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Chemoinformatics Analysis and Structural Similarity Studies of Food-Related Databases

  • Karina Martinez-Mayorga
  • Terry L. Peppard
  • Ariadna I. Ramírez-Hernández
  • Diana E. Terrazas-Álvarez
  • José L. Medina-FrancoEmail author
Chapter

Abstract

Chemoinformatics approaches to problem solving are commonly used in both academia and industry, and while a major focus is the pharmaceutical industry, many other sectors of the chemical industry lend themselves to it equally well. The chemoinformatic concepts, thoroughly discussed in Chap. 1 of this book, are general and can also be applied to address problems frequently encountered in food chemistry. A general strategy when applying these computational methods is to replace biological activity by a food-related property, for instance, flavor character or antioxidative activity. In many cases, the representation of the chemical structure remains the same (using, for example, molecular fingerprints, physicochemical and/or structure/substructure representations). In other words, structure/activity relationships (SAR) studies commonly conducted in medicinal chemistry for the purpose of drug discovery can be generalized to the study of structure–property relationships (SPR) for virtually any chemistry-related project. Herein, we discuss representative and specific applications of methods used in chemoinformatics to mine data and characterize SPR information relevant to food chemistry. The chapter is organized into two major sections. First, we discuss exemplary applications of chemoinformatic analyses and characterization of the chemical space of compound databases. In this section, we cover major related concepts such as chemical space and molecular representation. The second section is focused on the application of similarity searching to food chemical databases.

Keywords

Valproic Acid Quantitative Structure Activity Relationship Virtual Screening Chemical Space Compound Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

K.M-M. thanks the Institute of Chemistry-UNAM and DGAPA-UNAM for funding (PAPIIT IA200513). The authors also wish to thank Robertet Flavors for permission to publish this chapter.

