Multiple Global Sequence Alignment and Phylogenetic trees

Eidhammer et al. Protein Bioinformatics Chapter 4

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Multiple Global Sequence Alignment and

Phylogenetic trees

Inge Jonassen

and

Ingvar Eidhammer


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Definition

• A global alignment of a set of sequences is obtained by– inserting into each sequence gap characters ‘

’

• so that– the resulting sequences are of the same

length

• and so that– no “column” has only gap characters


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Example: Chromo domains aligned


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Use of alignments• High sequence similarity usually means significant structural and/or

functional similarity. The reverse does not need to be true

• Homolog proteins (common ancestor) can vary significantly in large parts of the sequences, but still retain common 2D-patterns, 3D-patterns or common active site or binding site.

• Comparison of several sequences in a family can reveal what is common for the family (From Lesk: Two homologous sequences whisper,.. A full multiple alignment shouts out load). Something common for several sequences can be significant when regarding all of the sequences, but need not if regarding only two.

• Multiple alignment can be used to derive evolutionary history.


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Use of alignments

• Predict features of aligned objects– conserved positions

• structurally/functionally important


6Conserved positions


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Use of alignments



– patterns of hydrophobicity/hydrophilicity• secondary structure elements


8Helix pattern


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Use of alignments




– “gappy” regions• loops/variable regions


10Loop? Loop?Loop?


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Use of alignments




– “gappy” regions• loops/variable regions

– covariation• structural proximity


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Use of Alignments- make patterns/profiles

• Can make a profile or a pattern that can be used to match against a sequence database and identify new family members

• Profiles/patterns can be used to predict family membership of new sequences

• Databases of profiles/patterns– PROSITE– PFAM– PRINTS– ...


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Prosite: Motifs for classification

Protein sequence

Prositepattern 1

Prositepattern 2

Prositepattern n

Family 1 Family 2 Family n

PatternRegular expression

Profile


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Pattern from alignment[FYL]-x-[LIVMC]-[KR]-W-x-[GDNR]-[FYWLE]-x(5,6)-[ST]-W-[ES]-[PSTDN]-x(3)-[LIVMC]


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Alignment problem

Given a set of sequences, produce a multiple alignment which corresponds as

well as possible to the biological relationships between the corresponding

bio-molecules


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For homologous proteins

• Two residues should be aligned (on top of each other)– if they are homologous (evolved from the

same residue in a common ancestor protein)– if they are structurally equivalent


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Automatic approach

• Need a way of scoring alignments – fitness function which for an alignment

quantifies its “goodness”

• Need an algorithm for finding alignments with good scores

• Not all methods provide a scoring function for the final alignment!


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Analysis of fitness function

• One can test whether the alignments optimal under a given fitness function correspond well to the biological relationships between the sequences

• For example, if the structure of (some of) the proteins are known.


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Align by use of dynamic programming

• Dynamic programming finds best alignment of k sequences with given scoring scheme

• For two sequences there are three different column types

• For three sequences there are seven different column types x means an amino acid, - a blank Sequence1 x - x x - - x Sequence2 x x - x - x - Sequence3 x x x - x - x

• Time complexity of O(nk) (sequence lengths = n)


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Use of dynamic programming

• Dynamic programming finds best alignment of k sequences given scoring scheme


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Algorithm for dynamic programming


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Connection alignment and evolutionary tree

Consider a set of sequences ARL, ARTL, ARSI, ARSL, AWTL, AWT

AlignmentAR-LARTLARSIARSLAWTLAWT-

Possible tree

Use the tree to calculate alignment

AWTL ARTL ARSL AWTL ARTL AWTLAWT- AR-L ARSI AWT- AR-L AWT- ARSL ARTL ARSI AR-L ARSL ARSI


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Phylogenetic studies

The purpose of phylogenetic studies of related objects are

• to reconstruct the correct genealogical ties between them (the topology); and

• to estimate the time of divergence between them since they last shared a common ancestor (length of edges in the tree).

In phylogenetic studies, the objects are often referred to as operational taxonomic units (OTUs). In our case the objects are protein or nucleic acid sequences. We will denote the set of sequences we have at the start for the original sequences.


