Protein function and classification

1. Protein function and classification www.ebi.ac.uk/interpro Hsin-Yu Chang www.ebi.ac.uk

2. Protein classification could help scientists to gain information about protein functions.

3. Greider and Blackburn discovered telomerase in 1984 and were awarded Nobel prize in 2009. Which model organism they used for this study ? 3. Mouse 2. Saccharomyces cerevisiae 1. Tetrahymena 4. Human

4. 1995 Clone hTR 1995/1997 Clone hTERT 1997 Telomerase knockout mouse 1989 Telomere hypothesis of cell senescence Szostak 1999/2000… Telomerase/telomere dysfunctions and cancer 1998 Ectopic expression of telomerase in normal human epithelial cells cause the extension of their lifespan 1984 Discovery of telomerase Greider and Blackburn A single Tetrahymena cell has 40,000 telomeres, whereas a human cell only has 92. Gilson and Ségal-Bendirdjian, Biochimie, 2010.

5. Therefore, classify proteins into families and identify protein homologues can help scientists to gather more information about their favourite proteins.

6. However, in the lab, what do we usually do to analyse protein sequences and find out their functions?

7. How can we annotate ProteinA ? >ProteinA MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLTKGPSKLNDRADSRRSLWDQGNFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLTLTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSIVYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPLHLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAKVSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCVRCRHRRRQAERMSQIKRLLSEKKTCQCPHRFQKTCSPI

8. Protein BLAST Publications - text books or papers UniProt PDB Specialized protein databases such as SGD, the human protein atlas, etc. What I used to do:

9. BLAST (Basic Local Alignment Tool) : compares protein sequences to sequence databases and calculates the statistical significance of matches.

10. BLAST • Drawbacks: • sometimes struggle with multi-domain proteins • less useful for weakly-similar sequences (e.g., divergent homologues) • Advantages: • Relatively fast • User friendly • Very good at recognising similarity between closely related sequences

11. Using BLAST to find clues of protein functions-when it goes well

12. Pairwise alignment of two proteins: CD4 from two closely-related species

13. Using BLAST to find clues of protein functions-when it does not give you much information

14. Using BLAST to find clues of protein functions-when it does not give you much information

15. Because BLAST performs localpairwise alignment, it: • Cannot encode the information found in a multiple sequence alignment that show you conserved sites.

16. 60S acidic ribosomal protein P0: multiple sequence alignment Using pairwise alignment could miss out on conserved residues

17. An alternative approach: protein signature search

18. An alternative approach: protein signature search • Construction of a multiple sequence alignment (MSA) from characterised protein sequences. • Modelling the pattern of conserved amino acids at specific positions within a MSA. • Use these models to infer relationships with the characterisedsequences • This is the approach taken by protein signature databases

19. Three different protein signature approaches Patterns Single motif methods Sequence alignment Profiles & Hidden Markov Models (HMMs) Full alignment methods Fingerprints Multiple motif methods

20. Protein databases that use signature approaches Profiles Protein features  (sites) Functional annotation of families/domains HAMAP Structural domains Patterns Finger prints Hidden Markov Models

21. Patterns

22. PS00000 Patterns Patterns are usually directed against functional sequence features such as: active sites, binding sites, etc. Sequence alignment Motif ALVKLISG AIVHESAT CHVRDLSC CPVESTIS Pattern sequences [AC] – x -V- x(4) - {ED} Regular expression Pattern signature

23. Patterns • Drawbacks: • Simple but less flexible • Advantages: • Strict - a pattern with very little variability and can produce highly accurate matches

24. Fingerprints

25. Motif 1 Motif 2 Motif 3 xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx Motif sequences Fingerprint signature PR00000 Fingerprints: a multiple motif approach Sequence alignment Define motifs Weight matrices

26. The significance of motif context • Identify small conserved regions in proteins • Several motifs  characterise family order 1 2 3 interval

27. Fingerprints • Good at modeling the often small differences between closely related proteins • Distinguish individual subfamilies within protein families, allowing functional characterisation of sequences at a high level of specificity

28. Profiles & HMMs

29. Profiles & HMMs Whole protein Sequence alignment Entire domain Define coverage xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxx Use entire alignment of domain or protein family xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Build model (Profile or HMMs) Profile or HMM signature

30. Profiles Start with a multiple sequence alignment Amino acids at each position in the alignment are scored according to the frequency with which they occur Scores are weighted according to evolutionary distance using a BLOSUM matrix • Good at identifying homologues

31. HMMs Start with a multiple sequence alignment Amino acid frequency at each position in the alignment and their transition probabilities are encoded Insertions and deletions are also modelled • Can model very divergent regions of alignment • Very good at identifying evolutionarily distant homologues

32. Three different protein signature approaches Patterns Single motif methods Profiles & HMMs hidden Markov models Full alignment methods Fingerprints Multiple motif methods

33. www.ebi.ac.uk/interpro

34. HAMAP Profiles Protein features  (sites) Functional annotation of families/domains Structural domains Patterns Finger prints Hidden Markov Models

35. The aim of InterPro Protein sequences Family entry: description, proteins matched and more information. Domain entry: description, proteins matched and more information. Site entry: description, proteins matched and more information.

36. What is InterPro? • InterProis an integrated sequence analysis resource • It combines predictive models (known as signatures) from different databases • It provides functional analysis of protein sequences by classifying them into families and predicting domains and important sites

37. Facts about InterPro • First release in 1999 • 11 partner databases • Add annotation to UniProtKB/TrEMBL • Provides matches to over 80% of UniProtKB • Source of >85 million Gene Ontology (GO) mappings to >24 million distinct UniProtKBsequences • 50,000 unique visitors to the web site per month> 2 million sequences searched online per month. Plus offline searches with downloadable version of software

38. InterPro signature integration process • Signatures are provided by member databases • They are scanned against the UniProt database to see which sequences they match • Curators manually inspect the matches before integrating the signatures into InterPro InterPro curators

39. InterPro signature integration process • Signatures representing the same entity are integrated together • Relationships between entries are traced, where possible • Curators add literature referenced abstracts, cross-refs to other databases, and GO terms

40. http://www.ebi.ac.uk/interpro/

41. How can we annotate ProteinA by using InterPro? >ProteinA MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLTKGPSKLNDRADSRRSLWDQGNFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLTLTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSIVYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPLHLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAKVSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCVRCRHRRRQAERMSQIKRLLSEKKTCQCPHRFQKTCSPI

42. Search using protein sequences

43. Family

44. Type

45. InterPro entry types Proteins share a common evolutionary origin, as reflected in their related functions, sequences or structure. Ex. Telomerase family. Family Domain Distinct functional, structural or sequence units that may exist in a variety of biological contexts. Ex. DNA binding domain. Short sequences typically repeated within a protein. Ex. Tubulin binding repeats in microtubule associated protein Tau. Repeats Active Site Binding Site Conserved Site PTM Sites Ex. Phosphorylation sites, ion binding sites, tubulin conserved site.

46. Type Name Identifier Contributing signatures Description References GO terms