Insilico structural analysis of parasporin 2 protein sequences of
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Transcript of P pt rise insilico 2
In Silico discovery of Histone-lysine N-methyltransferase SETD2
inhibitors.
Juan Carlos Torres Sánchez1
Gretel Saraí Montañez Próspere1
Adriana O. Díaz 1
Dr. Hector M. Maldonado 2
1RISE Program, University of Puerto Rico at Cayey; 2Universidad Central del Caribe, Medical School.
Outline of the Presentation
• Background and SignificanceA. Methyltransferases
B. Histone-lysine N Methyltransferase
• Hypothesis
• Methodology
• Results
• Conclusions
• Future Work
• Acknowledgments/Questions
In Silico discovery of Histone-lysine N-methyltransferase SETD2 inhibitors.
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)
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.
• 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.
Background and SignificanceHistone Methyltranferases (HMT):
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”.
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.
2. 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.
3. Perform a secondary screening to identify “top-hits” or potential lead compounds (AutoDock Vina)
Methodology 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 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/
Results: Histone-lysine N-methyltransferase (3H6L.pdb)
SAM
3H6L.pdb
Pharmacophore Model 01 Pharmacophore Model 02
Results: Pharmacophore model generation.
Affinity # of Drugs
-9.7 1
-9.5 1
-9.4 3
-9.3 13
-9.2 16
-9.1 10
-9 15
TOTAL 59
Compound Name
Affinity (kcal/m
ol)Model
1 MTHLY_01 -9.7 M01_0.32 MTHLY_02 -9.5 M02_0.03 MTHLY_03 -9.4 M01_0.24 MTHLY_04 -9.4 M02_0.35 MTHLY_05 -9.4 M01_0.36 MTHLY_06 -9.3 M01_0.47 MTHLY_07 -9.3 M01_0.38 MTHLY_08 -9.3 M02_0.29 MTHLY_09 -9.3 M02_0.010 MTHLY_10 -9.3 M02_0.411 MTHLY_11 -9.3 M01_0.312 MTHLY_12 -9.3 M02_0.413 MTHLY_13 -9.3 M02_0.214 MTHLY_14 -9.3 M02_0.315 MTHLY_15 -9.3 M02_0.416 MTHLY_16 -9.3 M01_0.317 MTHLY_17 -9.3 M02_0.018 MTHLY_18 -9.3 M02_0.319 MTHLY_19 -9.2 M02_0.420 MTHLY_20 -9.2 M01_0.521 MTHLY_21 -9.2 M01_0.222 MTHLY_22 -9.2 M02_0.223 MTHLY_23 -9.2 M02_0.224 MTHLY_24 -9.2 M02_0.225 MTHLY_25 -9.2 M02_0.0
• A database of >150,000 lead-like compounds where used for screening against our two Pharmacophore models.
• A total of 18,082 compounds fulfill all requirements of Model 1 while 13,587 compounds where obtained with Model 2.
• 21 % of these compounds where selected by both models.
Results: Docking and ranking of top-hits.
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.
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.
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
Acknowledgments
• Dr. Maldonado
• Adriana Díaz
• Dra. Díaz
• Dra. Gonzalez
Questions?...
Thanks for your attention.