Alignment Most alignment programs create an alignment that

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Alignment Most alignment programs create an alignment that represents what happened during evolution at

Alignment Most alignment programs create an alignment that represents what happened during evolution at the DNA level. To carry over information from a well studied to a newly determined sequence, we need an alignment that represents the protein structures of today. ©CMBI 2001

The amino acids Most information that enters the alignment procedure comes from the physicochemical

The amino acids Most information that enters the alignment procedure comes from the physicochemical properties of the amino acids. Example: which is the better alignment (left or right)? CPISRTWASIFRCW CPISRT---LFRCW CPISRTWASIFRCW CPISRTL---FRCW ©CMBI 2001

A difficult alignment problem AYAYSY AGAPAPAPSP LGLPLP So, in an alignment of more than

A difficult alignment problem AYAYSY AGAPAPAPSP LGLPLP So, in an alignment of more than 2 sequences you can find more information than from just the 2 sequences you are interested in. How do we make these multisequence alignmnets? ©CMBI 2001

A difficult alignment problem solved AYAYSY AGAPAPAPSP LGLPLP ©CMBI 2001

A difficult alignment problem solved AYAYSY AGAPAPAPSP LGLPLP ©CMBI 2001

Alignment order MIESAYTDSW QFEKSYVTDY -MIESAYTDSW QFEKSYVTDY- ©CMBI 2001

Alignment order MIESAYTDSW QFEKSYVTDY -MIESAYTDSW QFEKSYVTDY- ©CMBI 2001

Alignment order MIESAYTDSW QFEKSYVTDY QWERTYASNF -MIESAYTDSW QFEKSYVTDYQWERTYASNF- ©CMBI 2001

Alignment order MIESAYTDSW QFEKSYVTDY QWERTYASNF -MIESAYTDSW QFEKSYVTDYQWERTYASNF- ©CMBI 2001

Conclusion Align first the sequences that look very much like each other. So you

Conclusion Align first the sequences that look very much like each other. So you ‘build up information’ while generating those alignments that most likely are correct. ©CMBI 2001

Alignment order In order to know which sequences look most like each other, you

Alignment order In order to know which sequences look most like each other, you need to do all pairwise alignments first. This is exactly what CLUSTAL does. CLUSTAL builds a tree while doing the build-up of the multiple sequence alignment. ©CMBI 2001

MSA and trees Take, for example, the three sequences: 1 ASWTFGHK 2 GTWSFANR 3

MSA and trees Take, for example, the three sequences: 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR and you see immediately that 2 and 3 are close, while 1 is further away. So the tree will look roughly like: 3 2 1 ©CMBI 2001

Aligning sequences; start with distances D E Matrix of pair-wise distances between five sequences.

Aligning sequences; start with distances D E Matrix of pair-wise distances between five sequences. 10 8 7 D and E are the closest pair. Take them, and collapse the matrix by one row/column. ©CMBI 2001

Aligning sequences D E A B ©CMBI 2001

Aligning sequences D E A B ©CMBI 2001

Aligning sequences C D E A B ©CMBI 2001

Aligning sequences C D E A B ©CMBI 2001

Aligning sequences C D E A B ©CMBI 2001

Aligning sequences C D E A B ©CMBI 2001

Back to the alignment 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR Actually I cheated. 1

Back to the alignment 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR Actually I cheated. 1 is closer to 3 than to 2 because of the A at position 1. How can we express this in the tree? For example: 3 2 1 2 I will call this 3 tree-flipping 1 ©CMBI 2001

Can we generalize tree-flipping? To generalize tree flipping, sequences must be placed ‘distancecorrect’ in

Can we generalize tree-flipping? To generalize tree flipping, sequences must be placed ‘distancecorrect’ in 1 dimension: And then connect them, as we did before: 2 3 So, now most info sits in the horizontal dimension. Can we use the vertical dimension usefully? 1 ©CMBI 2001

The problem is actually bigger 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR d(i, j) is

The problem is actually bigger 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR d(i, j) is the distance between sequences i and j. d(1, 2)=6; d(1, 3)=5; d(2, 3)=3. So a perfect representation would be: 3 1 2 But what if a 4 th sequence is added with d(1, 4)=4, d(2, 4)=5, d(3, 4)=4? Where would that sequence sit? ©CMBI 2001

So, nice tree, but what did we actually do? 1)We determined a distance measure

So, nice tree, but what did we actually do? 1)We determined a distance measure 2)We measured all pair-wise distances 3)We reduced the dimensionality of the space of the problem 4)We used an algorithm to visualize 5)In a way, we projected the hyperspace in which we can perfectly describe all pair-wise distances onto a 1 -dimensional line. 6)What does this sentence mean? ©CMBI 2001

Projection Gnomonic projection: Correct distances Fuller projection; Unfolded Dymaxion map Political projection Source: Wikepedia

