1. Bioinformatics Methods Course Multiple Sequence Alignment Burkhard Morgenstern University of Göttingen Institute of Microbiology and Genetics Department of Bioinformatics Göttingen, October/November 2006

2. Tools for multiple sequence alignment T Y I M R E A Q Y E T C I V M R E A Y E

3. Tools for multiple sequence alignment T Y I - M R E A Q Y E T C I V M R E A - Y E

4. Tools for multiple sequence alignment T Y I M R E A Q Y E T C I V M R E A Y E Y I M Q E V Q Q E Y I A M R E Q Y E

5. Tools for multiple sequence alignment T Y I - M R E A Q Y E T C I V M R E A - Y E Y - I - M Q E V Q Q E Y – I A M R E - Q Y E

6. Tools for multiple sequence alignment T Y I - M R E A Q Y E T C I V M R E A - Y E - Y I - M Q E V Q Q E Y – I A M R E - Q Y E Astronomical Number of possible alignments!

7. Tools for multiple sequence alignment T Y I - M R E A Q Y E T C I V - M R E A Y E - Y I - M Q E V Q Q E Y – I A M R E - Q Y E Astronomical Number of possible alignments!

8. Tools for multiple sequence alignment T Y I - M R E A Q Y E T C I V M R E A - Y E - Y I - M Q E V Q Q E Y – I A M R E - Q Y E Which one is the best ???

9. Tools for multiple sequence alignment Questions in development of alignment programs: (1) What is a good alignment? objective function (`score) (2) How to find a good alignment? optimization algorithm First question far more important !

10. Tools for multiple sequence alignment Before defining an objective function (scoring scheme) What is a biologically good alignment ??

11. Tools for multiple sequence alignment Criteria for alignment quality: 1. 3D-Structure: align residues at corresponding positions in 3D structure of protein! 2. Evolution: align residues with common ancestors!

12. Tools for multiple sequence alignment T Y I - M R E A Q Y E T C I V - M R E A Y E - Y I - M Q E V Q Q E - Y I A M R E - Q Y E Alignment hypothesis about sequence evolution Search for most plausible hypothesis!

13. Tools for multiple sequence alignment Compute for amino acids a and b Probability p a,b of substitution a b (or b a), Frequency q a of a Define s(a,b) = log (p a,b / q a q b )

14. Tools for multiple sequence alignment

16. Traditional objective functions: Define Score of alignments as Sum of individual similarity scores s(a,b) Gap penalty g for each gap in alignment Needleman-Wunsch scoring system (1970) for pairwise alignment (= alignment of two sequences)

17. T Y W I V T - - L V Example: Score = s(T,T) + s(I,L) + s (V,V) – 2 g

18. T Y W I V T - - L V Idea: alignment with optimal (maximal) score probably biologically meaningful. Dynamic programming algorithm finds optimal alignment for two sequences efficiently (Needleman and Wunsch, 1970).

19. Tools for multiple sequence alignment Traditional Objective functions can be generalized to multiple alignment (e.g. sum-of-pair score, tree alignment) Needleman-Wunsch algorithm can also be generalized to find optimal multiple alignment, but: Very time and memory consuming! -> Heuristic algorithm needed, i.e. fast but sub-optimal solution

20. Tools for multiple sequence alignment Most commonly used heuristic for multiple alignment: Progressive alignment (mid 1980s)

21. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP

22. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP Guide tree

23. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYP- AVVIQDNSDIKVVP--KAKIIRD YAVESEASFQPVAALERIN WLNYNEERGDFPGTYVEYIGRKKISP Profile alignment, once a gap - always a gap

24. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYP- AVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN------ WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, once a gap - always a gap

25. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN- WW--RLNDKEGYVPRNLLGLYP- AVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN------ WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, once a gap - always a gap

26. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------- WW--RLNDKEGYVPRNLLGLYP-------- AVVIQDNSDIKVVP--KAKIIRD------- YAVESEA---SVQ--PVAALERIN------ WLN-YNE---ERGDFPGTYVEYIGRKKISP Profile alignment, once a gap - always a gap

27. CLUSTAL W Most important software program: CLUSTAL W: J. Thompson, T. Gibson, D. Higgins (1994), CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment … Nuc. Acids. Res. 22, 4673 - 4680 (~ 20.000 citations in the literature)

28. Tools for multiple sequence alignment Problems with traditional approach: Results depend on gap penalty Heuristic guide tree determines alignment; alignment used for phylogeny reconstruction Algorithm produces global alignments.

