Biochemical ontologies aim to capture and represent biochemical entities and the relations that exist between them in an accurate manner. A fundamental starting point is biochemical identity, but our current approach for generating identifiers is haphazard and consequently integrating data is error-prone. I will discuss plausible structure-based strategies for biochemical identity whether it be at molecular level or some part thereof (e.g. residues, collection of residues, atoms, collection of atoms, functional groups) such that identifiers may be generated in an automatic and curator/database independent manner. With structure-based identifiers in hand, we will be in a position to more accurately capture context-specific biochemical knowledge, such as how a set of residues in a binding site are involved in a chemical reaction including the fact that a key nitrogen atom must first be de-protonated. Thus, our current representation of biochemical knowledge may improve such that manual and automatic methods of bio-curation are substantially more accurate.

1. Increasingly Accurate Representation of Biochemistry Michel Dumontier , Ph.D. Assistant Professor of Bioinformatics Department of Biology, School of Computer Science Institute of Biochemistry, Ottawa Institute of Systems Biology Carleton University IMG Seminar:Manchester:Michel Dumontier 11/05/2009

7. 11/05/2009 IMG Seminar::Michel Dumontier

8. Bio2RDF: 2.3B triples of SPARQL-accessible linked biological data! Chemical Parts!

11. (S)-Glutamic Acid InChI= {version}1 /{formula}C5H9NO4 /c{connections}6-3(5(9)10)1-2-4(7)8 /h{H_atoms}3H,1-2,6H2,(H,7,8)(H,9,10) /p{protons}+1 /t{stereo:sp3}3- /m{stereo:sp3:inverted}0 /s{stereo:type (1=abs, 2=rel, 3=rac)}1 /i{isotopic:atoms}4+1

12. 2. Structure Accurate and Extensible Descriptions Required CML SDF O1[C@@H]([C@@H](O)([C@H](O)([C@@H](O)([C@@H]1(O)))))(CO) 79025 IUPAC InChI=1/C6H12O6/c7-1-2-3(8)4(9)5(10)6(11)12-2/h2-11H,1H2/t2-,3-,4+,5-,6+/m1/s1 InCHI α -D-Glucose 6-(hydroxymethyl)oxane-2,3,4,5-tetrol OR (2R,3R,4S,5R,6R)-6 -(hydroxymethyl)tetrahydro -2H-pyran-2,3,4,5-tetraol SMILES

14. http://code.google.com/p/semanticwebopenbabel/

15. Chemical Ontology Chemical Knowledge for the Semantic Web. Mykola Konyk , Alexander De Leon , and Michel Dumontier . LNBI . 2008. 5109:169-176. Data Integration in the Life Sciences (DILS2008) . Evry. France.

16. Describing chemical functional groups in OWL-DL for the classification of chemical compounds hydroxyl group methyl group Knowledge of functional groups is important in chemical synthesis, pharmaceutical design and lead optimization. Functional groups describe chemical reactivity in terms of atoms and their connectivity, and exhibits characteristic chemical behavior when present in a compound. N Villanueva-Rosales, MDumontier. 2007. OWLED, Innsbruck, Austria. Ethanol

18. Fully Classified Ontology 35 FG

20. Organic Compound Ontology 28 OC

29. PSI-MOD contains modified residues with links to structural descriptions

33. Biological Identifier Service

37. Semantic Science will create a Bio2RDF endpoint to link semantically equivalent biochemical identifiers

38. Situational Modeling

42. dumontierlab.com [email_address] Special thanks to PhD Student Leonid Chepelev for insightful discussions  semanticscience.org