Linear motifs and phosphorylation sites

1. Linear motifs and phosphorylation sites

2. What is a linear motif? (in molecular biology)

3. …a first taste Short sequence of amino acids encoding a particular molecular function Functional sites Linear Motifs We need a more accurate definition!

4. What are you going to learn about Linear Motifs? Where can we find them? Why are they important? Can we classify them? How can we represent them? How can we discover them? When and how can we use them? What are tools and resources to handle them?

5. What are you going to learn about Linear Motifs? Where can we find them? Why are they important? Can we classify them? How can we represent them? How can we discover them? When and how can we use them? What are tools and resources to handle them?

6. CSK phosphorylation (Y527) & SH2 ligand Myristoylation site SH3 ligand Auto phosphorylation site (Y416) Tyrosine kinsase Src has several functional sites

7. p53 is full of functional sites CYCLIN MDM2 NES TAFII31 CBP P300 S100B NLS SIR2 P300 Pin1 P-Ser-Pro isomerisation Acetylation SUMO Ubiquitinylation phosphorylation

8. The sequences of many proteins contain short, conserved motifs that are involved in recognition and targeting activities, often separate from other functional properties of the molecule in which they occur. These motifs are linear, in the sense that three-dimensional organization is not required to bring distant segments of the molecule together to make the recognizable unit. Tim Hunt (TIBS 1990)

9. The conservation of these motifs varies: some are highly conserved while others, for example, allow substitutions that retain only a certain pattern of charge across the motif. Tim Hunt (TIBS 1990)

10. A more accurate definition • short, common stretches of polypeptide chains (~ 3-10 amino acid residues long) • embody a distinct molecular function independent of a larger sequence/structure context. • bind with low affinity (1.0-150 M). Mediate transient interactions. • are nearly always involved in regulation • are involved in protein/domain-protein/domain interactions • often reside in disordered or low-complexity regions • often become ordered upon binding to another protein or domain • occurrences of LMs seem to arise or disappear as a result of point mutations

11. What are you going to learn about Linear Motifs? Where can we find them? Why are they important? Can we classify them? How can we represent them? How can we discover them? When and how can we use them? What are tools and resources to handle them?

12. Why are they important? Evolutionary unrelated protein sharing a functional feature are likely to contain similar linear motifs This may be the result of - convergent evolution - evolutionary conservation in a divergent evolution process In any case, linear motifs are indicative of functions In other words… They are made up of the amino acid residues encoding a functional site With the appropriate tools, they can be used to identify: •protein functions •functional regions (in a protein sequence and on its three- dimensional structure, if available)

13. What are you going to learn about Linear Motifs? Where can we find them? Why are they important? Can we classify them? How can we represent them? How can we discover them? When and how can we use them? What are tools and resources to handle them?

14. Can we classify LMs? How?

15. Can we classify LMs? How? Functional site (Linear Motif) Functional group

16. PRACTICE: Let’s find linear motifs in human p53… Go to the UniProt website: http://www.uniprot.org/ Type p53 in the Query text box and select P04637 or Type directly either P04637 or P53_HUMAN in the Query text box Work in groups and analyse the p53 entry record: • how many LMs can you identify? • which function(s) are they indicative of? • are they always annotated as “motif”? • can you classify them according to the 4 categories?

17. What are you going to learn about Linear Motifs? Where can we find them? Why are they important? Can we classify them? How can we represent them? How can we discover them? When and how can we use them? What are tools and resources to handle them?

18. How can we represent LMs? Alignment of cyclin ligands inhibitors Regular expression: [RK].L.{0,1}[FLIV]

19. How can we represent LMs? Alignment of cyclin ligands inhibitors Regular expression: [RK].L.{0,1}[FLIV]

20. Regular Expression (regexp) L: single amino acid “L” = Leucine [KR]: different amino acids allowed at this position x or .: wildcard {0,1}: variable length

21. Regular Expression: Examples

22. Before we describe what regexp are useful for, let’s briefly see how to discover de novo motifs In some cases, the structure and function of an unknown protein which is too distantly related to any protein of known structure to detect its affinity by overall sequence alignment may be identified by its possession of a particular cluster of residues types classified as a motifs. The motifs, or templates, or fingerprints, arise because of particular requirements of binding sites that imposevery tight constraint on the evolution of portions of a protein sequence Arthur Lesk, 1988

23. What are you going to learn about Linear Motifs? Where can we find them? Why are they important? Can we classify them? How can we represent them? How can we discover them? When and how can we use them? What are tools and resources to handle them?

