Developing a protein-interactions ontology

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Developing a protein-interactions ontology

• Esther Ratsch
• European Media Laboratory

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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)

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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

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What is a signal transduction pathway?

• signal from outside is transduced to the nucleus
• often phosphorylation cascade

change
signal
transcription
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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

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Jak-Stat pathway
P
P
P
P
P
P
P
P
P
P
STAT monomers
P
nucleus
P
tyrosine residues
target genes
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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
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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
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Identify concepts Interacting compounds

• Different kinds of compounds proteins,
  genes/DNA, ions, ...
• Composition of compounds, e.g. amino acids,
  domains

Scope Concepts Representation
Rules/Constraints Formalisation
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Properties of compounds

• Characteristics molecular weight, sequence,
  isoelectric point...
• Interaction potential modifications, location,
  binding partners

Scope Concepts Representation
Rules/Constraints Formalisation
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Identify concepts Interactions I

• Different types of interactions phosphorylation,
  binding, translocation ...
• Other classification grouping of gt 100 verbs
  (Swissprot) ? 11 not disjoint classes

• Control/Regulation
• Biochemical Interactions
• Logical Interactions
• Bind/Dissociate
• Formation
• Integrity

• Availability
• Change of Location
• Modification of Structure
• Special Processes/ Reactions
• Order

Scope Concepts Representation
Rules/Constraints Formalisation
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Representation of proteins

• General characteristics sequence, molecular
  weight, ...
• Protein state
• location
• list of modifications
• list of binding partners

Scope Concepts Representation
Rules/Constraints Formalisation
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Representation of interactions

• Event with pre- and postconditions

Scope Concepts Representation
Rules/Constraints Formalisation
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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
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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
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Formalisation phosphorylation

• Phosphorylation of a protein by a kinase at a
  distinct residue
• S/T-kinase phosphorylation

 
Scope Concepts Representation
Rules/Constraints Formalisation
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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

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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

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Complexity of the domain II

• description detail
• not known no data available
• doesnt have no binding partners
• dont care not important for a certain
  interaction

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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)

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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)