Download PDF
Planta Med 2015; 81(06): 459-466
DOI: 10.1055/s-0035-1545881
Original Papers
Georg Thieme Verlag KG Stuttgart · New York

Unique Macrocycles in the Taiwan Traditional Chinese Medicine Database

Christian Kramer
1   Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
,
Maren Podewitz
1   Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
,
Peter Ertl
2   Novartis Institutes for BioMedical Research, Basel, Switzerland
,
Klaus R. Liedl
1   Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
› Author Affiliations
Further Information

Publication History

received 11 August 2014
revised 20 December 2014

accepted 23 February 2015

Publication Date:
09 April 2015 (online)

    Permissions and Reprints

Abstract

Chemical space coverage within natural product databases is an important criterion for the selection of databases for virtual screening. Chemical space analysis can also provide valuable hints towards chemical substructures that are responsible for medical activity. We therefore developed a protocol for structurally characterizing the chemical space covered in specific natural product databases by comparing it to a “standard” natural product scaffold distribution. In this contribution, we analyzed the structural characteristics of the traditional Chinese medicine database@Taiwan as an example. While we did not find classes of very characteristic small molecule scaffolds, we found that there are a number of specific macrocyclic scaffolds that are highly enriched in the traditional Chinese medicine database@Taiwan and not documented elsewhere. This surprising finding points towards underused regions in chemical space with a big potential for biological impact.

Key words

chemical space - natural product - macrocycle - TCM database@Taiwan
 

  • References

  • 1 Newman DJ, Cragg GM. Natural products as sources of new drugs over the last 25 years. J Nat Prod 2007; 70: 461-477
    CrossrefPubMedGoogle Scholar
  • 2 Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod 2012; 75: 311-335
    CrossrefPubMedGoogle Scholar
  • 3 Chen CY. TCM Database@Taiwan: the worldʼs largest traditional Chinese medicine database for drug screening in silico . PLoS One 2011; 6: e15939
    CrossrefPubMedGoogle Scholar
  • 4 López-Vallejo F, Giulianotti MA, Houghten RA, Medina-Franco JL. Expanding the medicinally relevant chemical space with compound libraries. Drug Discov Today 2012; 17: 718-726
    CrossrefPubMedGoogle Scholar
  • 5 Shen M, Tian S, Li Y, Li Q, Xu X, Wang J, Hou T. Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines. J Cheminform 2012; 4: 31
    CrossrefPubMedGoogle Scholar
  • 6 Ertl P, Roggo S, Schuffenhauer A. Natural product-likeness score and its application for prioritization of compound libraries. J Chem Inf Model 2008; 48: 68-74
    CrossrefPubMedGoogle Scholar
  • 7 Vanii K, Moreno P, Truszkowski A, Ertl P, Steinbeck C. Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinform 2012; 13: 106
    CrossrefPubMedGoogle Scholar
  • 8 Schuffenhauer A, Ertl P, Roggo S, Wetzel S, Koch MA, Waldmann H. The scaffold tree–visualization of the scaffold universe by hierarchical scaffold classification. J Chem Inf Model 2007; 47: 47-58
    CrossrefPubMedGoogle Scholar
  • 9 Koch MA, Schuffenhauer A, Scheck M, Wetzel S, Casaulta M, Odermatt A, Ertl P, Waldmann H. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc Natl Acad Sci U S A 2005; 102: 17272-17277
    CrossrefPubMedGoogle Scholar
  • 10 Wetzel S, Schuffenhauer A, Roggo S, Ertl P, Waldmann H. Cheminformatic analysis of natural products and their chemical space. Chim Int J Chem 2007; 61: 355-360
    PubMedGoogle Scholar
  • 11 Ertl P. Intuitive ordering of scaffolds and scaffold similarity searching using scaffold keys. J Chem Inf Model 2014; 54: 1617-1622
    CrossrefPubMedGoogle Scholar
  • 12 Driggers EM, Hale SP, Lee J, Terrett NK. The exploration of macrocycles for drug discovery–an underexploited structural class. Nat Rev Drug Discov 2008; 7: 608-624
    CrossrefPubMedGoogle Scholar
  • 13 Giordanetto F, Kihlberg J. Macrocyclic drugs and clinical candidates: what can medicinal chemists learn from their properties?. J Med Chem 2014; 57: 278-295
    CrossrefPubMedGoogle Scholar
  • 14 Marsault E, Peterson ML. Macrocycles are great cycles: applications, opportunities, and challenges of synthetic macrocycles in drug discovery. J Med Chem 2011; 54: 1961-2004
    CrossrefPubMedGoogle Scholar
  • 15 Wessjohann LA, Ruijter E, Garcia-Rivera D, Brandt W. What can a chemist learn from natureʼs macrocycles? – A brief, conceptual view. Mol Divers 2005; 9: 171-186
    CrossrefPubMedGoogle Scholar
  • 16 Walker S, Landovitz R, Ding WD, Ellestad GA, Kahne D. Cleavage behavior of calicheamicin gamma 1 and calicheamicin T. Proc Natl Acad Sci U S A 1992; 89: 4608-4612
    CrossrefPubMedGoogle Scholar
  • 17 Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2011; 40: D1100-D1107
    PubMedGoogle Scholar