1.In silico discovery of DNAmethyltransferase inhibitors. Anglica M. Gonzlez-SnchezKhrystall K. Ramos-Callejas Adriana O. Diaz-QuionesHctor M. Maldonado, Ph.D. University of Puerto Rico at Cayey Universidad Central del Caribe at Bayamn

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 groupfrom a donor molecule to an acceptor. Methylation often occurs on nucleic bases in DNA oramino acids in protein structures. The methyl donor used by Methytransferases is a reactivemethyl 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 methylgroupsare 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 complexwith Sinefunginpdb: 3SWR 5. Significance In mammals, regulation of normal growth duringembryonic stages is modulated by DNA methylation. Methylation of both DNA and proteins has also beenlinked to cancer development, as methylations thatregulate expression of tumor suppressor genespromotes tumor genesis and metastasis. 6. HypothesisSpecific, high-affinity inhibitors of DNA methyltransferase (DNMT1) can be identified via an In Silico approach. 7. Objectives To identify potential new targets in DNAMethyltransferase. Based on previous results, create apharmacophore model for the selectedtarget, and perform a primary screening usingLigandScout. To perform a Secondary Screening usingAutoDock Vina to identify top-hits. 8. MethodologyIn general we followed the methodology presented in the In Silico DrugDiscovery Workshop: Pharmacophore models were generated using information from drugspreviously identified and benzene mapping analysis. Pharmacophore models generated were then used to "filter" relatively largedatabases of small chemical compounds (drug-like or lead-like). A smallerdatabase with the compounds presenting characteristics imposed by the modelwas generated. This smaller database of compounds was screened by docking analysisagainst the originally selected target. Results were combined and rankedaccording to predicted binding energies and potential Top-hits identified. Results were analyzed and can be used for further refinement of thePharmacophore model. 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. Results 11. ResultsD357 -10.8 D506 -11.0M02M01 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_0.5Predicted 6 DNMT1_6 -10.4 M02_0.5Number of Binding Energy 7 DNMT1_7 -10.3 M01_0.3compounds (kcal/mol) 8 DNMT1_8 -10.3 M02_0.5 9 DNMT1_9 -10.3 M02_0.4-10.5210 DNMT1_10-10.2 M02_0.3-10.4411 DNMT1_11-10.2 M02_0.4-10.3312 DNMT1_12-10.2 M01_0.413 DNMT1_13-10.2 M01_0.5-10.21014 DNMT1_14-10.2 M01_0.0-10.1 1115 DNMT1_15-10.2 M01_0.3 -10 1416 DNMT1_16-10.2 M01_0.3 -9.92617 DNMT1_17-10.2 M02_0.018 DNMT1_18-10.2 M01_0.0 -9.83619 DNMT1_19-10.2 M01_0.0 -9.77620 DNMT1_20-10.1 M01_0.421 DNMT1_21-10.1 M02_0.5 Total 18222 DNMT1_22-10.1 M02_0.523 DNMT1_23-10.1 M01_0.324 DNMT1_24-10.1 M01_0.025 DNMT1_25-10.1 M02_0.2 13. Conclusions Two Pharmacophore models were generated usinginformation obtained from the interaction of two previouslyidentified compounds with the DNA methyltransferase astarget. Ranking of predicted top-hits indicated that results obtainedby Model 2 are superior to the results obtained with Model 1. Although close to 27% of the compounds obtained wereselected by both models, a significant number of compoundswith predicted high binding energies was also obtained withModel 1. A total of 182 compounds with predicted binding energiesequal or higher than -9.7 kcal/mol was found between the twomodels used in this pilot project. 14. Future studies Complete the analysis of the interactions between thetop-hits and the target and evaluate possibility ofrefining the Pharmacophore model. Broaden the sample of the compound database toinclude a larger number of drugs (1.7 million lead-likecompounds). Identify top-hits and test a group of these compoundsin a bioassay (proof-of-concept). 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. AcknowledgementsDr. Hctor M. MaldonadoMs. Adriana O. Daz-Quiones RISE Program 17. QuestionsThanks for your attention!