Bio-IT_2011

1. Comparison of Compound-to-Target
Relationships in Chemogenomic and
Drug Databases
Aprill 2012 update: FYI these two blog posts are on the same theme
http://cdsouthan.blogspot.se/2012/01/our-human-beta-lactamase-is-not_09.html
http://cdsouthan.blogspot.se/2011/08/compound-to-target-mappings-part-i.html
Chris Southan
ChrisDS Consulting, Göteborg, Sweden,
Presented to the NCBI PubChem team on 11 April. the BioIT World
Chemogenomics and Toxicogenomics Workshop on 12 April Boston, USA, and
as a shorter version, the ChEMBL users meeting at the EBI, 27 may 2011
[1]

2. Aknowledgments and Context
• I profoundly appreciate the efforts of those who develop, manage
and maintain public resources specified here and many others I
enjoy acessing
• I have some history in evaluating the utility, exploitation and
content quality of both bioinformatics and cheminformatics
databases. I thus enjoy the dual roles (roughly in equal parts) of
both fan and critic
• All databases have imperfections. This presentation investigates a
selection of these but critical analysis should not be missinterpreted
as disparaging either the quality of primary sources or the work of
curators and database teams
[2]

3. Outline
• Mapping concepts sources and challenges
• Extremes of the distribution
• Atorvastatin, drug-to-targets
• Hmg-CoA reductase target-to-drugs
• Equivocal mapping examples
• Exploring data intersects
• Complex targets
• Conclusions and outlook
[3]

4. Activity-to-compound-to-protein Mapping:
Capturing Relationships Between four Concepts
MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGG
APLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQ
GYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHR
YYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSI
PHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEI
ARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSE
VLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIV
RVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFE
AAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNI
FPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDD
CYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAV
SACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTL
MTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHD
DFADDISLLK
Document Assay Result Compound Protein
Expert extraction and curation
Unstructured data Structured data
Papers & Patents Databases
[4]

5. The D-A-R-C-P Axis
Pathway/module/
system
[5]

6. Compound and drug-to-target Collations
D-A-R-C-P
Targets = 5,662 protein targets, cpds = 284,206 data points = 648,915,
D-A-R-C-P
Targets = 8,091 Small Molecules = 658,075, data points = 3,030,317
(D)-A-R-C-P-S
BioAssays extracted from literature (ChEMBL) = 499,520, Direct screening
assays = 3,208, active Compounds = 23,677, Targets = 447
D-C-P-S
Approved cpds = 1431 , Targets = 1458,
Experimental cpds = 5212, research targets = 3206
D-C-P
Targets = 358 successful, 251 clinical trial and 1,254 research,
Drugs = 1,511 approved, 1,118 clinical trial and 2,331 experimental
[6]

7. PDB
Drug-to-
Protein
Mappings
in DrugPort
[7]

8. Target Mapping: Curatorial Challenges
• Target = (infered) direct binding
• Primary (bona fide) target = therapeutic causality
• Polytargets = multiple
• Para-target = sub-family specificity
• Ortho-target = cross-species specificity
• Cross-screen = non-homologous
• Non-target (e.g. trypsin, albumin)
• Off-target = liability (ADR or side effect)
• Anti-target = known libaility (e.g. HERG)
• Indirect target = non-binding (e.g. APP)
• Complex = resolvable to sequence IDs (eg proteosome)
• Complex = experimentaly unresolved (e.g. PDE5s)
• Ambigous = lack of metadata or curatorial judgment (e.g. BACE)
• Non-canonical = where metadata specifies mutation, splice or PTM
• Metabo-target = metabolic interactions
• Transport-target = transporters
[8]

9. Drug-target Networks
[9]

10. One target-to-many compounds:
Dopamine Receptor D2
[10]

11. One compound-to-(367)-
proteins
[11]

12. Mapping sources for
the top selling drug
[12]

13. Target Matrix for Atorvastatin
Swiss-Prot ChEMBL TTD DrugBa PubChem
(BindingD nk
B)
HMDH_HUMAN X X X (PDB) X
HMDH_RAT X X
DPP4_HUMAN X
DPP4_PIG X
AHR_HUMAN X
[13]

14. Other
Statins:
Different
BioAssay
Coverages
[14]

15. Diferent PubChem CIDs map to different
submissions, structures and activity profiles
Atorvastatin -> 10 CID name matches
Substances 397 Links
Same structure: 33 Links
Mixture: 364 Links
CID 60823 39 canonical
Substances: 19 Links
[15]

16. Vice-versa, Compounds-to-target: HMG-CoA
[16]

17. Drugs mapped to HMG-CoA as target
Swiss-Prot cross-reference
[17]

18. Equivocal Mappings
[18]

19. Swiss-Prot Target Intersects
• 1,627 results for database:(type:drugbank)
• 297 results for database:(type:bindingdb)
• 45 results for database:(type:bindingdb) AND
database:(type:drugbank) AND organism:"Homo sapiens
[19]

20. Mixed
Mappings
[20]

21. Mannitol: drug ? yes -
ligand ? yes ? target ? no
[21]

22. Polypropylene Glycol: drug ? no, ligand ?
maybe, target ? no
[22]

23. E-2012: False-negative?
[23]

24. Antifreeze: drug ?, no, ligand ? no,
154 targets ? no
Wikipedia: Ethylene glycol is
moderately toxic with an oral
LDLO = 786 mg/kg for
humans
[24]

25. Crowdsourcing Works !
[25]

26. Curation Challenges
[26]

27. Secretase matches
in TTD
Mixed-concept targets
but no small-molecule
true positives
[27]

28. Gamma Secretase Activity: Variable Subunit
Mappings
[28]

29. APP: Indirect Target, three mechanisms
“for small molecules that suppress the Amyloid Precursor Protein (APP)
translation by binding to the 5'Untranslated Region of the APP mRNA
[29]

30. Proteasome: Target Descriptions and Cross-
screens for Bortzemib
[30]

31. PubChem Compound Intersects:
Primary Drug Targets with Screening data
[31]

32. Mycophenolic acid
[32]

33. Mycophenolic acid and Prodrug: Complex mappings
• Primary Target human IMPDH2
• IMPDH1 ?
• IMPDH2 hamster
• IMPDH2 Tritrichomonas
• myfortic is an enteric-coated formulation of MPA in a delayed-
release tablet.
[33]

34. Conclusions
• Compared to what we had even a few years ago, let alone in
LBPC (life-before-PubChem) these compound-to-protein
sources are fantastic
• However, most things that could go wrong have
• We don’t often see QC statistics
• Data coverage is patchy, ad hoc and can be circular
• If you operate on these data at large scale you have no choice
but to ”trust and filter”
• If detailed realationships are important you need to ”verify and
judge” back to the primary source
• You can only really do this if you have at least some in vitro
background rather than just in silico
[34]

35. Wouldn’t it be nice if we had ....
• Interpreted mapping distribution statisitcs for each database
• Details about extraction triages, curation rules and parsing logic
• Harmonisation of mapping rules and cross-comparison of content
• Clear declarations and statistics of circularity between databases
• Curator judgments overuling document primacy
• Consolidated and extended Swiss-Prot cross-references
• Assay and target ontologies (Pistoia ? Open Phacts ?)
• “Standardization of Enzyme Data” (STRENDA, http://www.beilstein-
institut.de/en/projekte/strenda/)
• “Minimum Information About a Bioactive Entity” (MIABE,
http://www.psidev.info/index.php?q=node/394)
[35]

36. Our Efforts
http://www.jcheminf.com/content/3/1/14
http://www.jcheminf.com/content/1/1/10
[36]

37. [37]