24. In contrast to domains, which are readily detectable by sequence comparison, linear motifs are difficult to discover due to their short length, a tendency to reside in disordered regions in proteins, and limited conservation outside of closely related species. Neduva et al. PLoS Biology 2005

25. De novo Linear Motif discovery  Study literature paper(s)/review(s) on a group of unrelated proteins sharing a function  Build an alignment of these proteins  Add to the alignment other sequences relevant to the subject under consideration  Pay attention to the residues and regions thought or proved to be important to the biological function of that group of proteins: • enzyme catalytic sites • PTM sites • regions involved in binding  Try to find a short conserved sequence which includes functionally important residues

26. Discovery of de novo Linear Motif There are algorithms that do it automatically Neduva et al. PLoS Biology 2005

27. Discovery of de novo Linear Motif Our central hypothesis is that proteins with a common interaction partner will share a feature that mediates binding, either a domain or a linear motif. In the absence of a shared domain, a linear motif could well be the only common sequence feature and might thus be detectable simply by virtue of over-representation, which is the basis of our approach. Neduva et al. PLoS Biology 2005

28. A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins. Edwards et al. PLoS ONE 2007

29. PRACTICE: Discovery of de novo Linear Motifs Dilimot http://dilimot.russelllab.org/ SLIMFinder http://www.southampton.ac.uk/~re1u06/software/slimfinder/

30. What are you going to learn about Linear Motifs? Where can we find them? Why are they important? Can we classify them? How can we represent them? How can we discover them? When and how can we use them? What are tools and resources to handle them?

31. Linear Motif Databases ELM PROSITE R.[RK]{1,2}.R R-x-[RK]-x(1,2)-R 1632 documentation entries (domains and functional sites) 174 manually annotated motifs 16-03-2012

32. What regular expressions are useful for? How can we use regular expressions? Regular expressions can be used to search for motifoccurrences in (uncharacterised) protein sequences There are algorithms that do this for us We call the occurrence of a motif in a sequence an INSTANCE of that motif A motif (a regexp) can have many instances KKVAVVRTPPKSPSSAKSRL ISPPTPKPRPPRPLPVAPGS EDQILKKPLPPEPAAAPVST SHRKTKKPLPPTPEEDQILK TRICKIYDSPCLPEAEAMFA TAU_HUMAN P85A_HUMAN BTK_HUMAN BTK_HUMAN RAD51_HUMAN SH3 ligand motif [RKY]..P..P

33. Prediction of new instances of Linear Motifs INPUT: a protein sequence OUTPUT: PROSITE or user-defined motif matches in the input sequence ScanProsite Allows the search for user-defined regular expressions INPUT: a protein sequence OUTPUT: scansite motif matches in the input sequence Scansite INPUT: a protein sequence OUTPUT: ELM motif matches in the input sequence ELM INPUT: a protein sequence OUTPUT: MiniMotifMiner motif matches in the input sequence MiniMotifMiner

34. PRACTICE: Prediction of new instances of Linear Motifs Go to the ScanProsite website and search for the RGD motif in the SwissProt database http://prosite.expasy.org/scanprosite/ R-G-D Select database How many hits? How many hits are expected by chance?

35. Regular expression pros and cons Unfortunately matches to these motifs are not significant, providing a signal-to-noise problem for bioinformatics tools

36. Overprediction and context information

37. The site must be in the correct cellular context (subcellular localisation) The site is only relevant in a specific taxonomy range The site must be in correct molecular context - accessible - usually not in globular domains, - often together with certain types of co-domains Functional sites only work in proper context The cell knows how to discriminate TP from FP !!! Knowledge of context can provide the basis for filters for improved prediction of functional sites

38. For example…

39. Globular domain filter Motifs are mostly found in disordered regions The disordered regions are proving to be rich in Linear Motifs Src kinase We can exploit this observation and filter out motif matches inside domains

40. Structural Filter Motif matches are not ALWAYS outside domains Inside domains they are unlikely unless in surface loops When inside a domain, a motif match is more likely to be a True Positive (TP) if it occurs in a flexible (i.e. loop, turn or linker) and accessible region of the domain

41. The RGD motif is recognized by different members of the integrin family An exposed instance of the RGD motif in a domain An instance of the RGD motif in a region outside a domain

42. MOD_N-GLC_1 (.(N)[^P][ST]..) is a motif for N-glycosilation site Two MOD_N-GLC_1 motifs in a domain

43. Structural Filter We can think to implement a filter that is based on the three-dimensional features of motifs (i.e. their accessibility and secondary structure types) If the match is not accessible low score If the match is in -helix low score low score If the match is in -strand

44. Other features that can be used to filter out FPs: • Taxonomy • Cellular compartment • Evolutionary conservation Davey NE et al. Mol Biosyst 2011

45. Why is a Conservation Score useful for linear motif prediction? Improve the prediction of LM instances by discarding those matches that are unlikely to be functional because they have not been conserved during the evolution of the protein sequences

46. There is a resource which implements these filters It associates a score to occurrences of motifs based on • Cellular context • Molecular context • Domain context • Disorder • Taxonomy • Evolutionary conservation

47. The Eukaryotic Linear Motif (ELM) Resource implements a logical filtering system to reduce false matches

48. The Eukaryotic Linear Motif (ELM) Resource • Repository of information about functional sites • (including experimentally reported instances) • A motif-based query tool to find possible new functional sites • A logical filtering system to reduce false matches

49. The ELM Resource - An overview

50. PRACTICE: The ELM server (http://elm.eu.org/) Go to the ELM server Search for motif matches in the EH domain-binding mitotic phosphoprotein