1. Multiple sequence alignment Monday, December 8, 2008 Introduction to Bioinformatics ME:800.707 J. Pevsner pevsner@kennedykrieger.org

2. Many of the images in this powerpoint presentation are from Bioinformatics and Functional Genomics by J Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by Wiley. These images and materials may not be used without permission from the publisher. Visit http://www.bioinfbook.org Additional credit: some of these slides were made by Sarah Wheelan (Johns Hopkins Bloomberg School of Public Health) from the Intro to Bioinformatics course. Copyright notice

3. Multiple sequence alignment: today’s goals to define what a multiple sequence alignment is and how it is generated; to describe profile HMMs to introduce databases of multiple sequence alignments to introduce ways you can make your own multiple sequence alignments to show how a multiple sequence alignment provides the basis for phylogenetic trees Page 319

4. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

5. Multiple sequence alignment: definition a collection of three or more protein (or nucleic acid) sequences that are partially or completely aligned homologous residues are aligned in columns across the length of the sequences residues are homologous in an evolutionary sense residues are homologous in a structural sense Page 320

6. ClustalW Note how the region of a conserved histidine (▼) varies depending on which algorithm is used

7. Praline

8. MUSCLE

9. Probcons

10. TCoffee

11. Multiple sequence alignment: properties not necessarily one “correct” alignment of a protein family protein sequences evolve......the corresponding three-dimensional structures of proteins also evolve may be impossible to identify amino acid residues that align properly (structurally) throughout a multiple sequence alignment for two proteins sharing 30% amino acid identity, about 50% of the individual amino acids are superposable in the two structures Page 320

12. Multiple sequence alignment: features some aligned residues, such as cysteines that form disulfide bridges, may be highly conserved there may be conserved motifs such as a transmembrane domain there may be conserved secondary structure features there may be regions with consistent patterns of insertions or deletions (indels) Page 320

13. Multiple sequence alignment: uses MSA is more sensitive than pairwise alignment to detect homologs BLAST output can take the form of a MSA, and can reveal conserved residues or motifs Population data can be analyzed in a MSA (PopSet) A single query can be searched against a database of MSAs (e.g. PFAM) Regulatory regions of genes may have consensus sequences identifiable by MSA Page 321

14. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

15. Multiple sequence alignment: methods Exact methods: dynamic programming Instead of the 2-D dynamic programming matrix in the Needleman-Wunsch technique, think about a 3-D, 4-D or higher order matrix. Exact methods give optimal alignments but are not feasible in time or space for more than ~10 sequences. Still an extremely active field.

16. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

17. Multiple sequence alignment: methods Progressive methods: use a guide tree (a little like a phylogenetic tree but NOT a phylogenetic tree) to determine how to combine pairwise alignments one by one to create a multiple alignment. Making multiple alignments using trees was a very popular subject in the ‘80s. Fitch and Yasunobu (1974) may have first proposed the idea, but Hogeweg and Hesper (1984) and many others worked on the topic before Feng and Doolittle (1987)—they made one important contribution that got their names attached to this alignment method. Examples: CLUSTALW, MUSCLE

18. Multiple sequence alignment: methods Example of MSA using ClustalW: two data sets Five distantly related lipocalins (human to E. coli) Five closely related RBPs When you do this, obtain the sequences of interest in the FASTA format! (You can save them in a Word document) Page 321

19. The input for ClustalW: a group of sequences (DNA or protein) in the FASTA format

20. Get sequences from Entrez Protein (or HomoloGene)

21. You can display sequences from Entrez Protein in the fasta format

22. When you get a DNA sequence from Entrez Nucleotide, you can click CDS to select only the coding sequence. This is very useful for phylogeny studies.

23. HomoloGene: an NCBI resource to obtain multiple related sequences [1] Enter a query at NCBI such as globin [2] Click on HomoloGene (left side) [3] Choose a HomoloGene family, and view in the fasta format

24. Use ClustalW to do a progressive MSA http://www2.ebi. ac.uk/clustalw/ Fig. 10.1 Page 321

25. Feng-Doolittle MSA occurs in 3 stages [1] Do a set of global pairwise alignments (Needleman and Wunsch’s dynamic programming algorithm) [2] Create a guide tree [3] Progressively align the sequences Page 321

26. Progressive MSA stage 1 of 3: generate global pairwise alignments Fig. 10.2 Page 323 five distantly related lipocalins best score