References

  1. 1.
    Medina-Franco JL, Yongye AB, López-Vallejo F (2012) Consensus models of activity landscapes. In: Matthias D, Kurt V, Danail B (eds) Statistical modeling of molecular descriptors in QSAR/QSPR. Wiley-VCH, Weinheim, pp 307–326Google Scholar
  2. 2.
    Engel T (2006) Basic overview of chemoinformatics. J Chem Inf Model 46:2267–2277CrossRefGoogle Scholar
  3. 3.
    Varnek A, Baskin II (2011) Chemoinformatics as a theoretical chemistry discipline. Mol Inf 30:20–32CrossRefGoogle Scholar
  4. 4.
    Willett P (2011) Chemoinformatics: a history. WIREs Comput Mol Sci 1:46–56CrossRefGoogle Scholar
  5. 5.
    Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  6. 6.
    Pennington JT (2006) Issues of food description. Food Chem 57:145–148CrossRefGoogle Scholar
  7. 7.
    Caccia S, Gobbi M (2009) St. John’s wort components and the brain: uptake, concentrations reached and the mechanisms underlying pharmacological effects. Curr Drug Metab 10:1055–1065CrossRefGoogle Scholar
  8. 8.
    Haddad R, Khan R, Takahashi YK, Mori K, Harel D, Sobel N (2008) A metric for odorant comparison. Nat Methods 5:425–429CrossRefGoogle Scholar
  9. 9.
    Medina-Franco JL, Martínez-Mayorga K, Giulianotti MA, Houghten RA, Pinilla C (2008) Visualization of the chemical space in drug discovery. Curr Comput Aided Drug Des 4:322–333CrossRefGoogle Scholar
  10. 10.
    Virshup AM, Contreras-García J, Wipf P, Yang W, Beratan DN (2013) Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J Am Chem Soc 135:7296–7303CrossRefGoogle Scholar
  11. 11.
    Fitzgerald SH, Sabat M, Geysen HM (2006) Diversity space and its application to library selection and design. J Chem Inf Model 46:1588–1597CrossRefGoogle Scholar
  12. 12.
    Akella LB, DeCaprio D (2010) Cheminformatics approaches to analyze diversity in compound screening libraries. Curr Opin Chem Biol 14:325–330CrossRefGoogle Scholar
  13. 13.
    Medina-Franco JL, Martinez-Mayorga K, Meurice N (2014) Balancing novelty with confined chemical space in modern drug discovery. Expert Opin Drug Discov 9:151–165CrossRefGoogle Scholar
  14. 14.
    Harvey AL (2008) Natural products in drug discovery. Drug Discov Today 13:894–901CrossRefGoogle Scholar
  15. 15.
    Scior T, Bernard P, Medina-Franco JL, Maggiora GM (2007) Large compound databases for structure-activity relationships studies in drug discovery. Mini Rev Med Chem 7:851–860CrossRefGoogle Scholar
  16. 16.
    Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690CrossRefGoogle Scholar
  17. 17.
    Gozalbes R (2011) Rational generation of focused chemical libraries: an update on computational approaches. Comb Chem High Throughput Screen 14:428–428CrossRefGoogle Scholar
  18. 18.
    Ashburn TT, Thor KB (2004) Drug repositioning: Identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683CrossRefGoogle Scholar
  19. 19.
    Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL (2006) Global mapping of pharmacological space. Nat Biotechnol 24:805–815CrossRefGoogle Scholar
  20. 20.
    Medina-Franco JL, Giulianotti MA, Welmaker GS, Houghten RA (2013) Shifting from the single to the multi target paradigm in drug discovery. Drug Discov Today 18:495–501CrossRefGoogle Scholar
  21. 21.
    Scalbert A, Andres-Lacueva C, Arita M, Kroon P, Manach C, Urpi-Sarda M, Wishart D (2011) Databases on food phytochemicals and their health-promoting effects. J Agric Food Chem 59:4331–4348CrossRefGoogle Scholar
  22. 22.
    Schneider G, Neidhart W, Giller T, Schmid G (1999) Scaffold-hopping by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed 38:2894–2896CrossRefGoogle Scholar
  23. 23.
    Duarte CD, Barreiro EJ, Fraga CA (2007) Privileged structures: a useful concept for the rational design of new lead drug candidates. Mini Rev Med Chem 7:1108–1119CrossRefGoogle Scholar
  24. 24.
    Villar HO, Hansen MR, Kho R (2007) Substructural analysis in drug discovery. Curr Comput Aided Drug Des 3:59–67CrossRefGoogle Scholar
  25. 25.
    Martínez-Mayorga K, Peppard TL, Yongye AB, Santos R, Giulianotti M, Medina-Franco JL (2011) Characterization of a comprehensive flavor database. J Chemom 25:550–560CrossRefGoogle Scholar
  26. 26.
    Medina-Franco JL, Martínez-Mayorga K, Peppard TL, Del Rio A (2012) Chemoinformatic analysis of GRAS (Generally Recognized as Safe) flavor chemicals and natural products. PLoS One 7:e50798CrossRefGoogle Scholar
  27. 27.
    Peppard TL, Le M, Pandya RN (2008) Prediction tool for modern flavor development. In: Hofmann T, Meyerhof W, Schieberle P (eds) Recent Highlights in Flavor Chemistry & Biology. Proceedings of the 8th Wartburg Symposium on flavour chemistry and biology. Deutsche Forschungsanstalt für Lebensmittelchemie, Garching, pp 374–378Google Scholar
  28. 28.
    Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK (2012) Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 52:867–881CrossRefGoogle Scholar
  29. 29.
    Alvarez J, Shoichet B (2005) Virtual screening in drug discovery. Taylor & Francis Group, LLC CRC Press, Boca RatonCrossRefGoogle Scholar
  30. 30.
    Maldonado AG, Doucet JP, Petitjean M, Fan BT (2006) Molecular similarity and diversity in chemoinformatics: from theory to applications. Mol Divers 10:39–79CrossRefGoogle Scholar
  31. 31.
    Maggiora GM (2006) On outliers and activity cliffs-why QSAR often disappoints. J Chem Inf Model 46:1535CrossRefGoogle Scholar
  32. 32.
    Villoutreix BO, Renault N, Lagorce D, Sperandio O, Montes M, Miteva MA (2007) Free resources to assist structure-based virtual ligand screening experiments. Curr Protein Pept Sci 8:381–411CrossRefGoogle Scholar
  33. 33.
    Radestock S, Weil T, Renner S (2008) Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring. J Chem Inf Model 48:1104–1117CrossRefGoogle Scholar
  34. 34.
    Kruger DM, Evers A (2010) Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. Chemmedchem 5:148–158CrossRefGoogle Scholar
  35. 35.
    Mendez-Lucio O, Perez-Villanueva J, Castillo R, Medina-Franco JL (2012) Identifying activity cliff generators of PPAR ligands using SAS maps. Mol Inf 31:837–846CrossRefGoogle Scholar
  36. 36.
    Cruz-Monteagudo M, Medina-Franco JL, Pérez-Castillo Y, Nicolotti O, Cordeiro MNDS, Borges F (2014) Activity cliffs in drug discovery: Dr. Jekyll or Mr. Hyde? Drug Discov Today (in press). doi:10.1016/j.drudis.2014.02.003Google Scholar
  37. 37.
    Rius M, Lyko F (2012) Epigenetic cancer therapy: rationales, targets and drugs. Oncogene 31:4257–4265CrossRefGoogle Scholar
  38. 38.
    Méndez-Lucio O, Tran J, Medina-Franco JL, Meurice N, Muller M (2014) Towards drug repurposing in epigenetics: olsalazine as a novel hypomethylating compound active in a cellular context. ChemMedChem 9:560–565CrossRefGoogle Scholar
  39. 39.
    Sprous DG, Salemme FR (2007) A comparison of the chemical properties of drugs and FEMA/FDA notified GRAS chemical compounds used in the food industry. Food Chem Toxicol 45:1419–1427CrossRefGoogle Scholar
  40. 40.
    Pintore M, Wechman C, Sicard G, Chastrette M, Amaury N, Chretien JR (2006) Comparing the information content of two large olfactory databases. J Chem Inf Model 46:32–38CrossRefGoogle Scholar
  41. 41.
    Jensen K, Panagiotou G, Kouskoumvekaki I (2014) Integrated text mining and chemoinformatics analysis associates diet to health benefit at molecular level. PLoS One 10:e1003432Google Scholar
  42. Zarzo M, Stanton DT (2006) Identification of latent variables in a semantic odor profile database using principal component analysis. Chem Senses 31:713–724.Google Scholar
  43. 43.
    Martinez-Mayorga K, Peppard TL, López-Vallejo F, Yongye AB, Medina-Franco JL (2013) Systematic mining of generally recognized as safe (GRAS) flavor chemicals for bioactive compounds. J Agric Food Chem 61:7507–7514CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karina Martinez-Mayorga
    • 1
    • 2
  • Terry L. Peppard
    • 3
  • Ariadna I. Ramírez-Hernández
    • 1
  • Diana E. Terrazas-Álvarez
    • 1
  • José L. Medina-Franco
    • 1
    • 2
    Email author
  1. 1.Departamento de Fisicoquímica, Instituto de QuímicaUniversidad Nacional Autónoma de MéxicoMexico CityUSA
  2. 2.Torrey Pines Institute for Molecular StudiesPort St. LucieUSA
  3. 3.NJUSA

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