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

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Presentation on theme: "Joint EBI-Wellcome Trust Summer School 14-18 June 2010."— Presentation transcript:

1 Joint EBI-Wellcome Trust Summer School June 2010

2 5/3/20152 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 5/3/2015Teresa K.Attwood University of Manchester 3

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 5/3/20154

5 5 What's in a sequence?

6 5/3/2015 To understand what patterns of conservation mean –to recognise what’s similar –to identify what’s different –& to know when differences are meaningful The challenge Teresa K.Attwood University of Manchester 6 v

7 5/3/20157 super-family domain family sub-families families Understanding similarities & differences Teresa K.Attwood University of Manchester Alignments are a mine of structural/functional/evolutionary info

8 TM domain loop region TM domain database search Recognising similarities 5/3/20158

9 Similarities are informative They give insights into shared high-level functions 5/3/20159Teresa K.Attwood University of Manchester

10 loop region database search Identifying differences loop region 5/3/201510

11 5/3/ Differences are informative They give insights into unique functional specificities Teresa K.Attwood University of Manchester The more differences, the more you learn about the tool’s functional niche

12 K1 K2 K3 K4K5 PTP1 K6 PTP2 PTP3 K7K8 PTP4 PTP5 PTP6 WPD HCX 5 R A D B C 5/3/201512Teresa K.Attwood University of Manchester

13 The challenge is to spot patterns in data –to recognise what’s similar –to identify what’s different –& to know when differences are meaningful Similarities & differences 5/3/ Haemoglobin betaSickle cell haemoglobin

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

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

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

17 Nothing… –unless you get the alignment right! –unless you get the metaphor right! Say what?! 5/3/2015Teresa K.Attwood University of Manchester 17 What’s in an alignment?

18 5/3/ Protein sequence nomenclature The standard IUB/IUPAC 1- or 3-letter codes G GlycineGlyP ProlinePro A AlanineAlaV ValineVal L LeucineLeuI IsoleucineIle M MethionineMetC CysteineCys F PhenylalaninePheY TyrosineTyr W TryptophanTrpH HistidineHis K Lysine LysR ArginineArg Q GlutamineGlnN AsparagineAsn E Glutamic acidGluD Aspartic acidAsp S Serine SerT ThreonineThr B Asp or Asn Asx Z Glu or Gln Glx X anything/unknown Xxx/Unk Teresa K.Attwood University of Manchester

19 5/3/ 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 5/3/ 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? 5/3/ It all depends on how you look… Teresa K.Attwood University of Manchester

22 How similar are they? 5/3/ Polar/non-polar

23 How similar are they? 5/3/ Kyte hydropathy scale

24 How similar are they? 5/3/ Zimmerman hydropathy scale

25 5/3/ aromatic aliphatic negative positive neutral (non-polar) hydrophobic (polar) hydrophilic charged Amino acid properties overlap V L I P A G D E R K H N Q W Y F C M S T What you see depends on how you look…

26 5/3/ 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! 5/3/201527Teresa K.Attwood University of Manchester

28 How similar are they? Teresa K.Attwood University of Manchester 5/3/201528

29 5/3/ How similar are they? Coloured by conservation score

30 Polar/non-polar How similar are they? 5/3/201530

31 Polar/non-polar + Phe/Pro/etc. How similar are they? 5/3/201531

32 5/3/ How similar are they? Teresa K.Attwood University of Manchester

33 5/3/ How similar are they? Add acidic residues

34 5/3/ How similar are they? Add basic residues

35 5/3/ How similar are they? Add cysteine residues

36 5/3/ How similar are they? Add polar, unchargedresidues

37 5/3/ How similar are they? Add aromatic residues

38 5/3/ How similar are they? Add Pro/Gly residues

39 How similar are they? 5/3/2015Teresa K.Attwood University of Manchester 39 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?

40 5/3/2015Teresa K.Attwood University of Manchester 40 How similar are they? What properties are conserved? Do they look so similar now?

41 Alignment published in The Plant Journal, /3/2015Teresa K.Attwood University of Manchester 41 How alignments can mislead Looks persuasive with its filled-in boxes! But key to these proteins are 7 GXXG motifs So let’s find them…

42 Teresa K.Attwood University of Manchester 5/3/ How alignments can mislead Let’s now compare with a Utopian view

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 5/3/2015Teresa K.Attwood University of Manchester 43

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… 5/3/2015Teresa K.Attwood University of Manchester 44

45 Alignment Utopia 5/3/2015Teresa K.Attwood University of Manchester 45 Today’s hands-on will explore a number of alignment scenarios using Utopia

46 5/3/2015Teresa K.Attwood University of Manchester 46 Alignment Utopia We will also introduce Utopia Documents –visit the Website to see the movie

47 Epilogue Remember - we use biology-ignorant tools to analyse such complex, dynamic systems…‘string’-matchers To understand biological function & evolution, we must be realistic about what such naïve tools can achieve… 5/3/201547Teresa K.Attwood University of Manchester

48 Ground rules 5/3/ Don't always believe what programs tell you –they're often misleading & sometimes wrong! Don't always believe what databases tell you –they're often misleading & sometimes wrong! Don't always believe what speakers tell you –they're sometimes misleading & often wrong! 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… 5/3/ Eureka! Teresa K.Attwood University of Manchester

50 Thanks for your dogged attention! Any questions? 5/3/201550Teresa K.Attwood University of Manchester


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