In Silicodiscovery of Histone-lysine N-methyltransferaseSETD2             inhibitors.         Juan Carlos Torres Sánchez1 ...
In Silico discovery of Histone-lysine N-methyltransferase SETD2 inhibitors.               Outline of the Presentation • Ba...
Background and SignificanceMethyltranferases:• A methyltransferase, also known as a methylase, is a type of  tranferase en...
Background and SignificanceHistone Methyltranferases (HMT):• HMT are histone-modifying enzymes, including histone-lysine N...
Background and SignificanceHistone Methyltranferases (HMT):• Abnormal expression or activity of methylation-regulating enz...
Hypothesis“Selective, high-affinity inhibitors of Histone- lysine N-methyltransferaseSETD2 can be identified via an In Sil...
Objectives:1. Identify a new target for drug development in the   Histone-lysine N-methyltransferaseSETD2 by analysis   of...
MethodologySoftware Used:•   PyMOL Molecular Graphics System v1.3 http://www.pymol.org•   AutoDock (protein-protein dockin...
Results: Pharmacophore model generation.            3H6L.pdb               3H6L.pdb                  3H6L.pdb         ZINC...
Conclusions• Initial analysis of the Histone-lysine N-methyltransferase SETD2 suggests  that the binding site for the meth...
Future Work• Complete the screening of the lead-like database (>1.7 million  compounds) using both Pharmacophore models.• ...
Acknowledgments• Dr. Maldonado• Adriana Díaz• Dra. Díaz• Dra. Gonzalez
Questions?...  Thanks for your attention.
In silico drug discovery 2
In silico drug discovery 2
In silico drug discovery 2
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In silico drug discovery 2

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  • Also known as a methylasa
  • A la izquierda la foto de la proteina. Arriba derechafoto con los benzenos. Abajo derechafoto con la droga.
  • In silico drug discovery 2

    1. 1. In Silicodiscovery of Histone-lysine N-methyltransferaseSETD2 inhibitors. Juan Carlos Torres Sánchez1 Gretel SaraíMontañez 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), tRNAmethyltransferase (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 and/or arginine residues of histone proteins.• 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, suggesting associations between histone methylation and malignant transformation of cells or formation of tumors• 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-methyltransferaseSETD2 can be identified via an In Silico approach targeting this protein SAM binding site”.
    7. 7. Objectives:1. Identify a new target for drug development in the Histone-lysine N-methyltransferaseSETD2 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 (AutoDockVina)
    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 AutoDockhttp://mgltools.scripps.edu/downloads• AutoDockVina: 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: Pharmacophore model generation. 3H6L.pdb 3H6L.pdb 3H6L.pdb ZINC00000000 ZINC00000000 ZINC00000000 Pharmacophore Model 01 Pharmacophore Model 02
    10. 10. 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.
    11. 11. 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.
    12. 12. Acknowledgments• Dr. Maldonado• Adriana Díaz• Dra. Díaz• Dra. Gonzalez
    13. 13. Questions?... Thanks for your attention.
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