1. Developing a protein-interactions ontology Esther Ratsch European Media Laboratory

2. PIOG Protein Interactions Ontology Group Computer scientists: –Philipp Cimiano Lavin (IMS Stuttgart, EML Heidelberg) –Isabel Rojas (EML Heidelberg) Computational linguists: –Uwe Reyle (IMS Stuttgart) –Jasmin Saric (EML Heidelberg) Biologists: –Esther Ratsch (EML Heidelberg) –Jörg Schultz (MPI for Molecular Genetics Berlin) –Ulrike Wittig (EML Heidelberg)

3. Motivation Why protein interactions? –protein function analysis –larger datasets Why an ontology? –clear domain model –storage and understanding of data –information retrieval from text –retrieve hidden information, inferencing

4. What is a signal transduction pathway? signal from outside is transduced to the nucleus often phosphorylation cascade signal change transcription

5. Why are they important? control of cellular processes communication between cells response to environmental changes regulatory network stable system, single mutations may be overriden by other pathway complex network enables complex behaviour

6. Jak-Stat pathway ligand cytokine receptors JAKs P P P STAT monomers P P PP PP nucleus target genes P P P tyrosine residues P P

7. General approach Identify scope of the ontology Identify concepts involved and their properties How to represent them? Define rules and constraints Formalisation Scope Concepts Representation Rules/Constraints Formalisation

8. The scope Ontology that represents interactions between proteins and other cellular compounds Restriction on molecular detail: amino acids Concentration on signal transduction pathways in initial phase no quantitative properties are modeled Scope Concepts Representation Rules/Constraints Formalisation

9. Identify concepts: Interacting compounds Different kinds of compounds: proteins, genes/DNA, ions,... Composition of compounds, e.g. amino acids, domains DNA region Jak Stat TAD LZ DBD SH3SH2 Y Domain organisation of Stat proteins Scope Concepts Representation Rules/Constraints Formalisation

10. Properties of compounds Characteristics: molecular weight, sequence, isoelectric point... Interaction potential: modifications, location, binding partners Scope Concepts Representation Rules/Constraints Formalisation nucleus P P X

11. Identify concepts: Interactions I –Control/Regulation –Biochemical Interactions –Logical Interactions –Bind/Dissociate –Formation –Integrity –Availability –Change of Location –Modification of Structure –Special Processes/ Reactions –Order Different types of interactions: phosphorylation, binding, translocation... Other classification: grouping of > 100 verbs (Swissprot)  11 not disjoint classes Scope Concepts Representation Rules/Constraints Formalisation

12. Representation of proteins General characteristics: sequence, molecular weight,... Protein state: –location –list of modifications –list of binding partners StatState3(cytoplasm, phosphorylatedAtResidue701, Stat) P P JakState1(cytoplasm, none, cytokine-receptor) Scope Concepts Representation Rules/Constraints Formalisation

13. Representation of interactions Event with pre- and postconditions not pevent ep t phosphorylation P not phosphorylatedphosphorylated Scope Concepts Representation Rules/Constraints Formalisation

14. Rules and constraints Simple hierarchies: nucleolus inside nucleus, Stat1 is a Stat is a protein Rules for the definition of interactions Consistency checking Knowledge retrieval Scope Concepts Representation Rules/Constraints Formalisation

15. Rules and constraints: example „Protein A is phosphorylated by B at position X.“  A and B are located in the same compartment  A was not modified at X before  A is phosphorylated at X afterwards  B is a protein kinase, which is a protein  dependent on X, B is either a S/T-kinase or a Y-kinase Scope Concepts Representation Rules/Constraints Formalisation

16. Phosphorylation of a protein by a kinase at a distinct residue S/T-kinase phosphorylation Formalisation: phosphorylation Scope Concepts Representation Rules/Constraints Formalisation

17. Challenges met Multidisciplinarity of the group –Different vocabularies  clear expression, fewer ambiguities –Different goals, different needs  not restricted to one goal –Different experiences  mutual benefit Domain

18. Complexity of the domain Granularity of information –detail of compound part protein: Stat domain: SH2-domain amino acid: tyrosine701 –detail of protein identity protein family: Jak, Stat protein type: Jak2, Stat5 organism specific protein: Jak2_human, Jak2_rat

19. Complexity of the domain II description detail: –not known: no data available –doesn‘t have: no binding partners –don‘t care: not important for a certain interaction

20. What comes next? Go on with development of ontology Projects using the ontology: –integration in larger ontology on metabolic pathways –application to TIGERSearch (see poster)

21. Acknowledgements Protein Interactions Ontology Group Computer scientists: –Philipp Cimiano Lavin (IMS Stuttgart, EML Heidelberg) –Isabel Rojas (EML Heidelberg) Computational linguists: –Uwe Reyle (IMS Stuttgart) –Jasmin Saric (EML Heidelberg) Biologists: –Esther Ratsch (EML Heidelberg) –Jörg Schultz (MPI for Molecular Genetics Berlin) –Ulrike Wittig (EML Heidelberg)