Multiple sequence alignment. Multiple sequence alignment: outline [1] Introduction to MSA Exact...

Multiple sequence alignment

Multiple sequence alignment: outline

[1] Introduction to MSAExact methodsProgressive (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

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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ClustalW

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

Praline

MUSCLE

Probcons

TCoffee

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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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

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






[6] Introduction to molecular evolution and phylogeny

Multiple sequence alignment: methods

Exact methods: dynamic programmingInstead of the 2-D dynamic programming matrix in theNeedleman-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.






[6] Introduction to molecular evolution and phylogeny


Progressive methods: use a guide tree (a little like aphylogenetic tree but NOT a phylogenetic tree) to determine how to combine pairwise alignments one by oneto create a multiple alignment.

Making multiple alignments using trees was a verypopular subject in the ‘80s. Fitch and Yasunobu (1974)may have first proposed the idea, but Hogeweg andHesper (1984) and many others worked on the topic beforeFeng and Doolittle (1987)—they made one important contribution that got their names attached to thisalignment method.

Examples: CLUSTALW, MUSCLE


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

• 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) }

Multidimensional Dynamic Programming

HOW CAN I ALIGN MANY SEQUENCES

2 Globins =>1 Min

3 Globins =>2 hours


4 Globins => 10 days


5 Globins => 3 years


6 Globins =>300 years


7 Globins =>30. 000 years


Solidified Fossil,Old stuff

8 Globins =>3 Million years


• The Choice of an objective functionBiological 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, …)

Alignment Costs

A C

A

A

C

A, A, A, C, C

--

6

A

A

A

A

AC

C

C

A, A, A, C, C

A, A, C

1

C

C

A

A

A

A

A, A, A, C, C

A

2

Traditional

Input seq

Reconstructedseq

Missmatches

Traditional (SP) Tree-Alignment Star-Alignment

The Progressive Multiple Alignment

Algorithm(Clustal W)

Making An Alignment

Any Exact Method would be TOO SLOW

We will use a Heuristic Algorithm.

Progressive Alignment Algorithm is the most Popular

-Fast

-ClustalW

-Greedy Heuristic (No Guarranty).

Progressive Alignment

Feng and Dolittle, 1988; Taylor 1989

Clustering

Dynamic Programming Using A Substitution Matrix



-Depends on the ORDER of the sequences (Tree).

-Depends on the CHOICE of the sequences.

-Depends on the PARAMETERS:

•Substitution Matrix.

•Penalties (Gop, Gep).

•Sequence Weight.

•Tree making Algorithm.

Example : Progressive alignment

MSA by adding sequences

Pairwise Alignment

1 + 2

3 + 4

1 + 3

1 + 4

2 + 4

2 + 3

Guide Tree

1

2

3

4

2

3

4

1

Progressive alignment (cont.)

1 2 3 4 5

1

2

3

4

5

Sequence

Distance Matrix:

displays distances of all sequence pairs.

1

45

3

2

Guide Tree

UPGMA (unweighted pair group method of arithmetic averages)

or Neighbour-Joining method

D = 1 - S

UPGMA Clustering (Guide Tree)

d ij 1 2 3 4 51 0 2 6 9 72 0 5 7 73 0 5 44 0 35 0

d ij

20

.5

d ij

.5

30

55..

d ij u wu 0 6w 0

.840

5.

6 0

.5

12

3

5

4

12

3

5

4

12

3

5

4

12

3

5

4

u 3 4 5u 0 5 8 73 0 5 44 0 35 0

u 3 vu 0 5 73 0 4v 0

Progressive alignment (cont.)

• 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.

1

45

3

2

Guide Tree

21

Alignment of alignments

SeqA GARFIELD THE LAST FA-T CATSeqB GARFIELD THE FAST CA-T ---SeqC GARFIELD THE VERY FAST CATSeqD -------- THE ---- FA-T CAT

CLUSTALW (Score=20, Gop=-1, Gep=0, M=1)

SeqA GARFIELD THE LAST FA-T CATSeqB GARFIELD THE FAST ---- CATSeqC GARFIELD THE VERY FAST CATSeqD -------- THE ---- FA-T CAT

CORRECT (Score=24)

Progressive AlignmentWhen Doesn’t It Work

GARFIELD THE LAST FAT CATGARFIELD THE FAST CAT ---

GARFIELD THE LAST FAT CAT

GARFIELD THE FAST CAT

GARFIELD THE VERY FAST CAT

THE FAT CAT

GARFIELD THE VERY FAST CAT-------- THE ---- FA-T CAT

GARFIELD THE LAST FA-T CATGARFIELD THE FAST CA-T ---GARFIELD THE VERY FAST CAT-------- THE ---- FA-T CAT

41

ABCDE

Iterative alignment

Guide treeMSA

Pairwise distance table

A

DCB

AA BB CC DD EE

AA

BB 1111

CC 33 11

DD 22 22 1010

EE 11 11 11 11

Iterate until the MSA doesn’t change (convergence)

E

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

Get sequences from Entrez Protein (or HomoloGene)

You can display sequences from Entrez Protein in the fasta format

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.