Projection Gnomonic projection: Correct distances Fuller projection; Unfolded Dymaxion map Political projection Source: Wikepedia Mercator projection ©CMBI 2001

Back to sequences: ASASDFDFGHKMGHS ASASDFDFRRRLRIT ASLPDFLPGHSIGHS ASLPDFLPGHSIGIT ASLPDFLPRRRVRIT 1 2 5 3 6 3

Back to sequences: ASASDFDFGHKMGHS ASASDFDFRRRLRIT ASLPDFLPGHSIGHS ASLPDFLPGHSIGIT ASLPDFLPRRRVRIT 1 2 5 3 6 3 The more dimensions we retain, the less information we loose. The three is now in 3 D… ©CMBI 2001

Projection to visualize clusters We want to reduce the dimensionality with minimal distortion of

Projection to visualize clusters We want to reduce the dimensionality with minimal distortion of the pair-wise distances. One way is Eigenvector determination, or PCA. ©CMBI 2001

PCA to the rescue Now we have made the data one-dimensional, while the second,

PCA to the rescue Now we have made the data one-dimensional, while the second, vertical, dimension is noise. If we did this correctly, we kept as much data as possible. ©CMBI 2001

Back to sequences: In we have N sequences, we can only draw their distance

Back to sequences: In we have N sequences, we can only draw their distance matrix in an N-1 dimensional space. By the time it is a tree, how many dimensions, and how much information have we lost? Perhaps we should cluster in a different way? ©CMBI 2001

Cluster on critical residues? QWERTYAKDFGRGH AWTRTYAKDFGRPM SWTRTNMKDTHRKC QWGRTNMKDTHRVW Gray = conserved Red = variable

Cluster on critical residues? QWERTYAKDFGRGH AWTRTYAKDFGRPM SWTRTNMKDTHRKC QWGRTNMKDTHRVW Gray = conserved Red = variable Green = correlated ©CMBI 2001

Conclusions from correlated residues ©CMBI 2001

Conclusions from correlated residues ©CMBI 2001

Other algorithms Multi-sequence alignment can also be done with an iterative ‘profile’ alignment. A)

Other algorithms Multi-sequence alignment can also be done with an iterative ‘profile’ alignment. A) Make an alignment of few, well-aligned sequences B) Align all sequences using this profile ©CMBI 2001

1. What is a profile? Normally, we use a PAM-like matrix to determine the

1. What is a profile? Normally, we use a PAM-like matrix to determine the score for each possible match in an alignment. This assumes that all matches between I <-> E are the same. But the aren’t. ©CMBI 2001

2. What is a profile? QWERTYIPASEF QWEKSFIPGSEY NWERTMVPVSEM QFEKTYLPSSEY NFIKTLMPATEF QYIRSLIPAGEM NYIQSLIPSTEL QFIRSLFPSSEI 1

2. What is a profile? QWERTYIPASEF QWEKSFIPGSEY NWERTMVPVSEM QFEKTYLPSSEY NFIKTLMPATEF QYIRSLIPAGEM NYIQSLIPSTEL QFIRSLFPSSEI 1 2 3 At 1, E and I are both OK. At 2, I is OK, but E surely not. At 3, E is OK, but I surely not. ©CMBI 2001

3. What is a profile? The knowledge about which residue types are good at

3. What is a profile? The knowledge about which residue types are good at a certain position in the multiple sequence alignment can be expressed in a profile. A profile holds for each position 20 scores for the 20 residue types, and sometimes also two values for position specific gap open and gap elongation penalties. ©CMBI 2001

Conserved, variable, or in-between QWERTYASDFGRGH QWERTYASDTHRPM QWERTNMKDFGRKC QWERTNMKDTHRVW Gray = conserved Black = variable

Conserved, variable, or in-between QWERTYASDFGRGH QWERTYASDTHRPM QWERTNMKDFGRKC QWERTNMKDTHRVW Gray = conserved Black = variable Green = correlated mutations ©CMBI 2001

Correlated mutations determine the tree shape 1 2 3 4 AGASDFDFGHKM AGASDFDFRRRL AGLPDFMNGHSI AGLPDFMNRRRV

Correlated mutations determine the tree shape 1 2 3 4 AGASDFDFGHKM AGASDFDFRRRL AGLPDFMNGHSI AGLPDFMNRRRV ©CMBI 2001

Correlation = Information 1, 2 and 5 bind calcium; 3 and 4 don’t. Which

Correlation = Information 1, 2 and 5 bind calcium; 3 and 4 don’t. Which residues bind calcium? 1 2 3 4 5 123456789012345 ASDFNTDEKLRTTFI ASDFSTDEKLKTTFI LSFFTTDTRLATIYI LSHFLTNLRLATIYI ASDFTTDEKLALTFI Red has correct correlation, but wrong residue type. Brown has correct type, but wrong correlation. Green can be calcium-binders. ©CMBI 2001