1. Multiple sequence alignment

2. 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

3. 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
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4. ClustalW
Note how the region of a conserved histidine (▼) varies depending on which algorithm is used

5. Praline

6. MUSCLE

7. Probcons

8. TCoffee

9. 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
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10. 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)
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11. 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
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12. 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

13. 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.

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
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

16. 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)
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17. Multidimensional Dynamic Programming
Example: in 3D (three sequences):
7 neighbors/cells
F(i,j,k) = max{ F(i-1,j-1,k-1)+S(xi, xj, xk),
F(i-1,j-1,k )+S(xi, xj, - ),
F(i-1,j ,k-1)+S(xi, -, xk),
F(i-1,j ,k )+S(xi, -, - ),
F(i ,j-1,k-1)+S( -, xj, xk),
F(i ,j-1,k )+S( -, xj, xk),
F(i ,j ,k-1)+S( -, -, xk) }

18. HOW CAN I ALIGN MANY SEQUENCES
2 Globins =>1 Min

19. HOW CAN I ALIGN MANY SEQUENCES
3 Globins =>2 hours

20. HOW CAN I ALIGN MANY SEQUENCES
4 Globins => 10 days

21. HOW CAN I ALIGN MANY SEQUENCES
5 Globins => 3 years

22. HOW CAN I ALIGN MANY SEQUENCES
6 Globins =>300 years

23. HOW CAN I ALIGN MANY SEQUENCES
7 Globins => years
Solidified Fossil, Old stuff

24. HOW CAN I ALIGN MANY SEQUENCES 8 Globins =>3 Million years

25. The Choice of an objective function
Biological problem that lies in the definition of correctness
Sum of pair, Entropy score, Consistency based, …
The Optimization of that function
Exact Algorithms (Dynamic Programming)
Progressive alignment (ClustalW)
Iterative approaches (SA, GA, …)

26. Alignment Costs Traditional A C A C C A Input seq Reconstructed seq
Missmatches
Traditional (SP)
Tree-Alignment
Star-Alignment
A, A, A, C, C
A, A, A, C, C
A, A, A, C, C -- 6
A, A, C 1 A 2

29. The Progressive Multiple Alignment Algorithm
(Clustal W)

30. Making An Alignment
Any Exact Method would be TOO SLOW
We will use a Heuristic Algorithm.
Progressive Alignment Algorithm is the most Popular
-ClustalW
-Greedy Heuristic (No Guarranty).
-Fast

31. Progressive Alignment Feng and Dolittle, 1988; Taylor 1989
Clustering

32. Progressive Alignment
Dynamic Programming Using A Substitution Matrix

33. Progressive Alignment
-Depends on the CHOICE of the sequences.
-Depends on the ORDER of the sequences (Tree).
-Depends on the PARAMETERS:
Substitution Matrix.
Penalties (Gop, Gep).
Sequence Weight.
Tree making Algorithm.

34. Example : Progressive alignment
Pairwise Alignment
Guide Tree
MSA by adding
sequences
1 + 2
3 + 4
1 + 3
1 + 4
2 + 4
2 + 3 1 2 3 4 2 3 4 1 1 2 3

35. Progressive alignment (cont.)
Sequence
Guide Tree 1 1 2 3 4 5
Distance Matrix:
displays distances
of all sequence pairs. 2 4 5 3
D = 1 - S
UPGMA (unweighted pair group method of arithmetic averages)
or Neighbour-Joining method

36. UPGMA Clustering (Guide Tree) ij u u ij
u 3 v u v ij 6
u w
u 0 6
w 0 ij 2 . 5 . 5 . 5 . . 8 5 4 . . 5 5 3 1 2 3 5 4 1 2 3 5 4 1 2 3 5 4 1 2 3 5 4

37. Progressive alignment (cont.)
Guide Tree
Alignment of alignments 1 2 4 5 2 3 1
Columns - once aligned - are never changed. . . and new gaps are inserted.
Depend strongly on pairwise alignments and the intitial starting sequences
No guarantee that the global optimal solution will be found.
In case of sequences identity less than 25-30%, this approach become much less reliable.

