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Computational Approaches to Epigenetic Drug Discovery

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Emergent Computation

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 24))

Abstract

The misregulation of epigenetic mechanisms has been linked to disease. Current drugs that treat these dysfunctions have had some success, however many have variable potency, instability in vivo and lack target specificity. This may be due to the limited knowledge on epigenetic mechanisms, especially at the molecular level, which restricts the development and discovery of novel therapeutics and the optimization of existing drugs. Computational approaches, specifically in molecular modeling, have begun to address these issues by complementing phases of drug discovery and development. Here is presented a review of current computational efforts in drug discovery and development, with a focus on molecular modeling approaches including virtual screening, molecular dynamics, molecular docking, homology modeling and pharmacophore modeling.

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References

  1. Allen, M.P.: Introduction to molecular simulation. In: Attig, N., Binder, K., Grubmüller, H., Kremer, K. (eds.) Computational soft matter: From synthetic polymers to proteins, Lecture notes. NIC Series, Jülich (2004)

    Google Scholar 

  2. Baron, R., Vellore, N.A.: LSD1/CoREST reversible opening-closing dynamics: discovery of a nanoscale clamp for chromatin and protein binding. Biochemistry 51(15), 3151–3153 (2012)

    Article  Google Scholar 

  3. Choy, M.K., Movassagh, M., Goh, H.G., Bennett, M.R., Down, T.A., Foo, R.S.: Genome-wide conserved consensus transcription factor binding motifs are hyper-methylated. BMC Genomics. 11, 519 (2010)

    Article  Google Scholar 

  4. Comley, J.: Epigenetic targets: on the verge of becoming a major new category for successful drug research. Drug Discovery World. http://www.ddw-online.com/summer-15/p303686-epigenetic-targets-:-on-the-verge-of-becoming-a-major-new-category-for-successful-drug-research.html. Accessed 15 Oct 2015

  5. Cramer, S.A., Adjei, I.M., Labhasetwar, V.: Advancements in the delivery of epigenetic drugs. Expert Opin. Drug Deliv. 12(9), 1501–1512 (2015)

    Article  Google Scholar 

  6. Dueñas-González, A., García-López, P., Herrera, L.A., Medina-Franco, J.L., González-Fierro, A., Canderlaria, M.: The prince and the pauper. A tale of anticancer targeted agents. Mol. Cancer 7, 33 (2008)

    Article  Google Scholar 

  7. Evans, D.A., Bronowska, A.K.: Implications of fast-time scale dynamics of human DNA/RNA cytosine methyltransferases (DNMTs) for protein function. Theoret. Chem. Acc. 125, 407–418 (2010)

    Article  Google Scholar 

  8. Foulks, J.M., Parnell, K.M., Nix, R.N., Chau, S., Swierczek, K., Saunders, M., Kanner SB.: Epigenetic drug discovery: targeting DNA methyltransferases. J. Biomol. Screen 17(1), 2–17 (2012)

    Google Scholar 

  9. Francis, R.C.: Epigenetics: How Environment Shapes our Genes. W.W. Norton and Company, New York (2012)

    Google Scholar 

  10. Hay, E.A., Cowie. P., MacKenzie. A.: Determining epigenetic targets: A beginner’s guide to identifying genome functionality through database analysis. Methods Mol. Biol. 1–17 (2015)

    Google Scholar 

  11. Hughes, J.P., Rees, S., Kalindjian, S.B., Philpott, K.L.: Principles of early drug discovery. Br. J. Pharmacol. 162(6), 1239–1249 (2011)

    Article  Google Scholar 

  12. Krieger, E., Nabuurs, S.B., Vriend, G.: Homology modeling. In: Bourne, P.E., Weissig, H. (eds.) Structural Bioinformatics. Wiley-Liss Inc, Hoboken (2003)

    Google Scholar 

  13. Lindahl, E.R.: Molecular dynamics simulations. Methods Mol. Biol. 443, 3–23 (2008)

    Article  Google Scholar 

  14. Luong, L.D.: Basic principles of genetics (2009). http://cnx.org/contents/41c4c77e-a44c-431f-bbc0-32eb72726630@1/Basic-Principles-of-Genetics

  15. Martinez-Mayorga, K., Peppard, T.L., López-Vallejo, F., Yongye, A.B., Medina-Franco, J.L.: Systematic mining of generally approved safe (GRAS) flavor chemicals for bioactive compounds. J. Agric Food Chem. 61(31), 7507–7514 (2013)

    Article  Google Scholar 

  16. Medina-Franco, J.L., Caulfield, T.: Advances in the computational development of DNA methyltransferase inhibitors. Drug Discovery Today 16(9–10), 418–425 (2011)

    Article  Google Scholar 

  17. Medina-Franco, J.L., Yoo, J.: Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: insights to unveil inhibitors in chemical databases. Mol. Diversity 17, 337–344 (2013)

    Article  Google Scholar 

  18. Medina-Franco, J.L., Méndez-Lucio, O., Dueñas-González, Yoo J.: Discovery and development of DNA methyltransferase inhibitors using in silico approaches. Drug Discovery Today 20(5), 569–577 (2015)

    Article  Google Scholar 

  19. Mishra, N.K.: Computational modeling of P450s for toxicity prediction. Expert Opin. Drug Metab. Toxicol. 7(10), 1211–1231 (2011)

