Multiple sequence alignment

1
Multiple sequence alignmentWhy?

• It is the most important means to assess
  relatedness of a set of sequences
• Gain information about the structure/function of
  a query sequence (conservation patterns)
• Construct a phylogenetic tree
• Putting together a set of sequenced fragments
  (Fragment assembly)
• Recognise alternative splice sites
• Many bioinformatics methods depend on it
  (secondary/tertiary structure)

2
Multiple sequence alignment (MSA) of 12
Flavodoxin cheY
3
Pairwise alignment

• Now we know how to do it
• How do we get a multiple alignment (three or more
  sequences)?
• Multiple alignment much greater combinatorial
  explosion than with pairwise alignment..

4
Multi-dimensional dynamic programming(Murata et
al. 1985)
5
Simultaneous Multiple alignmentMulti-dimensional
dynamic programming

• MSA (Lipman et al., 1989, PNAS 86, 4412)
• extremely slow and memory intensive
• up to 8-9 sequences of 250 residues
• DCA (Stoye et al., 1997, CABIOS 13, 625)
• still very slow

6
Alternative multiple alignment methods

• Biopat (Hogeweg Hesper 1984, first method ever)
• MULTAL (Taylor 1987)
• DIALIGN (Morgenstern 1996)
• PRRP (Gotoh 1996)
• Clustal (Thompson Higgins Gibson 1994)
• Praline (Heringa 1999)
• T-Coffee (Notredame Higgins Heringa 2000)
• HMMER (Eddy 1998) Hidden Markov Model
• SAGA (Notredame Higgins1996) Genetic algorithm

7
Progressive multiple alignment general principles
1
Score 1-2
2
1
Score 1-3
3
4
Score 4-5
5
Scores
Similarity matrix
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Scores to distances
Iteration possibilities
Guide tree
Multiple alignment
8
General progressive multiple alignment
technique(follow generated tree)
d
1
3
1
3
2
5
1
3
2
5
1
root
3
2
5
4
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Progressive multiple alignment

• Problem
• Accuracy is very important
• Errors are propagated into the progressive steps
• Once a gap, always a gap
• Feng Doolittle, 1987

10
Pair-wise alignment quality versus sequence
identity(Vogt et al., JMB 249, 816-831,1995)
11
Multiple alignment profilesGribskov et al. 1987
i
A C D ? ? ? W Y
0.3 0.1 0 ? ? ? 0.3 0.3
Gap penalties
0.5
1.0
Position dependent gap penalties
12
Profile-sequence alignment
sequence
profile
ACDVWY
13
Profile-profile alignment
profile
A C D . . Y
profile
ACDVWY
14
Clustal, ClustalW, ClustalX

• CLUSTAL W/X (Thompson et al., 1994) uses
  Neighbour Joining (NJ) algorithm (Saitou and Nei,
  1984), widely used in phylogenetic analysis, to
  construct guide tree.
• Sequence blocks are represented by profiles, in
  which the individual sequences are additionally
  weighted according to the branch lengths in the
  NJ tree.
• Further carefully crafted heuristics include
• (i) local gap penalties
• (ii) automatic selection of the amino acid
  substitution matrix, (iii) automatic gap penalty
  adjustment
• (iv) mechanism to delay alignment of sequences
  that appear to be distant at the time they are
  considered.
• CLUSTAL (W/X) does not allow iteration (Hogeweg
  and Hesper, 1984 Corpet, 1988, Gotoh, 1996
  Heringa, 1999, 2002)

15
Strategies for multiple sequence alignment

• Profile pre-processing
• Secondary structure-induced alignment
• Globalised local alignment
• Matrix extension
• Objective try to avoid (early) errors

16
Pre-profile generation
1
Score 1-2
2
1
Score 1-3
3
4
Score 4-5
5
Cut-off
Pre-profiles
Pre-alignments
1
A C D . . Y
1
2
3
4
5
2
2
A C D . . Y
1
3
4
5
5
A C D . . Y
1
5
2
3
4
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Pre-profile alignment
Pre-profiles
1
A C D . . Y
2
A C D . . Y
Final alignment
3
A C D . . Y
1
2
3
4
5
4
A C D . . Y
A C D . . Y
5
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Pre-profile alignment
1
2
1
3
4
5
2
2
1
3
4
Final alignment
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3
1
1
3
2
2
4
3
5
4
5
4
4
1
2
3
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5
1
5
2
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4
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Strategies for multiple sequence alignment

• Profile pre-processing
• Secondary structure-induced alignment
• Globalised local alignment
• Matrix extension
• Objective try to avoid (early) errors

20
Protein structure hierarchical levels
TERTIARY STRUCTURE (fold)
21
One of the Molecular Biology Dogmas

• Structure more conserved than sequence

22
Secondary structure-induced alignment
23
Using secondary structure for alignment
Dynamic programming search matrix
Amino acid exchange weights matrices
MDAGSTVILCFV
HHHCCCEEEEEE
M D A A S T I L C G S
H H H H C C E E E C C
H
H
C
C
E
E
Default
24
Flavodoxin-cheYUsing predicted secondary
structure
1fx1 -PK-ALIVYGSTTGNTEYTAETIARQLANAG-YE
VDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLFD
S-LEETGAQGRKVACF e eeee b
ssshhhhhhhhhhhhhhttt eeeee stt tttttt seeee b
ee sss ee ttthhhhtt ttss tt
eeeee FLAV_DESVH MPK-ALIVYGSTTGNTEYTaETIARELA
DAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDD
FIPLFDS-LEETGAQGRKVACf e eeeeee
hhhhhhhhhhhhhhh eeeeee eeeeee
hhhhhh
eeeee FLAV_DESGI MPK-ALIVYGSTTGNTEGVaEAIAKTLN
SEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQED
FVPLYED-LDRAGLKDKKVGVf e eeeeee
hhhhhhhhhhhhhh eeeeee hhhhhh eeeeeee
hhhhhh
eeeeee FLAV_DESSA MSK-SLIVYGSTTGNTETAaEYVAEAF
ENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQD
DFIPLYDS-LENADLKGKKVSVf
eeeeee hhhhhhhhhhhhhh eeeee
eeeee hhhhhhh h
eeeee FLAV_DESDE MSK-VLIVFGSSTGNTESIaQKLEELIA
AGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDD
FLSLFEE-FNRFGLAGRKVAAf eeee
hhhhhhhhhhhhhh eeeee hhhhhhhhhhheeeee
hhhhhhh hh eeeee 2fcr
--K-IGIFFSTSTGNTTEVADFIGKTLGAK---ADAPIDVDDVT
DPQALKDYDLLFLGAPTWNTGAD----TERSGTSWDEFLYDKLPEVDMKD
LPVAIF eeeee
ssshhhhhhhhhhhhhggg b eeggg s gggggg seeeeeee
stt s s s sthhhhhhhtggg tt
eeeee FLAV_ANASP SKK-IGLFYGTQTGKTESVaEIIRDEFG
ND--VVTL-HDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSD
WEGLYSE-LDDVDFNGKLVAYf eeeee
hhhhhhhhhhhh eee hhh hhhhhhheeeeee
hhhhhhhhh
eeeeee FLAV_ECOLI -AI-TGIFFGSDTGNTENIaKMIQKQL
GKD--VADV-HDIAKSS-KEDLEAYDILLLgIPTWYYGEA--------QC
DWDDFFPT-LEEIDFNGKLVALf eee
hhhhhhhhhhhh eee hhh hhhhhhheeeee
hhhhh
eeeeee FLAV_AZOVI -AK-IGLFFGSNTGKTRKVaKSIKKRF
DDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENE
SWEEFLPK-IEGLDFSGKTVALf eee
hhhhhhhhhhhhh hhh hhhhhhheeeee
hhhhhhhhh
eeeeee FLAV_ENTAG MAT-IGIFFGSDTGQTRKVaKLIHQKL
DG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYD
SWQEFTNT-LSEADLTGKTVALf eeee
hhhhhhhhhhhh hhh hhhhhhheeeee
hhhhh eeeee 4fxn
----MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDV
NIDELLNE-DILILGCSAMGDEVL------E-ESEFEPFIEE-IST-KIS
GKKVALF eeeee
ssshhhhhhhhhhhhhhhtt eeeettt sttttt seeeeee
btttb ttthhhhhhh hst t tt
eeeee FLAV_MEGEL M---VEIVYWSGTGNTEAMaNEIEAAVK
AAG-ADVESVRFEDTNVDDVASK-DVILLgCPAMGSEEL------E-DSV
VEPFFTD-LAP-KLKGKKVGLf
hhhhhhhhhhhhhh eeeee hhhhhhhh eeeee

eeeee FLAV_CLOAB M-K-ISILYSSKTGKTERVaKLIEEGVK
RSGNIEVKTMNL-DAVDKKFLQESEGIIFgTPTY-YANI--------SWE
MKKWIDE-SSEFNLEGKLGAAf eee
hhhhhhhhhhhhhh eeeeee hhhhhhhhhh eeee
hhhhhhhhh eeeee 3chy
ADKELKFLVVDDFSTMRRIVRNLLKELGFNN-VEEAEDGV-DAL
NKLQAGGYGFVISD---WNMPNM----------DGLELLKTIRADGAMSA
LPVLMV tt eeee s
hhhhhhhhhhhhhht eeeesshh hhhhhhhh eeeee s
sss hhhhhhhhhh ttttt eeee 1fx1
GCGDS-SY-EYFCGAVDAIEEKLKNLGAEIVQD-----------
----------GLRIDGD--PRAARDDIVGWAHDVRGAI--------
eee s ss sstthhhhhhhhhhhttt ee s
eeees gggghhhhhhhhhhhhhh FLAV_
DESVH GCGDS-SY-EYFCGAVDAIEEKLKNLgAEIVQD------
---------------GLRIDGD--PRAARDDIVGwAHDVRGAI-------
- eee hhhhhhhhhhhh
eeeee eeeee
hhhhhhhhhhhhhh FLAV_DESGI GCGDS-SY-TYFCGAVDVI
EKKAEELgATLVAS---------------------SLKIDGE--P--DSA
EVLDwAREVLARV-------- eee
hhhhhhhhhhhh eeeee
hhhhhhhhhhh FLAV_DESSA
GCGDS-DY-TYFCGAVDAIEEKLEKMgAVVIGD-----------------
----SLKIDGD--P--ERDEIVSwGSGIADKI--------
hhhhhhhhhhhh eeeee
e eee FLAV_DESDE
ASGDQ-EY-EHFCGAVPAIEERAKELgATIIAE-----------------
----GLKMEGD--ASNDPEAVASfAEDVLKQL--------
e hhhhhhhhhhhhhh eeeee
ee hhhhhhhhhhh 2fcr
GLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSV
RD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------
eee ttt ttsttthhhhhhhhhhhtt eee b gggs
s tteet teesseeeettt ss hhhhhhhhhhhhhhhht FLAV_A
NASP GTGDQIGYADNFQDAIGILEEKISQRgGKTVGYWSTDGYD
FNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------
hhhhhhhhhhhhhh
eeee
hhhhhhhhhhhhhhhh FLAV_ECOLI
GCGDQEDYAEYFCDALGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADD
DHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA
hhhhhhhhhhhhhh eeee
hhhhhhhhhhhhhhhhhh FLAV_AZOVI
GLGDQVGYPENYLDALGELYSFFKDRgAKIVGSWSTDGYEFESS
EAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L--
e hhhhhhhhhhhhhh eeeee
hhhhhhhhhhh FLAV_ENTA
G GLGDQLNYSKNFVSAMRILYDLVIARgACVVGNWPREGYKFSF
SAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L------
hhhhhhhhhhhhhhh eeee
hhhhhhh hhhhhhhhhhhh 4fxn
G-----SYGWGDGKWMRDFEERMNGYGCVVVET---------
------------PLIVQNE--PDEAEQDCIEFGKKIANI---------
e eesss shhhhhhhhhhhhtt ee s
eeees ggghhhhhhhhhhhht FLAV
_MEGEL G-----SYGWGSGEWMDAWKQRTEDTgATVIGT-----
-----------------AIVNEM--PDNAPE-CKElGEAAAKA-------
-- hhhhhhhhhhh
eeeee eeee h
hhhhhhhh FLAV_CLOAB STANSIA-GGSDIALLTILNHLMVK
-gMLVYSG----GVAFGKPKTHLG-----YVHINEI--QENEDENARIfG
ERiANkV--KQIF--
hhhhhhhhhhhhhh eeeee
hhhh hhh hhhhhhhhhhhh h 3chy
-----------TAEAKKENIIAAAQAGASGY-------------------
------VVK----P-FTAATLEEKLNKIFEKLGM------
ess hhhhhhhhhtt see
ees s hhhhhhhhhhhhhhht

G
25
Strategies for multiple sequence alignment

• Profile pre-processing
• Secondary structure-induced alignment
• Globalised local alignment
• Matrix extension
• Objective try to avoid (early) errors

26
Globalised local alignment
1. Local (SW) alignment (M Po,e)


2. Global (NW) alignment (no M or Po,e)
Double dynamic programming
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M BLOSUM62, Po 0, Pe 0
28
M BLOSUM62, Po 12, Pe 1
29
M BLOSUM62, Po 60, Pe 5
30
Strategies for multiple sequence alignment

• Profile pre-processing
• Secondary structure-induced alignment
• Globalised local alignment
• Matrix extension
• Objective try to avoid (early) errors

31
Matrix extension

• T-Coffee
• Tree-based Consistency Objective Function For
  alignmEnt Evaluation
• Cedric Notredame
• Des Higgins
• Jaap Heringa J. Mol. Biol., 302, 205-2172000

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Matrix extension T COFFEE
2
1
3
1
4
1
3
2
4
2
4
3
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Integrating alignment methods and alignment
information with T-Coffee

• Integrating different pair-wise alignment
  techniques (NW, SW, ..)
• Combining different multiple alignment methods
  (consensus multiple alignment)
• Combining sequence alignment methods with
  structural alignment techniques
• Plug in user knowledge

34
Using different sources of alignment information

Structure alignments
Clustal
Clustal
Dialign
Lalign
Manual
T-Coffee
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Search matrix extension
36
T-Coffee

• Combine different alignment techniques by adding
  scores
• W(A(x), B(y)) ?S(A(x), B(y))
• A(x) is residue x in sequence A
• summation is over the scores S of the global and
  local alignments containing the residue pair
  (A(x), B(y))
• S is sequence identity percentage of the
  associated alignment
• Combine direct alignment seqA- seqB with each
  seqA-seqI-seqB
• W(A(x), B(y)) W(A(x), B(y))
• ?I?A,BMin(W(A(x), I(z)), W(I(z), B(y)))
• Summation over all third sequences I other than A
  or B

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T-Coffee
Other sequences
Direct alignment
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Search matrix extension
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Evaluating multiple alignments

• Conflicting standards of truth
• evolution
• structure
• function
• With orphan sequences no additional information
• Benchmarks depending on reference alignments
• Quality issue of available reference alignment
  databases
• Different ways to quantify agreement with
  reference alignment (sum-of-pairs, column score)
• Charlie Chaplin problem

40
Evaluating multiple alignments

• As a standard of truth, often a reference
  alignment based on structural superpositioning is
  taken

41
Evaluation measures
Query
Reference
Column score
Sum-of-Pairs score
42
Evaluating multiple alignments
?SP
BAliBASE alignment nseq len
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Summary

• Weighting schemes simulating simultaneous
  multiple alignment
• Profile pre-processing (global/local)
• Matrix extension (well balanced scheme)
• Smoothing alignment signals
• globalised local alignment
• Using additional information
• secondary structure driven alignment
• Schemes strike balance between speed and
  sensitivity

44
References

• Heringa, J. (1999) Two strategies for sequence
  comparison profile-preprocessed and secondary
  structure-induced multiple alignment. Comp. Chem.
  23, 341-364.
• Notredame, C., Higgins, D.G., Heringa, J. (2000)
  T-Coffee a novel method for fast and accurate
  multiple sequence alignment. J. Mol. Biol., 302,
  205-217.
• Heringa, J. (2002) Local weighting schemes for
  protein multiple sequence alignment. Comput.
  Chem., 26(5), 459-477.

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Where to find this.http//www.ibivu.cs.vu.nl/tea
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