27. Progressive MSA stage 1 of 3: generate global pairwise alignments Start of Pairwise alignments Aligning... Sequences (1:2) Aligned. Score: 84 Sequences (1:3) Aligned. Score: 84 Sequences (1:4) Aligned. Score: 91 Sequences (1:5) Aligned. Score: 92 Sequences (2:3) Aligned. Score: 99 Sequences (2:4) Aligned. Score: 86 Sequences (2:5) Aligned. Score: 85 Sequences (3:4) Aligned. Score: 85 Sequences (3:5) Aligned. Score: 84 Sequences (4:5) Aligned. Score: 96 Fig. 10.4 Page 325 five closely related lipocalins best score

28. Number of pairwise alignments needed For n sequences, (n-1)(n) / 2 For 5 sequences, (4)(5) / 2 = 10 Page 322

29. Feng-Doolittle stage 2: guide tree Convert similarity scores to distance scores A tree shows the distance between objects Use UPGMA (defined in the phylogeny lecture) ClustalW provides a syntax to describe the tree A guide tree is not a phylogenetic tree Page 323

30. Progressive MSA stage 2 of 3: generate a guide tree calculated from the distance matrix Fig. 10.2 Page 323

31. Progressive MSA stage 2 of 3: generate guide tree ( gi|5803139|ref|NP_006735.1|:0.04284, ( gi|6174963|sp|Q00724|RETB_MOUS:0.00075, gi|132407|sp|P04916|RETB_RAT:0.00423) :0.10542) :0.01900, gi|89271|pir||A39486:0.01924, gi|132403|sp|P18902|RETB_BOVIN:0.01902); Fig. 10.4 Page 325 five closely related lipocalins

32. Feng-Doolittle stage 3: progressive alignment Make a MSA based on the order in the guide tree Start with the two most closely related sequences Then add the next closest sequence Continue until all sequences are added to the MSA Rule: “once a gap, always a gap.” Page 324

33. Progressive MSA stage 3 of 3: progressively align the sequences following the branch order of the tree Fig. 10.3 Page 324

34. Progressive MSA stage 3 of 3: CLUSTALX output Note that you can download CLUSTALX locally, rather than using a web-based program!

35. Clustal W alignment of 5 closely related lipocalins CLUSTAL W (1.82) multiple sequence alignment gi|89271|pir||A39486 MEWVWALVLLAALGSAQAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 50 gi|132403|sp|P18902|RETB_BOVIN ------------------ERDCRVSSFRVKENFDKARFAGTWYAMAKKDP 32 gi|5803139|ref|NP_006735.1| MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 48 gi|6174963|sp|Q00724|RETB_MOUS MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50 gi|132407|sp|P04916|RETB_RAT MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50 ********************:* ***:***** gi|89271|pir||A39486 EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 100 gi|132403|sp|P18902|RETB_BOVIN EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 82 gi|5803139|ref|NP_006735.1| EGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTED 98 gi|6174963|sp|Q00724|RETB_MOUS EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100 gi|132407|sp|P04916|RETB_RAT EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100 *********:*******.*:************.**:************** gi|89271|pir||A39486 PAKFKMKYWGVASFLQKGNDDHWIIDTDYDTYAAQYSCRLQNLDGTCADS 150 gi|132403|sp|P18902|RETB_BOVIN PAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSCRLLNLDGTCADS 132 gi|5803139|ref|NP_006735.1| PAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADS 148 gi|6174963|sp|Q00724|RETB_MOUS PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150 gi|132407|sp|P04916|RETB_RAT PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150 ****************:*******:****:*:* ****** ********* Fig. 10.5 Page 326 * asterisks indicate identity in a column

36. Progressive MSA stage 3 of 3: progressively align the sequences following the branch order of the tree: Order matters THE LAST FAT CAT THE FAST CAT THE VERY FAST CAT THE FAT CAT THE LAST FAT CAT THE FAST CAT --- THE LAST FA-T CAT THE FAST CA-T --- THE VERY FAST CAT THE LAST FA-T CAT THE FAST CA-T --- THE VERY FAST CAT THE ---- FA-T CAT Adapted from C. Notredame, Pharmacogenomics 2002