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Phylogenetic studies


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Example


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Number of different tree topologies


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Additive tree


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Additive and ultrametric

Lemma1 It is possible to construct an additive tree from the distances between the sequences (metric space) if and only if for any four of them we can label them i,j,k,lsuch that Di,j + Dk,l = Di,k + Dj,l >= Di,l + Dj,k

Lemma2 It is possible to construct an ultrametric tree from the distances between theSequences (metric space) if and only if for every i,j,k Di,j <= max(Di,k,Dk,j)


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Maximum parsimony


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Parvis gruppering


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An example


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Neighbour joining


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Bootstrapping


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General progressive alignment

Algorithm 4.3. General progressive alignment.Progressive alignment of the sequences {s1, s2, . . . , sm} var C current set of alignmentsbegin C := ∅ for i := 1 to m do C := C union {{si }} end one alignment of each sequence

for i := 1 to m − 1 do choose two alignments Ap,Aq from C; C := C − {Ap,Aq } Ar := align(Ap,Aq );C := C union {Ar } end C now contains the (single) final alignmentend


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Clustering philosophy

Join the two groups with highest pairwise score.

1. Average scoring method: find average score over all pasirs in the two groups

2. Maximum scoring method: find maximum score over all pairs in the two groups (needs only one high-scoring pair)

3. Minimum (complete) scoring method: find minimum scoring over all pairs (all pairs are taken into account)

4. Special scoring method


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The Clustal Algorithm

• Three steps:1 Compare all pairs of sequences to obtain a

similarity matrix2 Based on the similarity matrix, make a guide

tree relating all the sequences3 Perform progressive alignment where the

order of the alignments is determined by the guide tree


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(A)1 pairwise comparison2 clustering/making tree

(B)3 Align according to tree


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ClustalW - Score of aligning two alignment columns

• sum the score matrix entry for all pairs of residues

• weight each pair by the sequences’ weights

1:peeksavtal2:geekaavlal

3:egewglvlhv4:aaektkirsa

Score: M(t,v)+M(t,i)+M(l,v)+M(l,i)


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ClustalW - Weighting sequences

• each sequence is given a weight

• groups of related sequences receive lower weight

Weighted score: w1*w3*M(t,v)+w1*s4*M(t,i)+w2*w3*M(l,v)+w2*w4*M(l,i)

1:peeksavtal2:geekaavlal

3:egewglvlhv4:aaektkirsa


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ClustalW - Similarity matrix

• Distance between sequences - measure from the guide tree - determines which matrix to use– 80-100% seq-id -> use Blosum80– 60-80% seq-id -> Blosum60– 30-60% seq-id -> Blosum45– 0-30% seq-id -> Blosum30


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ClustalW - Gap penalties

• Initial gap penalty– GOP

• Gap extension penalty– GEP

GTEAKLIVLMANEGA---------KL

Penalty: GOP+8*GEP


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ClustalW -Modifications of gap penalty

• Position specific penalty– gap at position

• yes -> lower GOP• no, but gap within 8 residues -> increase GOP

– hydrophilic residues• lower GOP


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Globin alignment

Default gap penaltyGEP=0.05


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Globin alignment - with insert

Default gap penaltyGEP=0.05


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Globin alignment - with insert

Lowered gap penaltyGEP=0.01


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ClustalW - summary

• Does not use a score for the final alignment

• Each pairwise alignment is done using dynamic programming

• Heuristics (e.g., gap-penalty modifications) are used - tailored to globular proteins

• Graphical version: ClustalX


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SAGA: Sequence Alignment by Genetic Algorithm

• An “objective function” is used to score the alignments

• An alignment is represented as a bit string• A population of alignment is “evolved”• Alignments can be combined (cross-over)• Alignments can be mutated• Alignments with higher score are more likely

to be chosen for mating/survival


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Evaluation of Alignment Methods

• Align set of protein sequences where the structures are known (at least for some proteins)

• Align the protein structures

• Identify “motifs” from the structure alignment

• Check if sequence alignment has correctly aligned motifs

• McClure et al, 1994

• Thompson et al, 1999


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Alignments are important

• Basis for other analyses– structure prediction– phylogeny– experiments

• PCR primer identification• site directed mutagenesis• ...

– identification of motifs


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Open Problems - space for improvements!

• Good scoring function for alignments– identify well aligned regions

• Efficient algorithms

• Resolving repeat structure, domain movements etc.

• Incorporating external information


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Future development

• More sequences– More families, but not so many– More densely populated families– “Easier” alignment problem– Identify more ancient relationships

(superfamilies)

• More structures– more sequences can be “threaded”– alignments help

Documents

Multiple Global Sequence Alignment and Phylogenetic trees