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Using Ontology Reasoning to Classify Protein Phosphatases K.Wolstencroft, P.Lord, L.tabernero, A.brass, R.stevens University of Manchester.

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Presentation on theme: "Using Ontology Reasoning to Classify Protein Phosphatases K.Wolstencroft, P.Lord, L.tabernero, A.brass, R.stevens University of Manchester."— Presentation transcript:

1 Using Ontology Reasoning to Classify Protein Phosphatases K.Wolstencroft, P.Lord, L.tabernero, A.brass, R.stevens University of Manchester

2 Introduction Automated classification of proteins into protein subfamilies 1.Background 2.Architecture 3.Advantages 4.Results 5.Future directions

3 Motivation Biological data production fast -High throughput techniques -Large numbers of species being sequenced -Large amount of data uncharacterised Data analysis is now the rate-limiting step

4 Why Classify? Classification and curation of a genome is the first step in understanding the processes and functions happening in an organism Classification enables comparative genomic studies - what is already known in other organisms The similarities and differences between processes and functions in related organisms often provide the greatest insight into the biology

5 Protein Classification Proteins divided into broad functional classes Protein Families - evolutionary relationships - common domain architecture Relationship between sequence and structure allows searching for distinct structural (and functional) domains within the sequence Domains could be several amino acids long – or could span most of the protein


7 Protein Family Classification Often diagnostic domains/motif signify family membership e.g. ALL proteins with a tyrosine protein kinase-specific active site (IPR008266) domain are types of tyrosine kinase

8 Current Techniques Human expert classification –gold standard – human knowledge applied to results from bioinformatics analysis tools Automated use of bioinformatics analysis tools –quick –less detailed

9 Automated Methods Bioinformatics analysis tools top BLAST hit - annotating as similar to other known proteins - Could result in protein A is similar to protein B, which is similar to protein C, which is similar to protein D etc, etc, Interpro Scan analysis - shows number and types of domains, but does not provide interpretations

10 Human Expert Annotation Same similarity searching tools used for domain/motif identification Humans use expert knowledge to classify proteins according to domain arrangements Presence / order / number of each important Can an ontology be used to capture this knowledge to the standard of a human annotator?

11 Ontology Approach Use ontology to capture the rules for protein family membership in formal OWL representation Ontology contains the human expert knowledge Ontology reasoning can take the place of human analysis of the data

12 The Protein Phosphatases large superfamily of proteins – involved in the removal of phosphate groups from molecules Important proteins in almost all cellular processes Involved in diseases – diabetes and cancer human phosphatases well characterised

13 Phosphatase Functional Domains Andersen et al (2001) Mol. Cell. Biol

14 Determining Class Definitions R5 -Contains 2 protein tyrosine phosphatase domains -Contains 1 transmembrane domain -Contains 1 fibronectin domains -Contains 1 carbonic anhydrase

15 Protégé OWL Modelling

16 Requirements Extract phosphatase sequences from rest of protein sequences from a whole genome Identify the domains present in each Compare these sequences to the formal ontology descriptions Classify each protein instance to a place in the hierarchy

17 Architecture Instance Store myGrid Services OWL DL ontology Reasoner (racer) Classified Protein Phosphatases Raw protein sequences

18 myGrid Services extract protein phosphatase sequences from whole genome using simple filtering – patmatdb EMBOSS tool used to extract proteins with phosphatase diagnostic motifs perform InterproScan to determine domain architecture transform the InterproScan results into abstract OWL instance descriptions

19 InterproScan Results

20 Conversion to abstract OWL format restriction( cardinality(1))

21 Instance Store Instance Store enables reasoning over individuals Can support much higher numbers of individuals OWL ontology is loaded into the instance store A DL reasoner (racer) is used to compare individuals to the OWL ontology definitions

22 Instance Store

23 Example Instances Protein Individual Dual Specificity Phosphatase DUSE restriction( cardinality(1)) Ontology Definition of Dual Specificity Phosphatase containsDomain IPR Necessary and Sufficient for class membership Also inherits containsDomain IPR from Parent Class PTP

24 Results Human phosphatases have been classified using the system The ontology classification performed equally well as expert classification The ontology system refined classification - DUSC contains zinc finger domain characterised and conserved – but not in classification - DUSA contains a disintegrin domain previously uncharacterised – evolutionarily conserved

25 Aspergillus fumigatus Phosphatase proteins very different from human >100 human <50 A.fumigatus Whole subfamilies missing Different fungi-specific phosphorylation pathways? No requirement for tissue-specific variations? Novel serine/threonine phosphatase with homeobox conserved in aspergillus and closely related species, but not in any other - virulence

26 Ongoing Work Phosphatases in other genomes –Trypanosomes –Plasmodium falciparum Other protein families –Ion Channels –ABC transporters –Nuclear receptors

27 Conclusions Using ontology allows automated classification to reach the standard of human expert annotation Reasoning capabilities allow interpretation of domain organisation Highlights anomalies and variations from what is known Allows fast, efficient comparative genomics studies

28 Acknowledgements PhD Supervisors: Andy Brass, Robert Stevens Group: myGrid, Phil Lord, Carole Goble Phosphatase Biologist: Lydia Tabernero Medical Research Council

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