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Using InterPro for functional analysis of protein sequences

Using InterPro for functional analysis of protein sequences. Alex Mitchell InterPro team mitchell@ebi.ac.uk. Why do we need predictive annotation tools?. Given a set of uncharacterised sequences, we usually want to know:. what are these proteins; to what family do they belong?.

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Using InterPro for functional analysis of protein sequences

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  1. Using InterPro for functional analysis of protein sequences Alex Mitchell InterPro team mitchell@ebi.ac.uk

  2. Why do we need predictive annotation tools?

  3. Given a set of uncharacterised sequences, we usually want to know: • what are these proteins; to what family do they belong? • what is their function; how can we explain this in structural terms?

  4. Pairwise alignment approaches (e.g., BLAST)

  5. Pairwise alignment approaches (e.g., BLAST)

  6. Pairwise alignment approaches (e.g., BLAST) Good at recognising similarity between closely related sequences Perform less well at detecting divergent homologues

  7. The protein signature approach • Alternatively, we can model the conservation of amino acids at specific positions within a multiple sequence alignment, seeking ‘patterns’ across closely related proteins • We can then use these models to infer relationshipswith previously characterised sequences • This is the approach taken by protein signature databases • They go about this in 3 different ways...

  8. Protein signature methods (patterns) (fingerprints) (profiles & HMMs)

  9. Families Domains Sequence features

  10. Multiple sequence alignment What are protein signatures? Protein family/domain Build model Search UniProt Protein analysis Significant match ITWKGPVCGLDGKTYRNECALL Mature model AVPRSPVCGSDDVTYANECELK

  11. Diagnostic approaches (sequence-based) Single motif methods Regex patterns (PROSITE) Full domain alignment methods Profiles (Profile Library) HMMs (Pfam) Multiple motif methods Identity matrices (PRINTS)

  12. Motif Define pattern xxxxxx xxxxxx xxxxxx xxxxxx Extract pattern sequences Build regular expression C-C-{P}-x(2)-C-[STDNEKPI]-x(3)-[LIVMFS]-x(3)-C Pattern signature PS00000 Patterns Sequence alignment

  13. Patterns Advantages • Anchoring the match to the extremity of a sequence • <M-R-[DE]-x(2,4)-[ALT]-{AM} • Some aa can be forbidden at some specific positions which can help to distinguish closely related subfamilies • Short motifs handling - a pattern with very few variability and forbidden positions, can produce significant matches e.g. conotoxins: very short toxins with few conserved cysteines C-{C}(6)-C-{C}(5)-C-C-x(1,3)-C-C-x(2,4)-C-x(3,10)- C Drawbacks • Simple but less powerful Patterns are mostly directed against functional residues: active sites, PTM, disulfide bridges, binding sites

  14. Motif 1 Motif 2 Motif 3 Define motifs xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx Extract motif sequences Correct order Fingerprint signature 1 2 3 Correct spacing PR00000 Fingerprints Sequence alignment Weight matrices

  15. 1 2 3 4 5 The significance of motif context • Identify small conserved regions in proteins • Several motifs  characterise family • Offer improved diagnostic reliability over single motifs by virtue of the biological context provided by motif neighbours order interval

  16. Profiles & HMMs Whole protein Sequence alignment Entire domain Define coverage xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxx Use entire alignment for domain or protein xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Models insertions and deletions Build model Profile or HMM signature

  17. PROSITE and HAMAP profiles: a functional annotation perspective • PROSITEdomains: high quality manually curated seeds (using biologically characterized UniProtKB/Swiss-Prot entries), documentation and annotation rules. Oriented toward functional domain discrimination. • HAMAPfamilies: manually curatedbacterial, archaeal and plastid protein families (represented by profiles and associated rules), covering some highly conserved proteins and functions.

