PubChem Bioassays as a Source of Polypharmacology

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  • 1. PubChem Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha PubChem Bioassays as a Source of Introduction Polypharmacology Methodology Visualization Application Bin Chen, David Wild, Rajarshi Guha School of Informatics Indiana University 236th ACS National Meeting 17th August, 2008
  • 2. PubChem PubChem Bioassays Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Currently contains 1157 assays Methodology A number are follow ups of primary screens Visualization Application Assay size ranges from 2 to 224,000 molecules Many compounds tested in multiple assays PubChem web interface support queries that focus on individual assays Cross-assay queries can be tough
  • 3. PubChem Assay Content Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha AID 1 AID 2 AID 50 The data is obviously primary Introduction Methodology But the assay description and Visualization target are also useful pieces of Application information Can we combine data AID 1 AID 2 AID 50 target description across multiple assays to draw conclusions, gain insight?
  • 4. PubChem A Network Model of Bioassays - Goals Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi PubChem Guha Bioassays Introduction PubMed PubChem Methodology Assay Network KEGG GO Visualization Construction Application Link into other data bases Storage & Deployment RDBMS Network Interactive Web Web Models Visualization SQL Service Page Mapping to External Networks PPI Drug -Target
  • 5. PubChem Mapping Assay Networks to Real Networks Bioassays as a Source of Polypharmacology Bin Chen, David An assay network is an artificial network - does not Wild, Rajarshi Guha necessarily have physical meaning We need to map the assay network onto a real biological Introduction network Methodology Visualization PPI networks Application metabolic networks drug target networks Using the mapping, we’d like to identify MLSCN compounds that might be active against one or more nodes in the real network The stepping stones . . . How do we construct the assay network? How do we map the network?
  • 6. PubChem Mapping Assay Networks to Real Networks Bioassays as a Source of Polypharmacology Bin Chen, David An assay network is an artificial network - does not Wild, Rajarshi Guha necessarily have physical meaning We need to map the assay network onto a real biological Introduction network Methodology Visualization PPI networks Application metabolic networks drug target networks Using the mapping, we’d like to identify MLSCN compounds that might be active against one or more nodes in the real network The stepping stones . . . How do we construct the assay network? How do we map the network?
  • 7. PubChem Why Perform a Mapping? Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Identify compounds that interacts with two targets in Introduction different pathways Methodology Visualization Alternatively, identify compounds that interact with a Application target in a pathway but not in another pathway Identify compounds capable of disrupting protein-protein interactions Our ability to do these will depend on the quality of assay data and the way we map the assay network to the real network Hopkins, A.L. et al, Curr. Opin. Chem. Biol, 2006, 16, 127–136
  • 8. PubChem Why Perform a Mapping? Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Identify compounds that interacts with two targets in Introduction different pathways Methodology Visualization Alternatively, identify compounds that interact with a Application target in a pathway but not in another pathway Identify compounds capable of disrupting protein-protein interactions Our ability to do these will depend on the quality of assay data and the way we map the assay network to the real network Hopkins, A.L. et al, Curr. Opin. Chem. Biol, 2006, 16, 127–136
  • 9. PubChem Assay Network Construction Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Download Extract protein Guha PubChem bioassay XML target ID's Introduction Methodology Extract activity Evaluate Visualization scores pairwise CLUSTAL Application similarities Exclude compounds with score < 80 Connect assays if Connect assays if Connect assays if they have their semantic their target compounds in similarity is greater similarity is greater common than X than X
  • 10. PubChem Assay Network Construction Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha We will focus on a compound-centric network Introduction Methodology A semantic network requires some form of annotation on Visualization the assays Application Initial attempts at annotation assays based on GO terms (via descriptions) Alternatively, could consider deriving annotations based on the targets Using protein target similarity restricts one to enzymatic assays which leads to a relatively small assay network
  • 11. PubChem Assay Network Construction – Caveats Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction A compound-centric network is not very rigorous Methodology Visualization The PubChem activity score is known to be noisy Application Currently the only way to look at assay readouts over the whole collection Using an activity score cutoff of 80 is arbitrary We haven’t considered promiscuity directly, though a filter would be useful
  • 12. PubChem Assay Network - Common Compounds Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Visualization Application
