P pt rise insilico 2


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  • Bullete 1: suggesting associations between histone methylation and malignant transformation of cells or formation of tumors
  • A lead compound is a chemical compound that has pharmacological or biological activity.
  • You start with a biological problemFollowed by downloading the 3D stuctureThen you identify the hot stops.You find the optimal target for drug developmentGeneration of pharmacophore model Then primary screening with database of 1.7 million drugs Identification of top hitsAfter that a secondary screeningIdentify ranking Binding energyMaybe further refinementThen Bio Assay
  • Here you can see the Protein with SAMSAM donor of methyl groupIf you find a drug that blocks where the SAM is, then you can inhibit the mehtylation process
  • P pt rise insilico 2

    1. 1. In Silico discovery of Histone-lysine N-methyltransferase SETD2 inhibitors. Juan Carlos Torres Sánchez1 Gretel Saraí Montañez Próspere1 1 Adriana O. Díaz 2 Dr. Hector M. Maldonado 1RISE Program, University of Puerto Rico at Cayey; 2Universidad Central del Caribe, Medical School.
    2. 2. In Silico discovery of Histone-lysine N-methyltransferase SETD2 inhibitors. Outline of the Presentation • Background and Significance A. Methyltransferases B. Histone-lysine N Methyltransferase • Hypothesis • Methodology • Results • Conclusions • Future Work • Acknowledgments/Questions
    3. 3. Background and SignificanceMethyltranferases:• A methyltransferase, also known as a methylase, is a type of tranferase enzyme that transfers a methyl group from a donor molecule (usually S-adenosyl methionine; SAM) to an acceptor.• Methylation often occurs on nucleic bases in DNA or amino acids in protein structures.• Several methyltransferases have ben identified including DNA (cytosine-5)-methyltransferase 1 (DNMT1), tRNA methyltransferase (TRDMT1) and protein methyltransferase (SETD2)
    4. 4. Background and SignificanceHistone Methyltranferases (HMT):• HMT are histone-modifying enzymes, including histone-lysine N- methyltransferase and histone-arginine N-methyltransferase.• These group of enzymes catalyze the transfer of up to three methyl groups to lysine.• Histones are highly alkaline proteins found in eukaryotic cell nuclei that package and order the DNA into structural units called nucleosomes.• Methylation of histones is important biologically because it is the principal epigenetic modification of chromatin that determines gene expression, genomic stability, etc.
    5. 5. Background and SignificanceHistone Methyltranferases (HMT):• Abnormal expression or activity of methylation-regulating enzymes has been noted in some types of human cancers• It is now generally accepted that in addition to genetic aberrations, cancer can be initiated by epigenetic changes in which gene expression is altered without genomic abnormalities.• The protein methyltransferases (PMTs) have emerged as a novel target class, especially for oncology indications where specific genetic alterations, affecting PMT activity, drive cancer tumorigenesis.
    6. 6. Hypothesis“Selective, high-affinity inhibitors of Histone- lysine N-methyltransferase SETD2 can be identified via an In Silico approach targeting the SAM binding site of this protein”.