37. Progressive MSA stage 3 of 3: progressively align the sequences following the branch order of the tree: Order matters THE FAT CAT THE FAST CAT THE VERY FAST CAT THE LAST FAT CAT THE FA-T CAT THE FAST CAT THE ---- FA-T CAT THE ---- FAST CAT THE VERY FAST CAT THE ---- FA-T CAT THE ---- FAST CAT THE VERY FAST CAT THE LAST FA-T CAT Adapted from C. Notredame, Pharmacogenomics 2002

38. Why “once a gap, always a gap”? There are many possible ways to make a MSA Where gaps are added is a critical question Gaps are often added to the first two (closest) sequences To change the initial gap choices later on would be to give more weight to distantly related sequences To maintain the initial gap choices is to trust that those gaps are most believable Page 324

39. Additional features of ClustalW improve its ability to generate accurate MSAs Individual weights are assigned to sequences; very closely related sequences are given less weight, while distantly related sequences are given more weight Scoring matrices are varied dependent on the presence of conserved or divergent sequences, e.g.: PAM2080-100% id PAM6060-80% id PAM12040-60% id PAM3500-40% id Residue-specific gap penalties are applied

44. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

45. Multiple sequence alignment: methods Iterative methods: compute a sub-optimal solution and keep modifying that intelligently using dynamic programming or other methods until the solution converges. Examples: IterAlign, Praline, MAFFT

46. MUSCLE: next-generation progressive MSA [1] Build a draft progressive alignment Determine pairwise similarity through k-mer counting (not by alignment) Compute distance (triangular distance) matrix Construct tree using UPGMA Construct draft progressive alignment following tree

47. MUSCLE: next-generation progressive MSA [2] Improve the progressive alignment Compute pairwise identity through current MSA Construct new tree with Kimura distance measures Compare new and old trees: if improved, repeat this step, if not improved, then we’re done

48. MUSCLE: next-generation progressive MSA [3] Refinement of the MSA Split tree in half by deleting one edge Make profiles of each half of the tree Re-align the profiles Accept/reject the new alignment

50. http://www.ebi.ac.uk/muscle/

51. MUSCLE output (formatted with SeaView) SeaView is a graphical multiple sequence alignment editor available at http://pbil.univ-lyon1.fr/software/seaview.html

52. Praline output for the same alignment: pure iterative approach Boxes highlight a region that is difficult to align

53. Iterative approaches: MAFFT Uses Fast Fourier Transform to speed up profile alignment Uses fast two-stage method for building alignments using k-mer frequencies Offers many different scoring and aligning techniques One of the more accurate programs available Available as standalone or web interface Many output formats, including interactive phylogenetic trees

54. Iterative approaches: MAFFT Has about 1000 advanced settings!

55. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

56. Multiple sequence alignment: methods Consistency-based algorithms: generally use a database of both local high-scoring alignments and long-range global alignments to create a final alignment These are very powerful, very fast, and very accurate methods Examples: T-COFFEE, Prrp, DiAlign, ProbCons

57. ProbCons—consistency-based approach Combines iterative and progressive approaches with a unique probabilistic model. Uses Hidden Markov Models (more in a minute) to calculate probability matrices for matching residues, uses this to construct a guide tree Progressive alignment hierarchically along guide tree Post-processing and iterative refinement (a little like MUSCLE)

58. 2e Fig. 5.12

59. ProbCons—consistency-based approach Sequence xx i Sequence yy j Sequence zz k If x i aligns with z k and z k aligns with y j then x i should align with y j ProbCons incorporates evidence from multiple sequences to guide the creation of a pairwise alignment.

60. ProbCons—consistency-based approach http://probcons.stanford.edu/

61. ProbCons output for the same alignment: consistency iteration helps

62. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

63. http://tcoffee.org Make an MSA MSA w. structural data Compare MSA methods Make an RNA MSA Combine MSA methods Consistency-based Structure-based Back translate protein MSA

64. APDB ClustalW output

65. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

66. Multiple sequence alignment: methods How do we know which program to use? There are benchmarking multiple alignment datasets that have been aligned painstakingly by hand, by structural similarity, or by extremely time- and memory-intensive automated exact algorithms. Some programs have interfaces that are more user- friendly than others. And most programs are excellent so it depends on your preference. If your proteins have 3D structures, use these to help you judge your alignments. For example, try Expresso at http://www.tcoffee.org.