  • 13. PubChem Some Network Statistics Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction 60 Methodology 222 assays with a single target 50 Visualization Selected the smallest assay if 40 Application Frequency more than assay had the same 30 target 20 N = 125, E = 598 10 Vmax = 40, Vavg = 9.6 0 ¯ C = 0.67 0 10 20 30 40 Vertex Degree Histogram of vertex degree
  • 14. PubChem Clustering in the Assay Network Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Visualization Application
  • 15. PubChem Assay Network - Common Compounds Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Visualization Application
  • 16. PubChem Assay Network - Common Compounds Bioassays as a Source of Polypharmacology 388 targets NAD+ -dependent Bin Chen, David Wild, Rajarshi 15-hydroxyprostaglandin Guha dehydrogenase Introduction Has active compounds common Methodology with Visualization pim-2-oncogene (505) Application 15-lipoxygenase (887) aldo-keto reductase (381) Luteonin Genistein
  • 17. PubChem Assay Network - Common Compounds Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Visualization Application
  • 18. PubChem Assay Network - Common Compounds Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha 749 and 755 target 5-HT1E and Introduction 5-HT1A respectively Methodology Both have a (different) Visualization compound in common with Application 1288 (selectin E) Probably promiscuous given that they are also active in many other assays But a selectin inhibitor is known to reduce hyperalgesia by blocking 5-HT3 Oliviera, M.C.G. et al, Neuroscience, 2007, 145, 708–714
  • 19. PubChem Assay Network - Common Compounds Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Most of these assay pairs have Introduction Methodology closely related targets Visualization Tissue non-specific alkaline Application phosphatase and intestinal alkaline phosphatase (1056 & 1017) STAT1 and STAT3 (1303 & 1310) ER-α and ER-β (1226 & 1228) lethal factor (B. anthracis) and nF-κB (942 & 1309) have one compound in common - podophyllotoxin
  • 20. PubChem Mapping an Assay Network Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Visualization Application Mapping Function
  • 21. PubChem Defining a Mapping Function Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Multiple mapping functions can be defined Methodology exact matches between assay target and external targets Visualization similarity between target sequences Application similarity between target binding sites One could also map edges of one network onto another Dependent on the nature of the external network Depending on the nature of the definition, the mapping procedure can be a trivial search or may require an optimization scheme if multiple mappings are possible
  • 22. PubChem Assay Network to HPRD Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha The HPRD database collects protein-protein interaction Introduction data and pathway membership Methodology Visualization The July 2007 release lists 31,708 PPI’s Application 96 assays can be mapped to the unique proteins in HPRD We construct a HPRD network by identifying the pairs from the 96 proteins that have a listed interaction When mapping the HPRD network to the assay network, we include singleton HPRD nodes
  • 23. PubChem HPRD Network Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Visualization Application
  • 24. PubChem Assay - HPRD Network Mapping Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Visualization Application
  • 25. PubChem Assay - HPRD Network Mapping Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Is this a useful mapping? Visualization Application Since we map assays to HPRD entries by target ID, we aren’t getting new information on the assays individually But we are able to easily identify assay targets that interact with each other (or not)
  • 26. PubChem Comparing Two Assays Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Score = 95 Score = 100 Visualization Application CID 126298 AID 835 AID 1325 Gene: EPHB4 Gene: ABCB1 Signal Transduction Transporter Axon guidance pathway ABC transporters Over expressed in breast carcinoma Overexpression is related to multidrug resistance in chemotherapy Preferentially expressed in veins Involved in the BBB Required for angiogenesis Not expressed very highly in vascular tissue
  • 27. PubChem Comparing Two Assays Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha Introduction Methodology Score = 94 Score = 93 Visualization Application CID 647501 AID 903 AID 755 Gene: TP53 Gene: HTR1A Nucleotide Metabolism Signal Transduction Apoptosis, Cell Cycle, Cancer Neuroactive ligand-receptor Controls cell cycle and apoptosis Target for anti-depressants Inactivated in cancer cells
  • 28. PubChem Disrupting PPI’s Bioassays as a Source of Polypharmacology Bin Chen, David The pairs of interacting Wild, Rajarshi Guha targets have compounds tested against both of them Introduction Methodology Majority are inactive or Visualization Tyrosine 3-monooxygenase NF-kappa B inconclusive in both of them activation protein Application CID 1025314 is active in AID 445 but inactive in AID 903 Hypoxia Bcl-2 Inducible P53 Factor
  • 29. PubChem Summary Bioassays as a Source of Polypharmacology Bin Chen, David Wild, Rajarshi Guha A network view of assays provides with a novel tool for Introduction Methodology visualization and summary of the assay collection Visualization It’s utility beyond visualization is dependent on the way Application we construct the network A compound-centric network allows us to use the assay collection as a probe into external networks Future work will investigate different forms of the assay network focusing on protein target and GO annotation similarity