1. In silico discovery of DNA
methyltransferase
inhibitors.
Angélica M. González-Sánchez¹
Khrystall K. Ramos-Callejas¹
Adriana O. Diaz-Quiñones¹
Héctor M. Maldonado, Ph.D.²
¹University of Puerto Rico at Cayey
²Universidad Central del Caribe at Bayamón

2. In Silico discovery of DNA methyltransferase inhibitors.
Outline
• Background and Significance
• Hypothesis
• Objectives
• Methodology
• Results
• Conclusions
• Future Studies
• Acknowledgements/Questions

3. Methyltransferase
• Type of transferase enzyme that transfers a methyl group
from a donor molecule to an acceptor.
• Methylation often occurs on nucleic bases in DNA or
amino acids in protein structures.
• The methyl donor used by Methytransferases is a reactive
methyl group bound to sulfur in S-adenosylmethionine
(SAM).
SAM Methyl Group

4. DNA methyltransferase
• DNMT1 adds methyl groups to
cytosine bases in newly
replicated DNA.
• These methyl groups are
important to:
• Modify how DNA bases are read
during protein synthesis.
• Control expression of genes in
different types of cells.
Structure of human DNMT1
(residues 600-1600) in complex
with Sinefungin
pdb: 3SWR

5. Significance
• In mammals, regulation of normal growth during
embryonic stages is modulated by DNA methylation.
• Methylation of both DNA and proteins has also been
linked to cancer development, as methylations that
regulate expression of tumor suppressor genes
promotes tumor genesis and metastasis.

6. Hypothesis
Specific, high-affinity inhibitors of DNA
methyltransferase (DNMT1) can be
identified via an In Silico approach.

7. Objectives
• To identify potential new targets in DNA
Methyltransferase.
• Based on previous results, create a
pharmacophore model for the selected
target, and perform a primary screening using
LigandScout.
• To perform a Secondary Screening using
AutoDock Vina to identify “top-hits”.

8. Methodology
In general we followed the methodology presented in the In Silico Drug
Discovery Workshop:
• Pharmacophore models were generated using information from drugs
previously identified and benzene mapping analysis.
• Pharmacophore models generated were then used to "filter" relatively large
databases of small chemical compounds (drug-like or lead-like). A smaller
database with the compounds presenting characteristics imposed by the model
was generated.
• This smaller database of compounds was screened by docking analysis
against the originally selected target. Results were combined and ranked
according to predicted binding energies and potential Top-hits identified.
• Results were analyzed and can be used for further refinement of the
Pharmacophore model.

9. Drug discovery strategy
Software Used:
• PyMOL Molecular Graphics System v1.3
http://www.pymol.org
• AutoDock (protein-protein docking
software) http://autodock.scripps.edu/
• Auto Dock Tools: Graphical Interface for AutoDock
http://mgltools.scripps.edu/downloads
• AutoDock Vina: improving the speed and accuracy of
docking with a new scoring function, efficient optimization
and multithreading. http://vina.scripps.edu/
• LigandScout: Advanced Pharmacophore Modeling and
Screening of Drug Databases.
http://www.inteligand.com/ligandscout/
Databases Used:
• Research Collaboratory for Structural Bioinformatics (RCSB)
www.pdb.org

10. Results

11. Results
D357 -10.8 D506 -11.0
M02 M01

12. • Clean lead-like ZINC Database (1.7 million compounds) Results
• Sample of >150,000 compounds (5 pieces)
• Pharmacophore M01: 27284; Average BE top 100 hits = 9.86
• Pharmacophore M02: 39525; Average BE top 100 hits = 9.94
• 27% of filtered compounds fulfilled requirements of both models.
Compound Affinity Model/pie
Name (Binding Energy) ce
1 DNMT1_1 -10.5 M02_0.4
2 DNMT1_2 -10.5 M02_0.0
3 DNMT1_3 -10.4 M02_0.4
4 DNMT1_4 -10.4 M02_0.2
5 DNMT1_5 -10.4 M02_0.5 Predicted
6 DNMT1_6 -10.4 M02_0.5 Number of
Binding Energy
7 DNMT1_7 -10.3 M01_0.3 compounds
(kcal/mol)
8 DNMT1_8 -10.3 M02_0.5
9 DNMT1_9 -10.3 M02_0.4 -10.5 2
10 DNMT1_10 -10.2 M02_0.3 -10.4 4
11 DNMT1_11 -10.2 M02_0.4 -10.3 3
12 DNMT1_12 -10.2 M01_0.4
13 DNMT1_13 -10.2 M01_0.5
-10.2 10
14 DNMT1_14 -10.2 M01_0.0 -10.1 11
15 DNMT1_15 -10.2 M01_0.3 -10 14
16 DNMT1_16 -10.2 M01_0.3 -9.9 26
17 DNMT1_17 -10.2 M02_0.0
18 DNMT1_18 -10.2 M01_0.0 -9.8 36
19 DNMT1_19 -10.2 M01_0.0 -9.7 76
20 DNMT1_20 -10.1 M01_0.4
21 DNMT1_21 -10.1 M02_0.5
Total 182
22 DNMT1_22 -10.1 M02_0.5
23 DNMT1_23 -10.1 M01_0.3
24 DNMT1_24 -10.1 M01_0.0
25 DNMT1_25 -10.1 M02_0.2

13. Conclusions
• Two Pharmacophore models were generated using
information obtained from the interaction of two previously
identified compounds with the DNA methyltransferase as
target.
• Ranking of predicted top-hits indicated that results obtained
by Model 2 are superior to the results obtained with Model 1.
• Although close to 27% of the compounds obtained were
selected by both models, a significant number of compounds
with predicted high binding energies was also obtained with
Model 1.
• A total of 182 compounds with predicted binding energies
equal or higher than -9.7 kcal/mol was found between the two
models used in this pilot project.

14. Future studies
• Complete the analysis of the interactions between the
top-hits and the target and evaluate possibility of
refining the Pharmacophore model.
• Broaden the sample of the compound database to
include a larger number of drugs (1.7 million lead-like
compounds).
• Identify top-hits and test a group of these compounds
in a bioassay (proof-of-concept).

15. References
Chik F, Szyf M. 2010. Effects of specific DMNT gene depletion on cancer cell
transformation and breast cancer cell invasion; toward selective DMNT
inhibitors. Carcinogenesis. 32(2):224-232.
Fandy T. 2009. Development of DNA Methyltransferase Inhibitors for the
Treatment of Neoplastic Diseases. Current Medicinal Chemistry. 16(17):2075-
2085.
Goodsell, D. 2011. Molecule of the month: DNA Methyltransferases. RCBS
Protein Data Bank. http://www.pdb.org/pdb/101/motm.do?momID=139
Perry A, Watson W, Lawler M, Hollywood D. 2010. The epigenome as a
therapeutic target in prostate cancer. Nature Reviews on Urology. 7(1):668-680.

16. Acknowledgements
Dr. Héctor M. Maldonado
Ms. Adriana O. Díaz-Quiñones
RISE Program

17. Questions
Thanks for your attention!