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

Methyltransferases are a type of

transferase enzyme that transfers a me-

thyl 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 methyltransfer-

ase is in charge of catalyzing certain reac-

tions 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-

Figure 1: Chem-

ical structure of

a Methyl group

In Silico discovery of DNA methyltransferase inhibitors.

May 2012. 2

adenosylmethionine, also called SAM,

which functions as a methyl donor

(Malygin and Hattman, 2012). This dona-

tion 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 Figure 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 sever-

al 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, meth-

ylation 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, methyltransferas-

es have to methylate DNA strands so that

this genetic information will be transmit-

ted as DNA replicates. Therefore, the me-

thyl groups that are added to the DNA

strands are important to modify how DNA

bases are read during protein synthesis

and to control expression of genes in dif-

ferent 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 methyl-

ation that can lead to cancer develop-

ment. That’s the reason why we have

derived the hypothesis that specific, high-

Figure 2: Chemical structure of the methyl do-

nor 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.

May 2012. 3

affinity inhibitors of DNA methyltransfer-

ase (DNMT1) can be identified via an In

Silico approach.

Materials and Methods

In 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

PyMOL 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 pharmaco-

phore 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 Me-

thyltransferase (refer to Figure 5). In that

previous research these compounds

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).

Those two pharmacophore models

generated were then used to "filter" rela-

tively large databases of small chemical

Figure 4: Clean structure of the DMNT1

(pdb: 3SWR)

In Silico discovery of DNA methyltransferase inhibitors.

May 2012. 4

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 Lig-

andScout. Further on, results were

combined and ranked according to pre-

dicted binding 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 observ-

ing the interactions of each of the top hit

drugs with the protein and identifying

which sites of interaction, or features,

were more common, whether the ones of

Model 1 or the ones of Model 2. These

results will also be used for further re-

finement of the pharmacophore model.

Results

Lead-like compounds are mole-

cules that serve as the starting point for

the development of a drug, typically by

variations in structure for optimal effica-

cy. From a database of about 1.7 million

lead-like compounds we evaluated more

than 150,000 of them, divided into 5 piec-

es of the database, each one with more

than twenty five thousand drugs. Twen-

ty-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 se-

cond 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 kilocal-

ories per mol were found between the

two models used in this pilot project (re-

fer to Figure 7).

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.

May 2012. 5

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

pharmacophore models allowed us to

identify compounds that had a significant

interaction with the DNA methyltransfer-

ase 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 ob-

tained 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-

Figure 7: Distribution of selected compounds

with predicted binding energies equal or high-

er than -9.7 kcal/mol.

In Silico discovery of DNA methyltransferase inhibitors.

May 2012. 6

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 Me-

thyltransferase.

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.

Acknowledgements

We 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.

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