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
______________________________________________________________________
Abstract
DNA 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. Thisproject, 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 silico
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 studywith
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 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 reactions
transferase enzyme that transfersa me- related to the DNA or to proteins. That’s
thyl group from a donor molecule to an why methylation most often takes place
acceptor. A methyl group is composed of in the nucleic bases in DNA or in amino
one 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 of
transferring this methyl group from one a Methyl group
molecule to another, the methyltransfe-
rase is in charge of catalyzing certain To function as a methyl group
reactions in the body.The transfer of this transporter, the methyltransferasecarries
methyl group from one compound to with itself a compound named S-

2. In Silico discovery of DNA methyltransferase inhibitors.
adenosylmethionine, also called SAM, trol expression of genes in different types
which functions as a methyl donor (Maly- of cells(Goodsell, 2011).
gin and Hattman, 2012).This donation oc-
In humans, as in other mammals, a
curs 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 other
to 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 process
transferases(Fandy, 2009). For this par- by which normal cells are transformed
ticular 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 cancer
transferase also has several subtypes, cells spread from one organ to another.
from which we chose the DNA methyl- This means that the methylation of these
transferase 1, or DNMT1 (refer to Figure tumor suppressor genes promotes cancer
3). 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 human
pable of doing a specific function it must DNMT1 (residues
600-1600) in
encode certain genes to produce specific
complex with
proteins. For this process, methylation of Sinefungin.
the DNA is essential because it adds me-
thyl groups to genes in the DNA, shutting Pdb: 3SWR
off some and activating others (Goodsell,
2011). In order for cell’s specificity to be
maintained, methyltransferases have to
methylate DNA strands so that this genet-
ic information will be transmitted as DNA Given this, it has been decided to
replicates.Therefore, the methyl groups investigate about a way of finding specific
that 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. 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 that
Silico approach. was downloaded with the structure of the
protein, called Sinefungin, was very useful
Materials 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 server
were mainly databases and software that
NanoBio and the software AutoDockVina
will be described further on. First, the
we started to make a benzene mapping by
structure of the methyltransferase
identifying benzenes that had a high bind-
DNMT1 was downloaded from the data-
ing energy in their interaction with the
base www.pdb.org by entering the acces-
protein. From this benzene mapping we
sion code of the desired protein
were supposed to develop a pharmaco-
(3SWR.pdb). The structure of the DNMT1
phore model, but by recommendation of
was then opened with the software Py-
our mentor, we decided to develop it by
MOL Molecular Grpahics System v1.3
using a different strategy. Therefore,we
(www.pymol.org). There, the protein was
took 2 compounds that have already been
cleaned from drugs and water molecules
studied in a research made by the In silico
that 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 software
AutoDock (protein docking software) we Those two pharmacophore models
were 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 chemical
May 2012. 3

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 NanoBio
and LigandScout. A smaller database with
Figure 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 by
the compounds presenting characteristics variations in structure for optimal effica-
imposed by the model was generated. cy. From a database of about 1.7 million
Therefore, the developed pharmacophore lead-like compounds we evaluated more
models 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 more
to be evaluated. This smaller database of than twenty five thousand drugs. Twenty-
compounds was screened by docking seven thousand two hundred and eighty
analysis against the originally selected four drugs which were suitable with the
target. This docking analysis consisted of features of the first model were obtained.
separating the smaller filtered database The average binding energy for these
into files of individual drugs to then be drugs in the first hundred top hits was
able to observe the characteristics of each 9.86 kilocalories per mole. On the other
drug, 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 drugs
gandScout. Further on, results that suited the features of the second
werecombined and ranked according to model. The average binding energy for
predicted binding energies, from the the first hundred top hits of this model
greatest affinity to the weakest one.From was 9.94 kilocalories per mole.This is
this, drugs with the greatest affinity, also quite significant for a relatively small
called potential top-hits,were identified. piece of the database evaluated.A total of
Finally, results were analyzed by observ- 182 compounds with predicted binding
ing 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 the
which sites of interaction, or features, two models used in this pilot
were 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. In Silico discovery of DNA methyltransferase inhibitors.
Model 2 are superior to the results ob-
Figure 7: Distribution of selected compounds
with 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, 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).
Discussion
From 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 27% of the compounds obtained where
pharmacophore models allowed us to selected by both models, a significant
identify compounds that had a significant number of compounds with predicted
interaction with the DNA methyltransfe- high binding energies were also obtained
rase 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 as
be concluded that results obtained by well. As a whole, we proved our hypothe-
May 2012. 5

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 to
identify several drugs, which are potential thank the RISE Program for giving us the
candidates 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 Cited
will definitely be useful for future studies. Chik F, Szyf M. 2010. Effects of specific
On 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 Medicinal
base 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.do
model along with their respective screen- ?momID=139
ings 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 in
Acknowledgements Biochemistry and Molecular Biology.
We would like to acknowledge the
Perry A, Watson W, Lawler M, Hollywood
great contribution of our mentor Dr. Hec- D. 2010. The epigenome as a therapeutic
tor Maldonado, our student assistant target in prostate cancer. Nature Reviews on
Adriana Diaz and the whole In Silico drug Urology. 7(1):668-680.
May 2012. 6