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Relating protein pharmacology by ligand chemistry

  • Nature Biotechnology volume 25, pages 197206 (2007)
  • doi:10.1038/nbt1284
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Abstract

The identification of protein function based on biological information is an area of intense research. Here we consider a complementary technique that quantitatively groups and relates proteins based on the chemical similarity of their ligands. We began with 65,000 ligands annotated into sets for hundreds of drug targets. The similarity score between each set was calculated using ligand topology. A statistical model was developed to rank the significance of the resulting similarity scores, which are expressed as a minimum spanning tree to map the sets together. Although these maps are connected solely by chemical similarity, biologically sensible clusters nevertheless emerged. Links among unexpected targets also emerged, among them that methadone, emetine and loperamide (Imodium) may antagonize muscarinic M3, α2 adrenergic and neurokinin NK2 receptors, respectively. These predictions were subsequently confirmed experimentally. Relating receptors by ligand chemistry organizes biology to reveal unexpected relationships that may be assayed using the ligands themselves.

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Acknowledgements

Supported by GM71896 (to B.K.S. and J.J.I.), Training Grant GM67547, a National Science Foundation graduate fellowship (to M.J.K.), the National Institute of Mental Health Psychoactive Drug Screening Program (B.L.R. and P.E.) and F32-GM074554 (to B.N.A.). We are grateful to Mark von Zastrow, Eswar Narayanan, Paul Valiant and Michael Mysinger for many thoughtful suggestions and to Jerome Hert, Veena Thomas and Kristin Coan for reading this manuscript. We also thank Elsevier MDL for use of the MDDR, and Daylight for the Daylight toolkit.

Author information

Affiliations

  1. Department of Pharmaceutical Chemistry, University of California San Francisco, 1700 4th St, San Francisco California 94143-2550, USA.

    • Michael J Keiser
    • , John J Irwin
    •  & Brian K Shoichet
  2. Biological and Medical Informatics, University of California San Francisco, 1700 4th St., San Francisco, California 94143-2550, USA.

    • Michael J Keiser
  3. Departments of Biochemistry and Nutrition and National Institute of Mental Health Psychoactive Drug Screening Program, Case Western Reserve University Medical School, 2109 Adelbert Road, Cleveland, Ohio 44106, USA.

    • Bryan L Roth
    •  & Paul Ernsberger
  4. Department of Pharmacology and Division of Medicinal Chemistry and Natural Products (BLR), The University of North Carolina Chapel Hill Medical School, Chapel Hill, North Carolina 27705, USA.

    • Bryan L Roth
    •  & Blaine N Armbruster

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Contributions

J.J.I., B.K.S. and M.J.K. developed the ideas for SEA, M.J.K. wrote the SEA algorithms and undertook the calculations reported here, with some assistance from J.J.I. B.L.R. and P.E. performed the methadone assays, B.N.A. performed the emetine and loperamide assays, and B.K.S. and M.J.K. wrote the manuscript with editorial review from J.J.I. and B.L.R.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to John J Irwin or Brian K Shoichet.

Supplementary information

PDF files

  1. 1.

    Supplementary Figure 1

    Statistical model fits for MDDR.

  2. 2.

    Supplementary Figure 2

    Set recovery in database search after TC-chemotype filtering.

  3. 3.

    Supplementary Figure 3

    Set recovery in database search with progressive random removal of compounds from query set.

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    Supplementary Figure 4

    Set recovery in database search over 246 MDDR classes.

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    Supplementary Figure 5

    Choice of threshold parameter.

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    Supplementary Figure 6

    PSI-BLAST heat map of MDDR activity class target protein sequences compared against themselves.

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    Supplementary Figure 7

    SEA heat map of MDDR activity classes compared against themselves.

  8. 8.

    Supplementary Table 1

    Expanded statistics for Table 1 and Table 2.

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    Supplementary Table 2

    MDDR unrelated orphans.

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    Supplementary Table 3

    Rankings of the correct MDDR activity class for each PubChem MeSH pharmacological action set by SEA and by MPS.

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    Supplementary Table 4

    Loperamide and emetine functional assay data.

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    Supplementary Table 5

    SEA statistical model fits.

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    Supplementary Methods

Zip files

  1. 1.

    Supplementary Data