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Joint EBI-Wellcome Trust

Joint EBI-Wellcome Trust. Summer School 14-18 June 2010. Alignment Utopia. Overview. What alignments are useful for What ’ s in a sequence? understanding similarities & differences What ’ s in an alignment? getting the alignment & the metaphor right How alignments can mislead

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Joint EBI-Wellcome Trust

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  1. Joint EBI-Wellcome Trust Summer School 14-18 June 2010

  2. Alignment Utopia Teresa K.Attwood University of Manchester

  3. Overview • What alignments are useful for • What’s in a sequence? • understanding similarities & differences • What’s in an alignment? • getting the alignment & the metaphor right • How alignments can mislead • How we make alignments • introducing Utopia Teresa K.Attwood University of Manchester

  4. What alignments are useful for • Alignments lie at the heart of sequence analyses • They provide • spring-boards for understanding evolutionary relationships • clues to structurally/functionally important regions/residues • the means to build diagnostic signatures, etc., etc., etc… • So, tools for alignment are essential

  5. What's in a sequence?

  6. The challenge • To understand what patterns of conservation mean • to recognise what’s similar • to identify what’s different • & to know when differences are meaningful v Teresa K.Attwood University of Manchester

  7. Understanding similarities & differences • Alignments are a mine of structural/functional/evolutionary info domain family super-family families sub-families Teresa K.Attwood University of Manchester

  8. database search Recognising similarities loop region TM domain TM domain

  9. Similarities are informative They give insights into shared high-level functions Teresa K.Attwood University of Manchester

  10. database search Identifying differences loop region loop region

  11. Differences are informative They give insights into unique functional specificities The more differences, the more you learn about the tool’s functional niche Teresa K.Attwood University of Manchester

  12. K1 K3 PTP5 K2 B A PTP1 K4 K5 PTP1 C D PTP2 K6 PTP4 PTP3 WPD K7 K8 PTP6 HCX5R Teresa K.Attwood University of Manchester

  13. Similarities & differences • The challenge is to spot patterns in data • to recognise what’s similar • to identify what’s different • & to know when differences are meaningful Haemoglobin beta Sickle cell haemoglobin

  14. Opsin - green-sensitive cone photoreceptor Rhodopsin - rod cell, achromatic receptor

  15. 1,4-beta-N-acetylmuramidase C - bacteriolytic protein Lactose synthase B protein - milk protein synthesis

  16. Delta crystallin - non-enzymatic, structural eye-lens protein Argininosuccinate lyase - amino acid biosynthesis

  17. What’s in an alignment? • Nothing… • unless you get the alignment right! • unless you get the metaphor right! • Say what?! Teresa K.Attwood University of Manchester

  18. Protein sequence nomenclature • The standard IUB/IUPAC 1- or 3-letter codes • GGlycineGlyPProlinePro • AAlanineAlaVValineVal • LLeucineLeuIIsoleucineIle • MMethionineMetCCysteineCys • FPhenylalaninePheYTyrosineTyr • WTryptophanTrpHHistidineHis • KLysineLysRArginineArg • QGlutamineGlnNAsparagineAsn • EGlutamic acidGluDAspartic acidAsp • SSerineSerTThreonineThr • B Asp or AsnAsxZ Glu or GlnGlx • X anything/unknownXxx/Unk Teresa K.Attwood University of Manchester

  19. Don’t forget the biology • The 1-letter code is a convenient short-hand • Imagine MethionineAlanineAsparticAcidIsoleucineGlutamineLeucineSerine… • MADIQLS is much more efficient! • But, with this notation, it’s easy to forget that the characters have biological meaning! • The letters are abstract representations of biology • the amino acids they represent have properties! Teresa K.Attwood University of Manchester

  20. Amino acids have properties • Simplistically, we can divide their properties into hydrophobic & hydrophilic: AVLIMGPCFYWNQDESTHKR • But there are many hydropathy scales, with subtle differences between them: e.g., • is Trp hydrophobic or hydrophilic? • what about Pro? And Tyr? And Lys? • Let’s take a closer look • consider the following set of sequences Teresa K.Attwood University of Manchester

