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Relating protein pharmacology by ligand chemistry


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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Figure 1: Comparing similar and dissimilar ligand sets to that of DHFR.
Figure 2: Similarity maps for 246 enzymes and receptors.
Figure 3: Comparison of sequence and ligand-based protein similarity.
Figure 4: Testing the off-target activities of methadone, loperamide, and emetine.

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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

Authors and Affiliations



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.

Corresponding authors

Correspondence to John J Irwin or Brian K Shoichet.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Figure 1

Statistical model fits for MDDR. (PDF 405 kb)

Supplementary Figure 2

Set recovery in database search after TC-chemotype filtering. (PDF 72 kb)

Supplementary Figure 3

Set recovery in database search with progressive random removal of compounds from query set. (PDF 72 kb)

Supplementary Figure 4

Set recovery in database search over 246 MDDR classes. (PDF 79 kb)

Supplementary Figure 5

Choice of threshold parameter. (PDF 171 kb)

Supplementary Figure 6

PSI-BLAST heat map of MDDR activity class target protein sequences compared against themselves. (PDF 964 kb)

Supplementary Figure 7

SEA heat map of MDDR activity classes compared against themselves. (PDF 579 kb)

Supplementary Table 1

Expanded statistics for Table 1 and Table 2. (PDF 114 kb)

Supplementary Table 2

MDDR unrelated orphans. (PDF 71 kb)

Supplementary Table 3

Rankings of the correct MDDR activity class for each PubChem MeSH pharmacological action set by SEA and by MPS. (PDF 86 kb)

Supplementary Table 4

Loperamide and emetine functional assay data. (PDF 84 kb)

Supplementary Table 5

SEA statistical model fits. (PDF 91 kb)

Supplementary Methods (PDF 115 kb)

Supplementary Data (ZIP 1653 kb)

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Keiser, M., Roth, B., Armbruster, B. et al. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25, 197–206 (2007).

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