29. Tools for multiple sequence alignment Problems with traditional approach: But: Many sequence families share only local similarity E.g. sequences share one conserved motif

30. Local sequence alignment Find common motif in sequences; ignore the rest EYENS ERYENS ERYAS

31. Local sequence alignment Find common motif in sequences; ignore the rest E-YENS ERYENS ERYA-S

32. Local sequence alignment Find common motif in sequences; ignore the rest – Local alignment E-YENS ERYENS ERYA-S

33. Gibbs Motive Sampler Local multiple alignment without gaps: C.E. Lawrence et al. (1993) Detecting subtle sequence signals: a Gibbs Sampling Strategy for Multiple Alignment Science, 262, 208 - 214

34. Traditional alignment approaches: Either global or local methods!

35. New question: sequence families with multiple local similarities Neither local nor global methods appliccable

36. New question: sequence families with multiple local similarities Alignment possible if order conserved

37. The DIALIGN approach Morgenstern, Dress, Werner (1996), PNAS 93, 12098-12103 Combination of global and local methods Assemble multiple alignment from gap-free local pair-wise alignments (,,fragments)

38. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

39. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

40. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

41. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

42. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

43. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

44. The DIALIGN approach atc------taatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

45. The DIALIGN approach atc------taatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaa--gagtatcacccctgaattgaataa

46. The DIALIGN approach atc------taatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaa--gagtatcacc----------cctgaattgaataa

47. The DIALIGN approach atc------taatagttaaactcccccgtgc-ttag cagtgcgtgtattactaac----------gg-ttcaatcgcg caaa--gagtatcacc----------cctgaattgaataa

48. The DIALIGN approach atc------taatagttaaactcccccgtgc-ttag cagtgcgtgtattactaac----------gg-ttcaatcgcg caaa--gagtatcacc----------cctgaattgaataa Consistency!

49. The DIALIGN approach atc------TAATAGTTAaactccccCGTGC-TTag cagtgcGTGTATTACTAAc----------GG-TTCAATcgcg caaa--GAGTATCAcc----------CCTGaaTTGAATaa

50. The DIALIGN approach Multiple alignment: atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

51. The DIALIGN approach Multiple alignment: atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaccctgaattgaagagtatcacataa (1) Calculate all optimal pair-wise alignments

52. The DIALIGN approach Multiple alignment: atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa (1) Calculate all optimal pair-wise alignments

53. The DIALIGN approach Multiple alignment: atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa (1) Calculate all optimal pair-wise alignments

54. The DIALIGN approach Fragments from optimal pair-wise alignments might be inconsistent

55. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

56. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

57. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

58. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaa--gagtatcacccctgaattgaataa

59. The DIALIGN approach atc------taatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaa--gagtatcacccctgaattgaataa

60. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

61. The DIALIGN approach Score of alignment: Define weight score for fragments based on probability of random occurrence Score of alignment = sum of weight scores of fragments Goal: find consistent set of fragments with maximum total weight

62. The DIALIGN approach Advantages of segment-based approach: Program can produce global and local alignments! Sequence families alignable that cannot be aligned with standard methods

63. T-COFFEE C. Notredame, D. Higgins, J. Heringa (2000), T-Coffee: A novel algorithm for multiple sequence alignment, J. Mol. Biol.

67. T-COFFEE T-COFFEE Less sensitive to spurious pairwise similarities Can handle local homologies better than CLUSTAL

68. T-COFFEE T-COFFEE Idea: 1. Build library of pairwise alignments 2. Alignment from seq i, j and seq j, k supports alignmetn from seq i, k.

