to the weakest one. From this, was 9.94 kilocalories per mole. This isdrugs 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 kilocal-drugs with the protein and identifying ories per mol were found between thewhich sites of interaction, or features, two models used in this pilot project (re-were more common, whether the ones of fer to Figure 7).Model 1 or the ones of Model 2. Theseresults will also be used for further re-finement 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 high- tained with Model 1. This is because they er 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 methyltransfer- high binding energies were also obtainedase 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. Fandy T. 2009. Development of DNA Me-The sample of the evaluated compound thyltransferase Inhibitors for the Treatment of Neoplastic Diseases. Current Medicinaldatabase should also be broaden to in- Chemistry. 16(17):2075-2085.clude a larger number of drugs. The goal Goodsell, D. 2011. Molecule of the month:would be to evaluate 1.7 million lead-like DNA Methyltransferases. RCBS Proteincompounds, which represent the whole Data-database. After several refinements of the Bank. along with their respective screen- ?momID=139ings we should identify top-hits and test a Malygin EG, Hattman S. 2012. DNA me-group of these compounds in a bioassay. thyltransferases: 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