1. Burkhard Morgenstern Institut für Mikrobiologie und Genetik Molekulare Evolution und Rekonstruktion von phylogenetischen Bäumen WS 2006/2007

2. Goal: Phylogeny reconstruction based on molecular sequence data (DNA, RNA, protein sequences)

3. Multiple sequence alignment Molecular phylogeny reconstruction relies on comparative nucleic acid and protein sequence analysis Alignment most important tool for sequence comparison Multiple alignment contains more information than pair-wise alignment

4. Tools for multiple sequence alignment Y I M Q E V Q Q E R Sequence duplicates in history (e.g. speciation event)

5. Tools for multiple sequence alignment Y I M Q E V Q Q E R

6. Tools for multiple sequence alignment Y I M Q E V Q Q E R

7. Tools for multiple sequence alignment Y I M Q E A Q Q E R Y L M Q E V Q Q E R Substitutions occur

8. Tools for multiple sequence alignment Y I M Q E A Q Q E R Y L M Q E V Q Q E R

9. Tools for multiple sequence alignment YAI M Q E A Q Q E R Y L M - - V Q Q E R V Insertions/deletions (indels) occur

10. Tools for multiple sequence alignment YAI M Q E A Q Q E R Y L M - - V Q Q E R V

11. Tools for multiple sequence alignment Y A I M Q E A Q Q E R Y L M V Q Q E R V because of insertions/deletions: sequence similarity no longer immediately visible!

12. Tools for multiple sequence alignment Y A I M Q E A Q Q E R - Y - L M V - - Q Q E R V Alignment brings together related parts of the sequences by inserting gaps into sequences

13. Tools for multiple sequence alignment Y A I M Q E A Q Q E R - Y - L M V - - Q Q E R V

14. Tools for multiple sequence alignment Y A I M Q E A Q Q E R - Y - L M V - - Q Q E R V Mismatches correspond to substitutions Gaps correspond to indels

15. Tools for multiple sequence alignment Pairwise alignment: alignment of two sequences Multiple alignment: alignment of N > 2 sequences

16. Tools for multiple sequence alignment s1 R Y I M R E A Q Y E S A Q s2 R C I V M R E A Y E s3 Y I M Q E V Q Q E R s4 W R Y I A M R E Q Y E Assumtion: sequence family related by common ancestry; similarity due to common history Sequence similarity not obvious (insertions and deletions may have happened)

17. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Multiple alignment = arrangement of sequences by introducing gaps Alignment reveals sequence similarities

18. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - -

19. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - -

20. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - General information in multiple alignment: Functionally important regions more conserved than non-functional regions Local sequence conservation indicates functionality!

21. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Phylogeny reconstruction based on multiple alignment: Estimate pairwise distances between sequences (distance-based methods for tree reconstruction) Estimate evloutionary events in evolution (parsimony and maximum likelihood methods)

22. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Task in bioinformatics: Find best multiple alignment for given sequence set

23. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Astronomical number of possible alignments!

24. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - - - Y E - s3 Y I - - - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Astronomical number of possible alignments!

25. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - - - Y E - s3 Y I - - - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Computer has to decide: which one is best??

26. 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 !

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

28. Tools for multiple sequence alignment Criteria for alignment quality: 1. 3D-Structure: align residues at corresponding positions in 3D structure of protein!

29. Tools for multiple sequence alignment Criteria for alignment quality:

30. Tools for multiple sequence alignment Criteria for alignment quality: 1. 3D-Structure: align residues at corresponding positions in 3D structure of protein!

31. Tools for multiple sequence alignment Species related by common history

32. Tools for multiple sequence alignment Genes / proteins related by common history

33. 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!

34. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Alignment hypothesis about sequence evolution Mismatches correspond to substitutions Gaps correspond to insertions/deletions

35. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - - Y I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Alignment hypothesis about sequence evolution Search for most plausible scenario! Estimate probabilities for individual evolutionary events: insertions/deletions, substitutions

36. Tools for multiple sequence alignment s1 - R Y I - M R E A Q Y E S A Q s2 - R C I V M R E A - Y E - - - s3 - Y - I - M Q E V Q Q E R - - s4 W R Y I A M R E - Q Y E - - - Alignment hypothesis about sequence evolution Search for most plausible scenario! Estimate probabilities for individual evolutionary events: insertions/deletions, substitutions

37. Tools for multiple sequence alignment Compute score s(a,b) for degree of similarity between amino acids a and b based on probability p a,b of substitution a → b (or b → a) (Extremely simplified!)