39. Progressive Alignment
When Doesn’t It Work
SeqA GARFIELD THE LAST FA-T CAT
SeqB GARFIELD THE FAST CA-T ---
SeqC GARFIELD THE VERY FAST CAT
SeqD THE ---- FA-T CAT
CLUSTALW (Score=20, Gop=-1, Gep=0, M=1)
SeqA GARFIELD THE LAST FA-T CAT
SeqB GARFIELD THE FAST ---- CAT
SeqC GARFIELD THE VERY FAST CAT
SeqD THE ---- FA-T CAT
CORRECT (Score=24)

40. GARFIELD THE LAST FAT CAT GARFIELD THE VERY FAST CAT
GARFIELD THE FAST CAT
GARFIELD THE VERY FAST CAT
THE FAT CAT
GARFIELD THE LAST FAT CAT
GARFIELD THE FAST CAT ---
GARFIELD THE LAST FA-T CAT
GARFIELD THE FAST CA-T ---
GARFIELD THE VERY FAST CAT
THE ---- FA-T CAT
GARFIELD THE VERY FAST CAT
THE ---- FA-T CAT

41. Iterative alignment Iterate until the MSA doesn’t change (convergence)B C DE
Pairwise distance
table
Iterate until the MSA doesn’t change (convergence) E D C B A 11 1 3 10 2
Guide tree
MSA A B C D E

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

43. Get sequences from Entrez Protein (or HomoloGene)

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

45. 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.

46. 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

47. http://www2.ebi. ac.uk/clustalw/ Use ClustalW to do a progressive MSA
Fig. 10.1
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48. 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
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49. Progressive MSA stage 1 of 3: generate global pairwise alignments
five distantly
related lipocalins
best score
Fig. 10.2
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50. Progressive MSA stage 1 of 3: generate global pairwise alignments
five closely
related lipocalins
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
best
score
Fig. 10.4
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51. Number of pairwise alignments needed
For n sequences, (n-1)(n) / 2
For 5 sequences, (4)(5) / 2 = 10
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52. 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
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53. Progressive MSA stage 2 of 3: generate a guide tree calculated from
the distance matrix
Fig. 10.2
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54. Progressive MSA stage 2 of 3:
generate guide tree (
gi| |ref|NP_ |: ,
gi| |sp|Q00724|RETB_MOUS: ,
gi|132407|sp|P04916|RETB_RAT: )
: )
: ,
gi|89271|pir||A39486: ,
gi|132403|sp|P18902|RETB_BOVIN: );
five closely
related lipocalins
Fig. 10.4
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55. 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.”
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56. Progressive MSA stage 3 of 3: progressively align the sequences
following the branch order of the tree
Fig. 10.3
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57. Progressive MSA stage 3 of 3:
CLUSTALX output
Note that you can download CLUSTALX locally, rather than using a web-based program!

58. Clustal W alignment of 5 closely related lipocalins
CLUSTAL W (1.82) multiple sequence alignment
gi|89271|pir||A MEWVWALVLLAALGSAQAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 50
gi|132403|sp|P18902|RETB_BOVIN ERDCRVSSFRVKENFDKARFAGTWYAMAKKDP 32
gi| |ref|NP_ | MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 48
gi| |sp|Q00724|RETB_MOUS MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50
gi|132407|sp|P04916|RETB_RAT MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50
********************:* ***:*****
gi|89271|pir||A EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 100
gi|132403|sp|P18902|RETB_BOVIN EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 82
gi| |ref|NP_ | EGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTED 98
gi| |sp|Q00724|RETB_MOUS EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100
gi|132407|sp|P04916|RETB_RAT EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100
*********:*******.*:************.**:**************
gi|89271|pir||A PAKFKMKYWGVASFLQKGNDDHWIIDTDYDTYAAQYSCRLQNLDGTCADS 150
gi|132403|sp|P18902|RETB_BOVIN PAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSCRLLNLDGTCADS 132
gi| |ref|NP_ | PAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADS 148
gi| |sp|Q00724|RETB_MOUS PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150
gi|132407|sp|P04916|RETB_RAT PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150
****************:*******:****:*:* ****** *********
* asterisks indicate identity in a column
Fig. 10.5
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59. 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

60. 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

61. 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
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62. 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.:
PAM % id
PAM % id
PAM % id
PAM % id
Residue-specific gap penalties are applied

63. Molecular Evolutionary Genetics Analysis
MEGA version 4:
Molecular Evolutionary Genetics Analysis
Download from

64. Molecular Evolutionary Genetics Analysis
MEGA version 4:
Molecular Evolutionary Genetics Analysis

65. Molecular Evolutionary Genetics Analysis
MEGA version 4:
Molecular Evolutionary Genetics Analysis 1 2
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

66. [1] Open the
Alignment Explorer
[2] Select “Create
a new alignment”
[3] Click yes (for DNA)
or no (for protein)

67. [4] Find, select, and copy a multiple sequence alignment (e. g
[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