This document summarizes a presentation on discovering inhibitors for the histone-lysine N-methyltransferase SETD2 using an in silico approach. It discusses methyltransferases and histone methyltransferases as a potential target. The hypothesis is that selective, high-affinity SETD2 inhibitors can be identified by targeting its SAM binding site. The methodology involves generating pharmacophore models using software and screening databases of compounds. Results show two pharmacophore models and top-hit compounds identified. The conclusions are that the SETD2 binding site is a potential drug target and compounds with high predicted binding energies were identified. Future work involves refining models and testing top compounds in assays.

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. 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. Background and Significance
Methyltranferases:
• 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. Background and Significance
Histone 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. Background and Significance
Histone 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. 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. 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. 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
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/

11. Results: Pharmacophore model generation.
3H6L.pdb 3H6L.pdb 3H6L.pdb
ZINC00000000 ZINC00000000 ZINC00000000
Pharmacophore Model 01 Pharmacophore Model 02

13. 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.

14. 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.

15. Acknowledgments
• Dr. Maldonado
• Adriana Díaz
• Dra. Díaz
• Dra. Gonzalez

16. Questions?...
Thanks for your attention.