    Article  MathSciNet  Google Scholar 

  20. National Cancer Center Research Institute: DNA methylation (2010). http://www.ncc.go.jp/en/nccri/divisions/14carc/14carc01_1.html

  21. Pharmaceutical Research and Manufacturers of America: Biopharmaceutical research and development: The process behind new medicines. PhRMA (2015)

    Google Scholar 

  22. Ptak, C., Petronis, A.: Epigenetics and complex disease: from etiology to new therapeutics. Annu. Rev. Pharmacol. Toxicol. 48, 257–276 (2008)

    Article  Google Scholar 

  23. Siedlecki, P., Garcia Boy, R., Comagic, S., Schirrmacher, R., Wiessler, M., Zielenkiewicz, P., Lyko, F.: Establishment and functional validation of a structural homology model for human DNA methyltransferase 1. Biochem. Biophys. Res. Commun. 306(2), 558–563 (2003)

    Google Scholar 

  24. Siedlecki, P., Boy, R.G., Musch, T., Brueckner, B., Suhai, S., Lyko, F., Zielenkiewicz, P.: Discovery of two novel, small-molecule inhibitors of DNA methylation. J. Med. Chem. 49, 678–683 (2006)

    Article  Google Scholar 

  25. Singh, N., Dueñas-González, A., Lyko, F., Medina-Franco, J.L.: Molecular modeling and molecular dynamics studies of hydralazine with human DNA methyltransferase 1. Chem. Med. Chem. 4(5), 792–799 (2009)

    Article  Google Scholar 

  26. Turner, B.M.: Histone acetylation and an epigenetic code. Bioessays 22(9), 836–845 (2000)

    Article  Google Scholar 

  27. Vellore, N.A., Baron, R.: Epigenetic molecular recognition: a biomolecular modeling perspective. Chem. Med. Chem. 9(3), 484–494 (2014)

    Article  Google Scholar 

  28. Ververis, K., Hiong, A., Karagiannis, T.C., Licciardi, P.V.: Histone deacetylase inhibitors (HDACIs): multitargeted anticancer agents. Biologics 7, 47–60 (2013)

    Google Scholar 

  29. Wermuth, C.G., Ganellin, R.C., Lindberg, P., Mitscher, L.A.: Chapter 36—Glossary of terms used in medical chemistry (IUPAC recommendations 1997). Anuu. Rep. Med. Chem. 33, 385–395 (1998)

    Google Scholar 

  30. Xu, Y.Z., Kanagaratham, C., Radzioch, D.: Chromatin remodelling druing host-bacterial pathogen interaction. In: Radzioch, D. (ed.) Chromating remodelling (2013). http://www.intechopen.com/books/chromatin-remodelling/chromatin-remodelling-during-host-bacterial-pathogen-interaction

  31. Yang, S.Y.: Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discovery Today 15, 444–450 (2010)

    Article  Google Scholar 

  32. Yoo, J., Medina-Franco, J.L.: Discovery and optimization of inhibitors of DNA methyltransferase as novel drugs for cancer therapy. In: Rundfeldt, C. (ed.) Drug development—a case study based insight into modern strategies, InTech. http://www.intechopen.com/books/drug-development-a-case-study-based-insight-into-modernstrategies/discovery-and-optimization-of-inhibitors-of-dna-methyltransferase-as-novel-drugs-for-cancertherapy

  33. Yoo, J., Medina-Franco, J.L.: Homology modeling, docking and structure-based pharmacophore of inhibitors of DNA methyltransferase. J. Comput. Aided Mol. Des. 25(6), 555–567 (2011)

    Article  Google Scholar 

  34. Yoo, J., Choi, S., Medina-Franco, J.L.: Molecular modeling studies of the novel inhibitors of DNA methyltransferases SGI-1027 and CBC12: implications for the mechanism of inhibition of DNMTs. PLoS One 8(4), e62152 (2012)

    Article  Google Scholar 

  35. Yoo, J., Kim, J.H., Robertson, K.D., Medina-Franco, J.L.: Molecular modeling of inhibitors of human DNA methyltransferase with a crystal structure: discovery of a novel DNMT1 inhibitor. Adv. Protein Chem. Struct. Biol. 87, 219–247 (2012)

    Article  Google Scholar 

  36. Yoo, J., Medina-Franco, J.L.: Computer-guided discovery of epigenetics drugs: molecular modeling and identification of inhibitors of DNMT1. J. Cheminform. 4, 25 (2012)

    Article  Google Scholar 

  37. Yoo, J., Medina-Franco, J.L.: Inhibitors of DNA methyltransferases: insights from computational studies. Curr. Med. Chem. 19(21), 3475–3487 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing, Queen’s University, Kingston, ON, Canada

    Emese E. Somogyvari & Selim G. Akl

  2. Department of Biomedical and Molecular Sciences, School of Environmental Studies and School of Computing, Queen’s University, Kingston, ON, Canada

    Louise M. Winn

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  1. Emese E. Somogyvari
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  2. Selim G. Akl
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  3. Louise M. Winn
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Correspondence to Emese E. Somogyvari .

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Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England Unconventional Computing Centre, Bristol, United Kingdom

    Andrew Adamatzky

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Somogyvari, E.E., Akl, S.G., Winn, L.M. (2017). Computational Approaches to Epigenetic Drug Discovery. In: Adamatzky, A. (eds) Emergent Computation . Emergence, Complexity and Computation, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-46376-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-46376-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46375-9

  • Online ISBN: 978-3-319-46376-6

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