69. Evaluation of multi-alignment methods Alignment evaluation by comparison to trusted benchmark alignments. `True alignment known by information about structure or evolution.

70. Evaluation of multi-alignment methods For protein alignment: M. McClure et al. (1994): 4 protein families, known functional sites J. Thompson et al. (1999): Benchmark data base, 130 known 3D structures (BAliBASE) T. Lassmann & E. Sonnhammer (2002): BAliBASE + simulated evolution (ROSE)

71. Evaluation of multi-alignment methods

72. 1aboA 1.NLFVALYDfvasgdntlsitkGEKLRVLgynhn..............gE 1ycsB 1 kGVIYALWDyepqnddelpmkeGDCMTIIhrede............deiE 1pht 1 gYQYRALYDykkereedidlhlGDILTVNkgslvalgfsdgqearpeeiG 1ihvA 1.NFRVYYRDsrd......pvwkGPAKLLWkg.................eG 1vie 1.drvrkksga.........awqGQIVGWYctnlt.............peG 1aboA 36 WCEAQt..kngqGWVPSNYITPVN...... 1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP...... 1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp 1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd..... 1vie 28 YAVESeahpgsvQIYPVAALERIN...... Key alpha helix RED beta strand GREEN core blocks UNDERSCORE BAliBASE Reference alignments Evaluation of multi-alignment methods

74. Result: DIALIGN best method for distantly related sequences, T-Coffee best for globally related proteins

75. Evaluation of multi-alignment methods BAliBASE: 5 categories of benchmark sequences (globally related, internal gaps, end gaps) CLUSTAL W, T-COFFEE, MAFFT, PROBCONS perform well on globally related sequences, DIALIGN superior for local similarities

76. Evaluation of multi-alignment methods Conclusion: no single best multi alignment program! Advice: try different methods!

77. Anchored sequence alignment Idea: semi-automatic alignment use expert knowledge to define constraints instead of fully automated alignment Define parts of the sequences where biologically correct alignment is known as anchor points, align rest of the sequences automatically.

78. Anchored sequence alignment NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS

79. Anchored sequence alignment NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS Anchor points in multiple alignment

80. Anchored sequence alignment NLFV ALYDFVASGDNTLSITKGEKLRVLGYNHN IIHREDKGVIYALWDYEPQND DELPMKEGDCMT GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS Anchor points in multiple alignment

81. Anchored sequence alignment -------NLF V-ALYDFVAS GD-------- NTLSITKGEk lrvLGYNhn iihredkGVI Y-ALWDYEPQ ND-------- DELPMKEGDC MT------- -------GYQ YrALYDYKKE REedidlhlg DILTVNKGSL VA-LGFS-- Anchored multiple alignment

82. Algorithmic questions Goal: Find optimal alignment (=consistent set of fragments) under costraints given by user- specified anchor points!

83. Additional input file with anchor points: 1 3 215 231 5 4.5 2 3 34 78 23 1.23 1 4 317 402 8 8.5 Algorithmic questions

84. NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS

85. Additional input file with anchor points: 1 3 215 231 5 4.5 2 3 34 78 23 1.23 1 4 317 402 8 8.5 Algorithmic questions

86. Additional input file with anchor points: 1 3 215 231 5 4.5 2 3 34 78 23 1.23 1 4 317 402 8 8.5 Sequences Algorithmic questions

87. Additional input file with anchor points: 1 3 215 231 5 4.5 2 3 34 78 23 1.23 1 4 317 402 8 8.5 Sequences start positions Algorithmic questions

88. Additional input file with anchor points: 1 3 215 231 5 4.5 2 3 34 78 23 1.23 1 4 317 402 8 8.5 Sequences start positions length Algorithmic questions

89. Additional input file with anchor points: 1 3 215 231 5 4.5 2 3 34 78 23 1.23 1 4 317 402 8 8.5 Sequences start positions length score Algorithmic questions