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

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In silico discovery of dna methyltransferase inhibitors 05 05 (1) (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. 2. In Silico discovery of DNA methyltransferase inhibitors. Outline • Background and Significance • Hypothesis • Objectives • Methodology • Results • Conclusions • Future Studies • Acknowledgements/Questions
  3. 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. 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. 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. 6. HypothesisSpecific, high-affinity inhibitors of DNA methyltransferase (DNMT1) can be identified via an In Silico approach.
  7. 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. 8. MethodologyIn general we followed the methodology presented in the In Silico DrugDiscovery 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. 9. Drug discovery strategySoftware 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. 10. Results
  11. 11. ResultsD357 -10.8 D506 -11.0 M02 M01
  12. 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. 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. 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. 15. ReferencesChik F, Szyf M. 2010. Effects of specific DMNT gene depletion on cancer celltransformation and breast cancer cell invasion; toward selective DMNTinhibitors. Carcinogenesis. 32(2):224-232.Fandy T. 2009. Development of DNA Methyltransferase Inhibitors for theTreatment of Neoplastic Diseases. Current Medicinal Chemistry. 16(17):2075-2085.Goodsell, D. 2011. Molecule of the month: DNA Methyltransferases. RCBSProtein Data Bank. http://www.pdb.org/pdb/101/motm.do?momID=139Perry A, Watson W, Lawler M, Hollywood D. 2010. The epigenome as atherapeutic target in prostate cancer. Nature Reviews on Urology. 7(1):668-680.
  16. 16. Acknowledgements Dr. Héctor M. MaldonadoMs. Adriana O. Díaz-Quiñones RISE Program
  17. 17. QuestionsThanks for your attention!

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