    7. 7. Objectives:1. Identify a new target for drug development in the Histone-lysine N-methyltransferase SETD2 by analysis of benzene mapping and the interactions of previously identified compounds.1. Using information from these interactions, create Pharmacophore Models (LigandScout) for the selected target and perform a virtual pre-screening of Drug Databases against our model.1. Perform a secondary screening to identify “top-hits” or potential lead compounds (AutoDock Vina)
    8. 8. MethodologySoftware Used:• PyMOL Molecular Graphics System v1.3 http://www.pymol.org• AutoDock (protein-protein docking software) http://autodock.scripps.edu/• Auto Dock Tools: Graphical Interfase 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:• SwissProt/TrEMBL; (Protein knowledgebase and Computer-annotated supplement to Swiss-Prot) http://www.expasy.ch/sprot/• Research Collaboratory for Structural Bioinformatics (RCSB) www.pdb.org• ZINC: A free database for virtual screening: http://zinc.docking.org/
    9. 9. Results: Histone-lysine N-methyltransferase (3H6L.pdb) SAM 3H6L.pdb
    10. 10. Results: Pharmacophore model generation. Pharmacophore Model 01 Pharmacophore Model 02
    11. 11. Results: Docking and ranking of top-hits.• A database of >150,000 lead-like compounds Affinity where used for screening against our two Compound (kcal/m Model Pharmacophore models. Name ol) 1 MTHLY_01 -9.7 M01_0.3 2 MTHLY_02 -9.5 M02_0.0• A total of 18,082 compounds fulfill all requirements 3 MTHLY_03 -9.4 M01_0.2 4 MTHLY_04 -9.4 M02_0.3 of Model 1 while 13,587 compounds where 5 MTHLY_05 -9.4 M01_0.3 obtained with Model 2. 6 MTHLY_06 -9.3 M01_0.4 7 MTHLY_07 -9.3 M01_0.3 8 MTHLY_08 -9.3 M02_0.2• 21 % of these compounds where selected by both 9 MTHLY_09 -9.3 M02_0.0 10 MTHLY_10 -9.3 M02_0.4 models. 11 MTHLY_11 -9.3 M01_0.3 12 MTHLY_12 -9.3 M02_0.4 Affinity # of Drugs 13 MTHLY_13 -9.3 M02_0.2 -9.7 1 14 MTHLY_14 -9.3 M02_0.3 15 MTHLY_15 -9.3 M02_0.4 -9.5 1 16 MTHLY_16 -9.3 M01_0.3 17 MTHLY_17 -9.3 M02_0.0 -9.4 3 18 MTHLY_18 -9.3 M02_0.3 19 MTHLY_19 -9.2 M02_0.4 -9.3 13 20 MTHLY_20 -9.2 M01_0.5 21 MTHLY_21 -9.2 M01_0.2 -9.2 16 22 MTHLY_22 -9.2 M02_0.2 23 MTHLY_23 -9.2 M02_0.2 -9.1 10 24 MTHLY_24 -9.2 M02_0.2 -9 15 25 MTHLY_25 -9.2 M02_0.0 TOTAL 59
    12. 12. Conclusions• Initial analysis of the Histone-lysine N-methyltransferase SETD2 suggests that the binding site for the methyl donor compound SAM can be used as potential targets for In Silico drug discovery and development.• Two distinct pharmacophore models where generated and used to filter the original database of small chemical compounds to less than 20% of the total number of compounds.• A total of 31,669 compounds where docked In Silico to the target protein and the results ranked according to their predicted binding energies.• A group of drugs-like-compounds with high binding energies (less than - 9.0 kcal/mol) were identified in the secondary screening consistent with the possibility of high affinity interactions.
    13. 13. Future Work• Complete the screening of the lead-like database (>1.7 million compounds) using both Pharmacophore models.• Evaluate results of top-hits and if appropriate use this information to refine the Pharmacophore model and repeat the screening cycle.• Obtain/purchase some of the predicted high affinity compounds and test their potential as inhibitors in a bioassay.
    14. 14. References• Duns G, Berg E, Duivenbode I, Osinga J, Hollema H, Hofstra R, and Kok K. 2010. Histone Methyltransferase Gene SETD2 Is a Novel Tumor Suppressor Gene in Clear Cell Renal Cell Carcinoma. Cancer Res. 70:4287-4291• Spannhoff A, Hauser A, Heinke R, Sippl W, and Jung M. 2009. The Emerging Therapeutic Potential of Histone Methyltransferase and Demethylase Inhibitors. ChemMedChem. 4:1568 – 1582• Campagna V, Wai M, Nguyen K, Feher M, Najmanovich R, and Schapira M. 2011. Structural Chemistry of the Histone Methyltransferases Cofactor Binding Site. Chem. Inf. Model. 51:612–623
    15. 15. Acknowledgments• Dr. Maldonado• Adriana Díaz• Dra. Díaz• Dra. Gonzalez
    16. 16. Questions?... Thanks for your attention.