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
______________________________________________________________________
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 meth-
ylation 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
silico 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 the
identified 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 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’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 methyltransfer-
ase is in charge of catalyzing certain reac- To function as a methyl group
tions in the body. The transfer of this transporter, the methyltransferase carries
methyl group from one compound to an- with itself a compound named S-

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, a
tion 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 by
transferases (Fandy, 2009). For this par- which normal cells are transformed into
ticular 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 cells
transferase also has several subtypes, spread from one organ to another. This
from which we chose the DNA methyl- means that the methylation of these tu-
transferase 1, or DNMT1 (refer to Figure mor 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 sever- Figure 3: Struc-
al implications. In order for a cell to be ture of human
capable of doing a specific function it DNMT1 (residues
600-1600) in
must encode certain genes to produce
complex with
specific proteins. For this process, meth- Sinefungin.
ylation of the DNA is essential because it
adds methyl groups to genes in the DNA, Pdb: 3SWR
shutting 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 that
this genetic information will be transmit- Given this, it has been decided to
ted as DNA replicates. Therefore, the me- investigate about a way of finding specific
thyl 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-
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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 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 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 server
were mainly databases and software that
NanoBio and the software AutoDock Vina
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
our mentor, we decided to develop it by
PyMOL 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 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
(www.inteligand.com). 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 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 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 more
to be evaluated. This smaller database of than twenty five thousand drugs. Twen-
compounds was screened by docking ty-seven thousand two hundred and
analysis against the originally selected eighty four drugs which were suitable
target. This docking analysis consisted of with the features of the first model were
separating the smaller filtered database obtained. The average binding energy for
into files of individual drugs to then be these drugs in the first hundred top hits
able to observe the characteristics of each was 9.86 kilocalories per mole. On the
drug, including their affinity with the pro- other hand, we also acquired thirty-nine
tein. This was also done by using Lig- thousand five hundred and thirty-five
andScout. Further on, results were drugs that suited the features of the se-
combined and ranked according to pre- cond model. The average binding energy
dicted binding energies, from the greatest for the first hundred top hits of this model
affinity to the weakest one. From this, was 9.94 kilocalories per mole. This is
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 kilocal-
drugs with the protein and identifying ories per mol were found between the
which 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. These
results will also be used for further re-
finement 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 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, 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 methyltransfer- high binding energies were also obtained
ase 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
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Chemistry. 16(17):2075-2085.
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Goodsell, D. 2011. Molecule of the month:
would be to evaluate 1.7 million lead-like
DNA Methyltransferases. RCBS Protein
compounds, which represent the whole Data-
database. After several refinements of the Bank.http://www.pdb.org/pdb/101/motm.do
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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