  18. HMM databases • Sequence-based • PIR SUPERFAMILY: families/subfamilies reflect the evolutionary relationship • PANTHER: families/subfamilies model the divergence of specific functions • TIGRFAM: microbial functional family classification • PFAM : families & domains based on conserved sequence • SMART: functional domain annotation • Structure-based • SUPERFAMILY : models correspond to SCOP domains • GENE3D: models correspond to CATH domains

  19. Why we created InterPro • By uniting the member databases, InterPro capitalises on their individual strengths, producing a powerful diagnostic tool & integrated database • to simplify & rationalise protein analysis • to facilitate automatic functional annotation of uncharacterised proteins • to provide concise information about the signatures and the proteins they match, including consistent names, abstracts (with links to original publications), GO terms and cross-references to other databases

  20. Finger- Prints Hidden Markov Models Profiles Patterns Structural domains Protein  features  (sites) Functional annotation of families/domains InterPro

  21. InterPro integration process Member databases InterPro + annotation Protein signatures

  22. InterPro Entry Groups similar signatures together Adds extensive annotation Adds extensive annotation Links to other databases Links to other databases Structural information and viewers • Hierarchical classification

  23. Interpro hierarchies: Families FAMILIES can have parent/child relationships with other Families • Parent/Child relationships are based on: • Comparison of protein hits • child should be a subset of parent • siblings should not have matches in common • Existing hierarchies in member databases • Biological knowledge of curators

  24. Interpro hierarchies: Domains DOMAINS can have parent/child relationships with other domains

  25. Domains and Families may be linked through Domain Organisation Hierarchy

  26. InterPro Entry Groups similar signatures together Adds extensive annotation Adds extensive annotation Links to other databases Links to other databases Structural information and viewers

  27. InterPro Entry Groups similar signatures together Adds extensive annotation Adds extensive annotation Links to other databases Links to other databases Structural information and viewers The Gene Ontology project provides a controlled vocabulary of terms for describing gene product characteristics

  28. InterPro Entry Groups similar signatures together Adds extensive annotation Adds extensive annotation Links to other databases Links to other databases Structural information and viewers UniProt KEGG ... Reactome ... IntAct ... UniProt taxonomy PANDIT ... MEROPS ... Pfam clans ... Pubmed

  29. InterPro Entry Groups similar signatures together Adds extensive annotation Adds extensive annotation Links to other databases Links to other databases Structural information and viewers PDB 3-D Structures SCOP Structural domains CATH Structural domain classification

  30. Searching InterPro

  31. Searching InterPro Protein family membership Domain organisation Domains, repeats & sites GO terms

  32. Searching InterPro

  33. Searching InterPro

  34. InterProScan access Interactive: http://www.ebi.ac.uk/Tools/pfa/iprscan/ Webservice (SOAP and REST): http://www.ebi.ac.uk/Tools/webservices/services/pfa/iprscan_rest http://www.ebi.ac.uk/Tools/webservices/services/pfa/iprscan_soap Downloadable: ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/

  35. Searching InterPro

  36. Searching InterPro: BioMart

  37. BioMart Search BioMart allows more powerful and flexible queries • Large volumes of data can be queried efficiently • The interface is shared with many other bioinformatics resources • It allows federation with other databases: • PRIDE (mass spectrometry-derived proteins and peptides • REACTOME (biological pathways)

  38. BioMart Search • Choose Dataset • Choose InterPro BioMart

  39. BioMart Search • Choose Dataset • Choose InterPro BioMart • Choose InterPro entries or protein matches

  40. BioMart Search • Choose Filters • Search specific entries, signatures or proteins

  41. BioMart Search • Choose Filters • e.g. Filter by specific proteins

  42. BioMart Search • Choose Attributes • What results you want

  43. BioMart Search • Choose additional Dataset (optional) • This is where you link results to Pride and Reactome

  44. BioMart Search Results User manual Click to view results HTML = web-formatted table CSV = comma-separated values TSV = tab-separated values XLS = excel spreadsheet

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