In silico discovery of dna methyltransferase inhibitors 05 05 (1) (1)
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.
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
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