67. [1] Create or obtain a database of protein sequences for which the 3D structure is known. Thus we can define “true” homologs using structural criteria. [2] Try making multiple sequence alignments with many different sets of proteins (very related, very distant, few gaps, many gaps, insertions, outliers). [3] Compare the answers. Strategy for assessment of alternative multiple sequence alignment algorithms Page 346

68. BaliBase: comparison of multiple sequence alignment algorithms Fig. 10.30 Page 349

69. Multiple sequence alignment: methods Benchmarking tests suggest that ProbCons, a consistency-based/progressive algorithm, performs the best on the BAliBASE set, although MUSCLE, a progressive alignment package, is an extremely fast and accurate program. ClustalW is the most popular program. It has a nice interface (especially with ClustalX) and is easy to use.

70. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

71. Multiple sequence alignment to profile HMMs ► Hidden Markov models (HMMs) are “states” that describe the probability of having a particular amino acid residue at arranged in a column of a multiple sequence alignment ► HMMs are probabilistic models ► HMMs may give more sensitive alignments than traditional techniques such as progressive alignment Page 325

72. Simple Hidden Markov Model Observation: YNNNYYNNNYN (Y=goes out, N=doesn’t go out) What is underlying reality (the hidden state chain)? R S 0.15 0.85 0.2 0.8 P(dog goes out in rain) = 0.1 P(dog goes out in sun) = 0.85

73. GTWYA (hs RBP) GLWYA (mus RBP) GRWYE (apoD) GTWYE (E Coli) GEWFS (MUP4) An HMM is constructed from a MSA Example: five lipocalins Fig. 10.6 Page 327

74. GTWYA GLWYA GRWYE GTWYE GEWFS Position Prob.12345 p(G)1.0 p(T)0.4 p(L)0.2 p(R)0.2 p(E)0.20.4 p(W)1.0 p(Y)0.8 p(F)0.2 p(A)0.4 p(S)0.2 Fig. 10.6 Page 327

75. GTWYA GLWYA GRWYE GTWYE GEWFS Prob.12345 p(G)1.0 p(T)0.4 p(L)0.2 p(R)0.2 p(E)0.20.4 p(W)1.0 p(Y)0.8 p(F)0.2 p(A)0.4 p(S)0.2 P(GEWYE) = (1.0)(0.2)(1.0)(0.8)(0.4) = 0.064 log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.4) = -2.75 Fig. 10.6 Page 327

76. GTWYA GLWYA GRWYE GTWYE GEWFS P(GEWYE) = (1.0)(0.2)(1.0)(0.8)(0.5) = 0.08 log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.5) = -2.53 G:1.0 W:1.0 T:0.4 L:0.2 R:0.2 E:0.2 Y:0.8 F:0.2 A:0.5 E:0.5 S:1.0

77. Structure of a hidden Markov model (HMM) M IyIy IxIx p1p1 p7p7 p6p6 p5p5 p3p3 p2p2 p4p4

78. Fig. 10.7 Page 328

79. Structure of a hidden Markov model (HMM) main state insert state delete state Fig. 10.7 Page 328

80. HMMER: build a hidden Markov model Determining effective sequence number... done. [4] Weighting sequences heuristically... done. Constructing model architecture... done. Converting counts to probabilities... done. Setting model name, etc.... done. [x] Constructed a profile HMM (length 230) Average score: 411.45 bits Minimum score: 353.73 bits Maximum score: 460.63 bits Std. deviation: 52.58 bits Fig. 10.8 Page 329

81. HMMER: calibrate a hidden Markov model HMM file: lipocalins.hmm Length distribution mean: 325 Length distribution s.d.: 200 Number of samples: 5000 random seed: 1034351005 histogram(s) saved to: [not saved] POSIX threads: 2 - - - - - - - - - - - - - - - - HMM : x mu : -123.894508 lambda : 0.179608 max : -79.334000 Fig. 10.8 Page 329

