Introduction to InterPro

1. Introduction to InterPro Amaia Sangrador InterPro curator amaia@ebi.ac.uk

2. What is InterPro? DIAGNOSTICS RESOURCE : InterPro uses signatures from several different databases (referred to as member databases) to predict information about proteins * Provides functional analysis of proteins by classifying them into families and predicting domains and important sites * Adds information about the signatures and the types of proteins they match

3. InterPro Consortium Consortium of 11 major signature databases

4. Why do we need predictive annotation tools?

5. What is UniProt? Based on the original work on PIR , Swiss-Prot and TrEMBL Collaboration between EBI, SIB and PIR The mission of UniProt is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequence and functional information.

6. UniProtKB Protein knowledgebase UniProtKB/Swiss-Prot Reviewed UniRef Sequence clusters UniMES Metagenomic and environmental sample sequences High-quality manual annotation UniRef100 UniRef90 UniRef50 UniProtKB/TrEMBL Unreviewed Automatic annotation UniParc - Sequence archive Current and obsolete sequences EMBL/GenBank/DDBJ, Ensembl, RefSeq, PDB, other resources

7. TrEMBL uncharacterised sequence groups of related proteins (same family or share domains) Annotation using InterPro automatic annotation pipeline InterPro CGCGCCTGTACGCTGAACGCTCGTGACGTGTAGTGCGCG protein signatures CGCGCCTGTACGCTGAACGCTCGTGACGTGTAGTGCGCG manually annotated sequence Swiss-Prot

8. Protein family classification Given a set of 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?

9. Protein family classification : BLAST (pairwise comparisons)

10. Protein family classification: BLAST

11. Limitations with Pairwise comparisons BLAST alignment of 2 proteins: 60S acidic ribosomal protein P0 from 2 species

12. Limitations with Pairwise comparisons

13. Protein family classification: signature databases • Alternatively, we can seek ‘patterns’ that will allow us to infer relationships with previously-characterised sequences • This is the approach taken by ‘signature’ databases

14. Protein signatures • More sensitive homology searches • Each member database creates signatures using different methods and methodologies: • manually-created sequence alignments • automatic processes with some human input and correction • entirely automatically.

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

16. Member databases METHODS Hidden Markov Models Finger- Prints Profiles Patterns Sequence Clusters Protein features (active sites…) Prediction of conserved domains Structural Domains Functional annotation of families/domains

17. 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)

18. 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

19. 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

20. Prosite patterns Pattern/motif in sequence  regular expression EXAMPLE: PS00296; Chaperonins cpn60 signature  (PATTERN) A-[AS]-{L}-[DEQ]-E-{A}-{Q}-{R}-x-G(2)-[GA] >sp|P29197|CH60A_ARATH Chaperonin CPN60, mitochondrial OS=Arabidopsis thaliana MYRFASNLASKARIAQNARQVSSRMSWSRNYAAKEIKFGVEARALMLKGVEDLADAVKVT MGPKGRNVVIEQSWGAPKVTKDGVTVAKSIEFKDKIKNVGASLVKQVANATNDVAGDGTT CATVLTRAIFAEGCKSVAAGMNAMDLRRGISMAVDAVVTNLKSKARMISTSEEIAQVGTI SA NGEREIGELIAKAMEKVGKEGVITIQDGKTLFNELEVVEGMKLDRGYTSPYFITNQKT QKCE LDDPLILIHEKKISSINSIVKVLELALKRQRPLLIVSEDVESDALATLILNKLRAG IKVCAIKAPGF GENRKANLQDLAALTGGEVITDELGMNLEKVDLSMLGTCKKVTVSKDDT VILDGAGDKKGI EERCEQIRSAIELSTSDYDKEKLQERLAKLSGGVAVLKIGGASEAEVG EKKDRVTDALNATK AAVEEGILPGGGVALLYAARELEKLPTANFDQKIGVQIIQNALKTP VYTIASNAGVEGA VIVGKLLEQDNPDLGYDAAKGEYVDMVKAGIIDPLKVIRTALVDAAS VSSLLTTTEAVVVDLP KDESESGAAGAGMGGMGGMDY

21. 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

22. 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

23. metabotropic glutamate receptors etc cAMP receptors rhodospin-like secretin-like adenosine receptors opsin receptors dopamine receptors somatostatin receptor type 1 somatostatin receptors somatostatin receptor type 2 histamine receptors somatostatin receptor type 3 etc etc PRINTS families are hierarchical Different motifs describe subfamilies G protein-coupled receptors

24. 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

25. Profiles Built using weight matrices Hidden Markov Models (HMM) More sophisticated algorithm Models insertions and deletions More flexible (can use partial alignments)

26. 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.

27. 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

28. 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

29. InterPro entry

30. InterPro entry

31. The InterPro entry: types Proteins share a common evolutionary origin, as reflected in their related functions, sequences or structure Family Domain Distinct functional, structural or sequence units that may exist in a variety of biological contexts Short sequences typically repeated within a protein Repeats Active Site Binding Site Conserved Site PTM Sites

32. InterPro Entry Groups similar signatures together Adds extensive annotation Adds extensive annotation Links to other databases Links to other databases Structural information and viewers • Quality control • Removes redundancy

33. 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

34. 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

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

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

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

38. 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

39. 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

40. 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

41. Understanding signatures:

42. Non-overlapping signatures can be describing the same thing Not always possible to use signature overlap to determine how family signatures are related PF03157 336 protein hits PR00210 331 protein hits e.g. High molecular weight glutenins Two very different signatures both describing the same thing!

43. PFAM shows domain is composed of two types of repeated sequence motifs SUPERFAMILY shows the potential domain boundaries Some signatures give us similar, but complementary information www.ebi.ac.uk/interpro

44. 1) Signature method 2) Duplicated domains 3) Repeated elements 4) Non-contiguous domains Discontinuous Signatures Require Interpretation www.ebi.ac.uk/interpro

45. e.g. PRINTS – discrete motifs 2) Duplicated domains 3) Repeated elements 4) Non-contiguous domains Discontinuous Signatures Require Interpretation 1) Signature method www.ebi.ac.uk/interpro

46. e.g. SSF - duplication consisting of 2 domains with same fold 3) Repeated elements 4) Non-contiguous domains Discontinuous Signatures Require Interpretation 1) Signature method 2) Duplicated domains www.ebi.ac.uk/interpro

47. e.g. Kringle, WD40 Discontinuous Signatures Require Interpretation 1) Signature method 2) Duplicated domains 3) Repeated elements 4) Non-contiguous domains www.ebi.ac.uk/interpro

48. Structural domains can consist of non-contiguous sequence Discontinuous Signatures Require Interpretation 1) Signature method 2) Duplicated domains 3) Repeats 4) Non-contiguous domains www.ebi.ac.uk/interpro

49. Discontinuous Signatures Require Interpretation 1) Signature method 2) Duplicated domains 3) Repeats 4) Non-contiguous domains www.ebi.ac.uk/interpro

50. Searching InterPro: