bioinformatics methods course multiple sequence alignment burkhard morgenstern university of...

Bioinformatics Methods Course

Multiple Sequence Alignment

Burkhard Morgenstern

University of GöttingenInstitute of Microbiology and Genetics

Department of Bioinformatics

Göttingen, October/November 2006

Tools for multiple sequence alignment

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Astronomical Number of possible alignments!

T C I V - M R E A Y E

Astronomical Number of possible alignments!

Which one is the best ???

Questions in development of alignment programs:

(1) What is a good alignment?

→ objective function (`score’)

(2) How to find a good alignment?

→ optimization algorithm

First question far more important !

Before defining an objective function (scoring scheme)

What is a biologically good alignment ??

Criteria for alignment quality:

1. 3D-Structure: align residues at corresponding positions in 3D structure of protein!

2. Evolution: align residues with common ancestors!

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Alignment hypothesis about sequence evolution

Search for most plausible hypothesis!

Compute for amino acids a and b

Probability pa,b of substitution

a → b (or b → a), Frequency qa of a

Define

s(a,b) = log (pa,b / qa qb)

Traditional objective functions:

Define Score of alignments as

Sum of individual similarity scores s(a,b) Gap penalty g for each gap in alignment

Needleman-Wunsch scoring system (1970) for pairwise alignment (= alignment of two sequences)

T Y W I V

T - - L V

Example:

Score = s(T,T) + s(I,L) + s (V,V) – 2 g

T Y W I V

T - - L V

Idea: alignment with optimal (maximal) score probably biologically meaningful.

Dynamic programming algorithm finds optimal alignment for two sequences efficiently (Needleman and Wunsch, 1970).

Traditional Objective functions can be generalized to multiple alignment (e.g. sum-of-pair score, tree alignment)

Needleman-Wunsch algorithm can also be generalized to find optimal multiple alignment, but:

Very time and memory consuming!

-> Heuristic algorithm needed, i.e. fast but sub-optimal solution

Most commonly used heuristic for multiple alignment:

Progressive alignment

(mid 1980s)

`Progressive´ Alignment

WCEAQTKNGQGWVPSNYITPVN

WWRLNDKEGYVPRNLLGLYP

AVVIQDNSDIKVVPKAKIIRD

YAVESEAHPGSFQPVAALERIN

WLNYNETTGERGDFPGTYVEYIGRKKISP

WWRLNDKEGYVPRNLLGLYP

AVVIQDNSDIKVVPKAKIIRD

YAVESEAHPGSFQPVAALERIN

WLNYNETTGERGDFPGTYVEYIGRKKISP

Guide tree

WW--RLNDKEGYVPRNLLGLYP-

AVVIQDNSDIKVVP--KAKIIRD

YAVESEASFQPVAALERIN

WLNYNEERGDFPGTYVEYIGRKKISP

Profile alignment, “once a gap - always a gap”

YAVESEASVQ--PVAALERIN------

WLN-YNEERGDFPGTYVEYIGRKKISP

WCEAQTKNGQGWVPSNYITPVN-

YAVESEASVQ--PVAALERIN------

WLN-YNEERGDFPGTYVEYIGRKKISP

WCEAQTKNGQGWVPSNYITPVN--------

WW--RLNDKEGYVPRNLLGLYP--------

AVVIQDNSDIKVVP--KAKIIRD-------

YAVESEA---SVQ--PVAALERIN------

WLN-YNE---ERGDFPGTYVEYIGRKKISP

CLUSTAL W

Most important software program:

CLUSTAL W:

J. Thompson, T. Gibson, D. Higgins (1994), CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment … Nuc. Acids. Res. 22, 4673 - 4680

(~ 20.000 citations in the literature)

Problems with traditional approach:

Results depend on gap penalty

Heuristic guide tree determines alignment;

alignment used for phylogeny reconstruction

Algorithm produces global alignments.

Problems with traditional approach:

Many sequence families share only local similarity

E.g. sequences share one conserved motif

Local sequence alignment

Find common motif in sequences; ignore the rest

ERYENS

Find common motif in sequences; ignore the rest

E-YENS

ERYENS

ERYA-S

Find common motif in sequences; ignore the rest – Local alignment

E-YENSERYENSERYA-S

Gibbs Motive Sampler

Local multiple alignment without gaps:

C.E. Lawrence et al. (1993)Detecting subtle sequence signals: a Gibbs Sampling Strategy for Multiple AlignmentScience, 262, 208 - 214

Traditional alignment approaches:

Either global or local methods!

New question: sequence families with multiple local similarities

Neither local nor global methods appliccable

New question: sequence families with multiple local similarities

Alignment possible if order conserved

The DIALIGN approach

Morgenstern, Dress, Werner (1996),PNAS 93, 12098-12103

Combination of global and local methods

Assemble multiple alignment from gap-free local pair-wise alignments (,,fragments“)

atctaatagttaaactcccccgtgcttag

cagtgcgtgtattactaacggttcaatcgcg

caaagagtatcacccctgaattgaataa

atc------taatagttaaactcccccgtgcttag

caaa--gagtatcacccctgaattgaataa

caaa--gagtatcacc----------cctgaattgaataa

atc------taatagttaaactcccccgtgc-ttag

cagtgcgtgtattactaac----------gg-ttcaatcgcg

atc------taatagttaaactcccccgtgc-ttag

cagtgcgtgtattactaac----------gg-ttcaatcgcg

Consistency!

atc------TAATAGTTAaactccccCGTGC-TTag

cagtgcGTGTATTACTAAc----------GG-TTCAATcgcg

caaa--GAGTATCAcc----------CCTGaaTTGAATaa

Multiple alignment:

caaccctgaattgaagagtatcacataa

(1) Calculate all optimal pair-wise alignments

Multiple alignment:

Fragments from optimal pair-wise alignments might be inconsistent

Score of alignment:

Define weight score for fragments based on probability of random occurrence

Score of alignment = sum of weight scores of fragments

Goal: find consistent set of fragments with maximum total weight

Advantages of segment-based approach:

Program can produce global and local alignments!

Sequence families alignable that cannot be aligned with standard methods

T-COFFEE

C. Notredame, D. Higgins, J. Heringa (2000), T-Coffee: A novel algorithm for multiple sequence alignment, J. Mol. Biol.

T-COFFEE

T-COFFEE Less sensitive to spurious pairwise similarities Can handle local homologies better than CLUSTAL

T-COFFEE

1. Build library of pairwise alignments

2. Alignment from seq i, j and seq j, k supports alignmetn from seq i, k.

Evaluation of multi-alignment methods

Alignment evaluation by comparison to trusted benchmark alignments.

`True’ alignment known by information about structure or evolution.

For protein alignment:

M. McClure et al. (1994):

4 protein families, known functional sites

J. Thompson et al. (1999):

Benchmark data base, 130 known 3D structures (BAliBASE)

T. Lassmann & E. Sonnhammer (2002): BAliBASE + simulated evolution (ROSE)

1aboA 1 .NLFVALYDfvasgdntlsitkGEKLRVLgynhn..............gE 1ycsB 1 kGVIYALWDyepqnddelpmkeGDCMTIIhrede............deiE 1pht 1 gYQYRALYDykkereedidlhlGDILTVNkgslvalgfsdgqearpeeiG 1ihvA 1 .NFRVYYRDsrd......pvwkGPAKLLWkg.................eG 1vie 1 .drvrkksga.........awqGQIVGWYctnlt.............peG

1aboA 36 WCEAQt..kngqGWVPSNYITPVN...... 1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP...... 1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp 1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd..... 1vie 28 YAVESeahpgsvQIYPVAALERIN......

alpha helix RED beta strand GREEN core blocks UNDERSCORE BAliBASE

Reference alignments

Result: DIALIGN best method for distantly related sequences, T-Coffee best for globally related proteins

BAliBASE: 5 categories of benchmark sequences

(globally related, internal gaps, end gaps)

CLUSTAL W, T-COFFEE, MAFFT, PROBCONS perform well on globally related sequences, DIALIGN superior for local similarities

Conclusion: no single best multi alignment program!

Advice: try different methods!

Anchored sequence alignment

Idea: semi-automatic alignment

use expert knowledge to define constraints instead of fully automated alignment

Define parts of the sequences where biologically correct alignment is known as anchor points, align rest of the sequences automatically.

NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN

IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT

GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS

NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN

IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT

Anchor points in multiple alignment

NLFV ALYDFVASGDNTLSITKGEKLRVLGYNHN

IIHREDKGVIYALWDYEPQND DELPMKEGDCMT

Anchor points in multiple alignment

-------NLF V-ALYDFVAS GD-------- NTLSITKGEk lrvLGYNhn

iihredkGVI Y-ALWDYEPQ ND-------- DELPMKEGDC MT-------

-------GYQ YrALYDYKKE REedidlhlg DILTVNKGSL VA-LGFS--

Anchored multiple alignment

Algorithmic questions

Find optimal alignment (=consistent set of fragments) under costraints given by user-specified anchor points!

Additional input file with anchor points:

1 3 215 231 5 4.5

2 3 34 78 23 1.23

1 4 317 402 8 8.5

NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN IIHREDKGVIYALWDYEPQNDDELPMKEGDCMTGYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS

1 3 215 231 5 4.5

2 3 34 78 23 1.23

1 4 317 402 8 8.5

1 3 215 231 5 4.5

2 3 34 78 23 1.23

1 4 317 402 8 8.5

Sequences

1 3 215 231 5 4.5

2 3 34 78 23 1.23

1 4 317 402 8 8.5

Sequences start positions

1 3 215 231 5 4.5

2 3 34 78 23 1.23

1 4 317 402 8 8.5

Sequences start positions length

1 3 215 231 5 4.5

2 3 34 78 23 1.23

1 4 317 402 8 8.5

Sequences start positions length score

bioinformatics methods course multiple sequence alignment burkhard morgenstern university of...

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