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

Use ClustalW to do a progressive MSA

http://www2.ebi.ac.uk/clustalw/ Fig. 10.1

Page 321

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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Progressive MSA stage 1 of 3:generate global pairwise alignments

Fig. 10.2Page 323

five distantly related lipocalins

best score

Progressive MSA stage 1 of 3:generate global pairwise alignments

Start of Pairwise alignmentsAligning...Sequences (1:2) Aligned. Score: 84Sequences (1:3) Aligned. Score: 84Sequences (1:4) Aligned. Score: 91Sequences (1:5) Aligned. Score: 92Sequences (2:3) Aligned. Score: 99Sequences (2:4) Aligned. Score: 86Sequences (2:5) Aligned. Score: 85Sequences (3:4) Aligned. Score: 85Sequences (3:5) Aligned. Score: 84Sequences (4:5) Aligned. Score: 96

Fig. 10.4Page 325

five closely related lipocalins

best score

Number of pairwise alignments needed

For n sequences, (n-1)(n) / 2

For 5 sequences, (4)(5) / 2 = 10

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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

Progressive MSA stage 2 of 3:generate a guide tree calculated from

the distance matrix

Fig. 10.2Page 323

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. 10.4Page 325

five closely related lipocalins

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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Progressive MSA stage 3 of 3:progressively align the sequences

following the branch order of the tree

Fig. 10.3Page 324

Progressive MSA stage 3 of 3:CLUSTALX output

Note that you can download CLUSTALX locally, rather than using a web-based program!

Clustal W alignment of 5 closely related lipocalins

CLUSTAL W (1.82) multiple sequence alignment

gi|89271|pir||A39486 MEWVWALVLLAALGSAQAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 50gi|132403|sp|P18902|RETB_BOVIN ------------------ERDCRVSSFRVKENFDKARFAGTWYAMAKKDP 32gi|5803139|ref|NP_006735.1| MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 48gi|6174963|sp|Q00724|RETB_MOUS MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50gi|132407|sp|P04916|RETB_RAT MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50 ********************:* ***:*****

gi|89271|pir||A39486 EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 100gi|132403|sp|P18902|RETB_BOVIN EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 82gi|5803139|ref|NP_006735.1| EGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTED 98gi|6174963|sp|Q00724|RETB_MOUS EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100gi|132407|sp|P04916|RETB_RAT EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100 *********:*******.*:************.**:**************

gi|89271|pir||A39486 PAKFKMKYWGVASFLQKGNDDHWIIDTDYDTYAAQYSCRLQNLDGTCADS 150gi|132403|sp|P18902|RETB_BOVIN PAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSCRLLNLDGTCADS 132gi|5803139|ref|NP_006735.1| PAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADS 148gi|6174963|sp|Q00724|RETB_MOUS PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150gi|132407|sp|P04916|RETB_RAT PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150 ****************:*******:****:*:* ****** *********

Fig. 10.5Page 326

* asterisks indicate identity in a column


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 CATTHE FAST CAT ---

THE LAST FA-T CATTHE FAST CA-T ---THE VERY FAST CAT THE LAST FA-T CAT

THE FAST CA-T ---THE VERY FAST CATTHE ---- FA-T CATAdapted from C. Notredame, Pharmacogenomics 2002


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 CATTHE FAST CAT

THE ---- FA-T CATTHE ---- FAST CATTHE VERY FAST CAT THE ---- FA-T CAT

THE ---- FAST CATTHE VERY FAST CATTHE LAST FA-T CATAdapted from C. Notredame, Pharmacogenomics 2002

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 beto give more weight to distantly related sequences

• To maintain the initial gap choices is to trustthat those gaps are most believable

Page 324

Additional features of ClustalW improveits 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 presenceof conserved or divergent sequences, e.g.:

PAM20 80-100% idPAM60 60-80% idPAM120 40-60% idPAM350 0-40% id

• Residue-specific gap penalties are applied

MEGA version 4: Molecular Evolutionary Genetics Analysis

Download from www.megasoftware.net



Two ways to create a multiple sequence alignment1. Open the Alignment Explorer, paste in a FASTA MSA2. Select a DNA query, do a BLAST search

Once your sequences are in MEGA, you can run ClustalWthen make trees and do phylogenetic analyses

1

2

[1] Open the Alignment Explorer

[2] Select “Create a new alignment”

[3] Click yes (for DNA) or no (for protein)

[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 .masthen exit and save as .meg

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