  21. How similar are they? It all depends on how you look… Teresa K.Attwood University of Manchester

  22. How similar are they? Polar/non-polar

  23. How similar are they? Kyte hydropathy scale

  24. How similar are they? Zimmerman hydropathy scale

  25. hydrophobic hydrophilic (non-polar) (polar) neutral aliphatic negative charged aromatic positive Amino acid properties overlap S M T I V Q N C E A P L G D K Y R F H W What you see depends on how you look…

  26. Getting the metaphor right • For most practical purposes, a fairly fine-grained property classification is helpful • aliphatic:A (Ala) V (Val) L (Leu) I (Ile) M (Met) • special structural: G (Gly) P (Pro) • sulphur containing: C (Cys) • aromatic:F (Phe) Y (Tyr) W (Trp) • basic:K (Lys) R (Arg) H (His) • acidic:E (Glu) D (Asp) • neutral:Q (Gln) N (Asn) S (Ser) T (Thr) Teresa K.Attwood University of Manchester

  27. So what’s this metaphor thing? • Here, we’re using colour as a metaphor for properties • AVLIM – aliphatic • GP - special structural • C - sulphur containing • FYW - aromatic • KRH – basic • DE – acidic • QNST – polar neutral • Alignments coloured in this way mean something • different metaphors have different meanings! Teresa K.Attwood University of Manchester

  28. How similar are they? Teresa K.Attwood University of Manchester

  29. How similar are they? Coloured by conservation score

  30. How similar are they? Polar/non-polar

  31. How similar are they? Polar/non-polar + Phe/Pro/etc.

  32. How similar are they? Teresa K.Attwood University of Manchester

  33. How similar are they? Add acidic residues

  34. How similar are they? Add basic residues

  35. How similar are they? Add cysteine residues

  36. How similar are they? Add polar, unchargedresidues

  37. How similar are they? Add aromatic residues

  38. How similar are they? Add Pro/Gly residues

  39. How similar are they? The same sequences, aligned using different algorithms & using different colour metaphors… The resulting alignments are completely different, & the colour metaphors are contradictory… What does it mean? Teresa K.Attwood University of Manchester

  40. How similar are they? What properties are conserved? Do they look so similar now? Teresa K.Attwood University of Manchester

  41. How alignments can mislead So let’s find them… But key to these proteins are 7 GXXG motifs Looks persuasive with its filled-in boxes! Alignment published in The Plant Journal, 2007 Teresa K.Attwood University of Manchester

  42. How alignments can mislead Let’s now compare with a Utopian view Teresa K.Attwood University of Manchester

  43. So how do we make alignments? • Automatically • lots of options, many presented here • Manually • lots more options, many presented here • Or using a combination of both • automatic algorithms are notoriously unreliable & results often need manual refinement • Utopia helps us do this Teresa K.Attwood University of Manchester

  44. Introducing Utopia • Utopia is protein sequence alignment, analysis & visualisation suite • It integrates • manual editing & automatic alignment algorithms • BLAST searches & TM-domain prediction • UniProt annotations & structure visualisation • & much more • It promises to make life easier… Teresa K.Attwood University of Manchester

  45. Alignment Utopia • Today’s hands-on will explore a number of alignment scenarios using Utopia Teresa K.Attwood University of Manchester

  46. Alignment Utopia • We will also introduce Utopia Documents • visit the Website to see the movie Teresa K.Attwood University of Manchester

  47. Remember - we use biology-ignorant tools to analyse such complex, dynamic systems…‘string’-matchers Epilogue To understand biological function & evolution, we must be realistic about what such naïve tools can achieve… Teresa K.Attwood University of Manchester

  48. Ground rules • Don't always believe what programs tell you • they're often misleading & sometimes wrong! • Don't always believe whatdatabases tell you • they're often misleading & sometimes wrong! • Don't always believe what speakers tell you • they're sometimes misleading & oftenwrong! • The bottom line - if we’re striving to do good science, it’s imperative to think critically • computers can’t & won’t think for us! Teresa K.Attwood University of Manchester

  49. If we get it right… Eureka! Teresa K.Attwood University of Manchester

  50. Thanks for your dogged attention!Any questions? http://utopia.cs.man.ac.uk/ http://www.youtube.com/watch?v=rIOgARl1a3E http://www.scivee.tv/node/17389 Teresa K.Attwood University of Manchester

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