38. Tools for multiple sequence alignment

39. Reason for different substitutin probabilities p a,b : Different physical and chemical properties of amino acids Amino acids with similar properties more likely to be substituted against each other

41. Tools for multiple sequence alignment Use penalty for gaps introduced into alignment Simplest approach: linear gap costs: penalty proportional to gap length Non-linear gap penalties more realistic: long gap caused by single insertion/deletion Most frequently used: affine linear gap penalties: more realistic, but efficient to calculate!

42. Traditional Objective functions: Define Score of alignments as Sum of individual similarity scores s(a,b) Minus gap penalties Needleman-Wunsch scoring system for pairwise alignment (1970)

43. Pair-wise sequence alignment T Y W I V T - - L V Example: Score = s(T,T) + s(I,L) + s (V,V) – 2 g Assumption: linear gap penalty!

44. Pair-wise sequence alignment T Y W I V T - - L V Dynamic-programming algorithm finds alignment with best score. (Needleman and Wunsch, 1970)

45. Pair-wise sequence alignment T Y W I V T - - L V Running time proportional to product of sequence length Time-complexity O(l 1 * l 2 )

46. Pair-wise sequence alignment Algorithm for pairwise alignment can be generalized to multiple alignment of N sequences Time-complexity O(l 1 * l 2 * … * l N ) Not feasable in reality (too long running time!) Heuristic necessary, i.e. fast algorithm that does not necessarily produce mathematically best alignment

47. `Progressive´ Alignment Most popular approach to (global) multiple sequence alignment: Progressive Alignment Since mid-Eighties: Feng/Doolittle, Higgins/Sharp, Taylor, …

48. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP

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

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

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

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

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

54. `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------- WW--RLNDKEGYVPRNLLGLYP-------- AVVIQDNSDIKVVP--KAKIIRD------- YAVESEA---SVQ--PVAALERIN------ WLN-YNE---ERGDFPGTYVEYIGRKKISP Most important implementation: CLUSTAL W

55. `Progressive´ Alignment CLUSTAL W; Thompson et al., 1994 (~17.000 citations) Pairwise distances as 1 - percentage of identity Calculate un-rooted tree with Neighbor Joining Define root as central position in tree Define sequence weights based on tree Gap penalties calculated based on various parameters

56. 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.

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

58. 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“)

59. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

60. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

61. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

62. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

63. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

64. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

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

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

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

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

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

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

71. More methods for multiple alignment: T-Coffee PIMA Muscle Prrp Mafft ProbCons

72. Substitution matrices Similarity score s(a,b) for amino acids a and b based on probability p a,b of substitution a -> b Idea: it is more reasonable to align amino acids that are often replaced by each other!

73. Substitution matrices Assumptions: p a,b does not depend on sequence position Sequence positions independent of each other p a,b = p b,a (symmetry!)

74. Substitution matrices Compute score s(a,b) for degree of similarity between 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 )

75. Substitution matrices

76. To calculate p a,b : Consider alignments of related proteins and count substitutions a → b (or b → a)

77. Substitution matrices To calculate p a,b : Consider alignments of related proteins and count substitutions a → b (or b → a) ESWTS-RQWERYTIALMSDQRREVLYWIALY ERWTSERQWERYTLALMS-QRREALYWIALY

78. Substitution matrices To calculate p a,b : Consider alignments of related proteins and count substitutions a → b (or b → a) ESWTS-RQWERYTIALMSDQRREVLYWIALY ERWTSERQWERYTLALMS-QRREALYWIALY

79. Substitution matrices Problems involved: 1. Probability p a,b depends on time t since sequences separated in evolution: p a,b = p a,b (t) 2. Protein families contain multiple sequences: phylogenetic tree must be known! 3. Alignment of protein families must be known! 4. Multiple mutations at one sequence position

80. Substitution matrices M. Dayhoff et al., Atlas of Protein sequence and Structure, 1978 PAM matrices

81. Substitution matrices Calculation of p a,b (t) : Consider multiple alignments of closely related protein families Count occurrence of a and b at corresponding positions in alignments using phylogenetic tree Estimate p a,b (t) for small times t Calculate conditional probabilities p(a|b,t) for small t Normalize to distance 1 PAM (= percentage of accepted mutations) Calculate p(a|b,t) for larger evolutionary distances by matrix multiplication Calculate p a,b (t) for larger evolutionary distances

82. Substitution matrices

83. Alternative: BLOSUM matrices S. Henikoff and J.G. Henikoff, PNAS, 1992 Basis: BLOCKS database, gap-free regions of multiple alignments. Cluster of sequences if percentage of similarity > L Estimate p a,b (t) directly. Default values: L = 62, L = 50