82. HMMER: search an HMM against GenBank Scores for complete sequences (score includes all domains): Sequence Description Score E-value N -------- ----------- ----- ------- --- gi|20888903|ref|XP_129259.1| [Mus] (XM_129259) ret 461.1 1.9e-133 1 gi|132407|sp|P04916|RETB_RAT Plasma retinol- 458.0 1.7e-132 1 gi|20548126|ref|XP_005907.5|[Homo] (XM_005907) sim 454.9 1.4e-131 1 gi|5803139|ref|NP_006735.1| (NM_006744) ret 454.6 1.7e-131 1 gi|20141667|sp|P02753|RETB_HUMAN Plasma retinol- 451.1 1.9e-130 1. gi|16767588|ref|NP_463203.1|[Salmonella] (NC_003197) out 318.2 1.9e-90 1 gi|5803139|ref|NP_006735.1|: domain 1 of 1, from 1 to 195: score 454.6, E = 1.7e-131 *->mkwVMkLLLLaALagvfgaAErdAfsvgkCrvpsPPRGfrVkeNFDv mkwV++LLLLaA + +aAErd Crv+s frVkeNFD+ gi|5803139 1 MKWVWALLLLAA--W--AAAERD------CRVSS----FRVKENFDK 33 erylGtWYeIaKkDprFErGLllqdkItAeySleEhGsMsataeGrirVL +r++GtWY++aKkDp E GL+lqd+I+Ae+S++E+G+Msata+Gr+r+L gi|5803139 34 ARFSGTWYAMAKKDP--E-GLFLQDNIVAEFSVDETGQMSATAKGRVRLL 80 eNkelcADkvGTvtqiEGeasevfLtadPaklklKyaGvaSflqpGfddy +N+++cAD+vGT+t++E dPak+k+Ky+GvaSflq+G+dd+ gi|5803139 81 NNWDVCADMVGTFTDTE----------DPAKFKMKYWGVASFLQKGNDDH 120 Fig. 10.9 Page 330

83. HMMER: search an HMM against GenBank match to a bacterial lipocalin gi|16767588|ref|NP_463203.1|: domain 1 of 1, from 1 to 177: score 318.2, E = 1.9e-90 *->mkwVMkLLLLaALagvfgaAErdAfsvgkCrvpsPPRGfrVkeNFDv M+LL+ +A a ++ Af+v++C++p+PP+G++V++NFD+ gi|1676758 1 ----MRLLPVVA------AVTA-AFLVVACSSPTPPKGVTVVNNFDA 36 erylGtWYeIaKkDprFErGLllqdkItAeySleEhGsMsataeGrirVL +rylGtWYeIa+ D+rFErGL + +tA+ySl++ +G+i+V+ gi|1676758 37 KRYLGTWYEIARLDHRFERGL---EQVTATYSLRD--------DGGINVI 75 eNkelcADkvGTvtqiEGeasevfLtadPaklklKyaGvaSflqpGfddy Nk++++D+ +++ +EG+a ++t+ P +++lK+ Sf++p++++y gi|1676758 76 -NKGYNPDR-EMWQKTEGKA---YFTGSPNRAALKV----SFFGPFYGGY 116 Fig. 10.9 Page 330

84. HMMER: search an HMM against GenBank Scores for complete sequences (score includes all domains): Sequence Description Score E-value N -------- ----------- ----- ------- --- gi|3041715|sp|P27485|RETB_PIG Plasma retinol- 614.2 1.6e-179 1 gi|89271|pir||A39486 plasma retinol- 613.9 1.9e-179 1 gi|20888903|ref|XP_129259.1| (XM_129259) ret 608.8 6.8e-178 1 gi|132407|sp|P04916|RETB_RAT Plasma retinol- 608.0 1.1e-177 1 gi|20548126|ref|XP_005907.5| (XM_005907) sim 607.3 1.9e-177 1 gi|20141667|sp|P02753|RETB_HUMAN Plasma retinol- 605.3 7.2e-177 1 gi|5803139|ref|NP_006735.1| (NM_006744) ret 600.2 2.6e-175 1 gi|5803139|ref|NP_006735.1|: domain 1 of 1, from 1 to 199: score 600.2, E = 2.6e-175 *->meWvWaLvLLaalGgasaERDCRvssFRvKEnFDKARFsGtWYAiAK m+WvWaL+LLaa+ a+aERDCRvssFRvKEnFDKARFsGtWYA+AK gi|5803139 1 MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAK 45 KDPEGLFLqDnivAEFsvDEkGhmsAtAKGRvRLLnnWdvCADmvGtFtD KDPEGLFLqDnivAEFsvDE+G+msAtAKGRvRLLnnWdvCADmvGtFtD gi|5803139 46 KDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTD 95 tEDPAKFKmKYWGvAsFLqkGnDDHWiiDtDYdtfAvqYsCRLlnLDGtC tEDPAKFKmKYWGvAsFLqkGnDDHWi+DtDYdt+AvqYsCRLlnLDGtC gi|5803139 96 TEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTC 145 Fig. 10.9 Page 330

85. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

86. Two kinds of multiple sequence alignment resources Text-based or query-based searches: CDD, Pfam (profile HMMs), PROSITE [2] Multiple sequence alignment by manual input Muscle, ClustalW, ClustalX [1] Databases of multiple sequence alignments Page 329

87. BLOCKS CDD Pfam SMART DOMO (Gapped MSA) INTERPRO iProClass MetaFAM PRINTS PRODOM (PSI-BLAST) PROSITE Databases of multiple sequence alignments These Use HMMs

88. PFAM (protein family) database: http://pfam.sanger.ac.uk/ Fig. 10.11 Page 331

89. PFAM (protein family) text search result Fig. 10.12 Page 334

90. PFAM HMM for lipocalins 20 amino acids position

91. PFAM HMM for lipocalins: GXW motif GW 20 amino acids

92. PFAM GCG MSF format Fig. 10.13 Page 335

93. Pfam (protein family) database

94. PFAM JalView viewer Fig. 10.14 Page 336

95. PFAM JalView viewer Fig. 10.15 Page 336

96. PFAM JalView viewer: principal components analysis (PCA) Fig. 10.16 Page 337

97. Fig. 10.17 Page 337

98. SMART: Simple Modular Architecture Research Tool (emphasis on cell signaling) Page 338

99. SMART: lipocalin result Fig. 10.18 Page 338

100. BLOCKS CDD Pfam SMART DOMO (Gapped MSA) INTERPRO iProClass MetaFAM PRINTS PRODOM (PSI-BLAST) PROSITE Databases of multiple sequence alignments Conserved Domain Database (CDD) at NCBI = PFAM + SMART

101. [1] Go to NCBI  Structure [2] Click CDD [3] Enter a text query, or a protein sequence CDD: Conserved domain database

103. CDD = PFAM + SMART Fig. 10.20 Page 339

104. Purpose: to find conserved domains in the query sequence Query = your favorite protein Database = set of many position-specific scoring matrices (PSSMs), i.e. a set of MSAs CDD is related to PSI-BLAST, but distinct CDD searches against profiles generated from pre-selected alignments CDD uses RPS-BLAST: reverse position-specific Page 333

105. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

106. MEGA version 4: Molecular Evolutionary Genetics Analysis Download from www.megasoftware.net

107. MEGA version 4: Molecular Evolutionary Genetics Analysis

108. MEGA version 4: Molecular Evolutionary Genetics Analysis Two ways to create a multiple sequence alignment 1. Open the Alignment Explorer, paste in a FASTA MSA 2. Select a DNA query, do a BLAST search Once your sequences are in MEGA, you can run ClustalW then make trees and do phylogenetic analyses 1 2

109. [1] Open the Alignment Explorer [2] Select “Create a new alignment” [3] Click yes (for DNA) or no (for protein)

110. [4] Find, select, and copy a multiple sequence alignment (e.g. from Pfam; choose FASTA with dashes for gaps) [5] Paste it into MEGA [6] If needed, run ClustalW to align the sequences [7] Save (Ctrl+S) as.mas then exit and save as.meg

111. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

112. Multiple sequence alignment of genomic DNA There are typically few sequences (up to several dozen, each having up to millions of base pairs. Adding more species improves accuracy. Alignment of divergent sequences often reveals islands of conservation (providing “anchors” for alignment). Chromosomes are subject to inversions, duplications, deletions, and translocations (often involving millions of base pairs). E.g. human chromosome 2 is derived from the fusion of two acrocentric chromosomes. There are no benchmark datasets available.

118. Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

119. Introduction to evolution and phylogeny Nomenclature of trees Four stages of molecular phylogeny: [1] selecting sequences [2] multiple sequence alignment [3] tree-building [4] tree evaluation Practical approaches to making trees Goal of the phylogeny lecture

120. Charles Darwin’s 1859 book (On the Origin of Species By Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life) introduced the theory of evolution. To Darwin, the struggle for existence induces a natural selection. Offspring are dissimilar from their parents (that is, variability exists), and individuals that are more fit for a given environment are selected for. In this way, over long periods of time, species evolve. Groups of organisms change over time so that descendants differ structurally and functionally from their ancestors. Introduction Page 357

121. Darwin did not understand the mechanisms by which hereditary changes occur. In the 1920s and 1930s, a synthesis occurred between Darwinism and Mendel’s principles of inheritance. The basic processes of evolution are [1] mutation, and also [2] genetic recombination as two sources of variability; [3] chromosomal organization (and its variation); [4] natural selection [5] reproductive isolation, which constrains the effects of selection on populations Introduction Page 357 (See Stebbins, 1966)

122. At the molecular level, evolution is a process of mutation with selection. Molecular evolution is the study of changes in genes and proteins throughout different branches of the tree of life. Phylogeny is the inference of evolutionary relationships. Traditionally, phylogeny relied on the comparison of morphological features between organisms. Today, molecular sequence data are also used for phylogenetic analyses. Introduction Page 358

123. Studies of molecular evolution began with the first sequencing of proteins, beginning in the 1950s. In 1953 Frederick Sanger and colleagues determined the primary amino acid sequence of insulin. (The accession number of human insulin is NP_000198) Historical background Page 358

124. Fig. 11.1 Page 359 Mature insulin consists of an A chain and B chain heterodimer connected by disulphide bridges The signal peptide and C peptide are cleaved, and their sequences display fewer functional constraints.

125. Fig. 11.1 Page 359

126. Fig. 11.1 Page 359 Note the sequence divergence in the disulfide loop region of the A chain

127. By the 1950s, it became clear that amino acid substitutions occur nonrandomly. For example, Sanger and colleagues noted that most amino acid changes in the insulin A chain are restricted to a disulfide loop region. Such differences are called “neutral” changes (Kimura, 1968; Jukes and Cantor, 1969). Subsequent studies at the DNA level showed that rate of nucleotide (and of amino acid) substitution is about six- to ten-fold higher in the C peptide, relative to the A and B chains. Historical background: insulin Page 358

128. Fig. 11.1 Page 359 Number of nucleotide substitutions/site/year 0.1 x 10 -9 1 x 10 -9

129. Surprisingly, insulin from the guinea pig (and from the related coypu) evolve seven times faster than insulin from other species. Why? The answer is that guinea pig and coypu insulin do not bind two zinc ions, while insulin molecules from most other species do. There was a relaxation on the structural constraints of these molecules, and so the genes diverged rapidly. Historical background: insulin Page 360

130. Fig. 11.1 Page 359 Guinea pig and coypu insulin have undergone an extremely rapid rate of evolutionary change Arrows indicate positions at which guinea pig insulin (A chain and B chain) differs from both human and mouse

131. Fig. 11.2 Page 360 Historical background Oxytocin CYIQNCPLG Vasopressin CYFQNCPRG In the 1950s, other labs sequenced oxytocin and vasopressin. These peptides differ at only two amino acid residues, but they have distinctly different functions. It became clear that there are significant structural and functional consequences to changes in primary amino acid sequence.

132. In the 1960s, sequence data were accumulated for small, abundant proteins such as globins, cytochromes c, and fibrinopeptides. Some proteins appeared to evolve slowly, while others evolved rapidly. Linus Pauling, Emanuel Margoliash and others proposed the hypothesis of a molecular clock: For every given protein, the rate of molecular evolution is approximately constant in all evolutionary lineages Molecular clock hypothesis Page 360

133. As an example, Richard Dickerson (1971) plotted data from three protein families: cytochrome c, hemoglobin, and fibrinopeptides. The x-axis shows the divergence times of the species, estimated from paleontological data. The y-axis shows m, the corrected number of amino acid changes per 100 residues. n is the observed number of amino acid changes per 100 residues, and it is corrected to m to account for changes that occur but are not observed. Molecular clock hypothesis Page 360 N 100 = 1 – e -(m/100)

134. Fig. 11.3 Page 361 Millions of years since divergence corrected amino acid changes per 100 residues (m) Dickerson (1971)

135. Dickerson drew the following conclusions: For each protein, the data lie on a straight line. Thus, the rate of amino acid substitution has remained constant for each protein. The average rate of change differs for each protein. The time for a 1% change to occur between two lines of evolution is 20 MY (cytochrome c), 5.8 MY (hemoglobin), and 1.1 MY (fibrinopeptides). The observed variations in rate of change reflect functional constraints imposed by natural selection. Molecular clock hypothesis: conclusions Page 361