1. Multiple sequence alignment Why?
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 alignment Multi-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 principles1
Score 1-2 2 1
Score 1-3 3 4
Score 4-5 5
Scores
Similarity
matrix
5×5
Scores to distances
Iteration possibilities
Guide tree
Multiple alignment

8. General progressive multiple alignment technique (follow generated tree)1 3 1 3 2 5 1 3 2 5
root 1 3 2 5 4

9. 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
Pair-wise alignment quality versus sequence identity (Vogt et al., JMB 249, ,1995)

11. Multiple alignment profiles Gribskov et al. 1987C D  W Y
0.3
0.1 
Gap
penalties
1.0
0.5
Position dependent gap penalties

12. Profile-sequence alignment
ACD……VWY

13. Profile-profile alignmentC D . Y
profile
ACD……VWY

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 generation1
Score 1-2 2 1
Score 1-3 3 4
Score 4-5 5
Cut-off
Pre-alignments
Pre-profiles 1 1 A C D . Y 2 3 4 5 2 2 A C D . Y 1 3 4 5 5 A C D . Y 5 1 2 3 4

17. 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 5 A C D . Y

18. Pre-profile alignment1 1 2 3 4 5 2 2 1 3 4
Final alignment 5 3 3 1 1 2 2 4 3 5 4 4 5 4 1 2 3 5 5 5 1 2 3 4

19. 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
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
QUATERNARY STRUCTURE (oligomers)
TERTIARY STRUCTURE (fold)

21. One of the Molecular Biology Dogma’s
“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 S T I L C G H C E H H C C E E
Default

24. Flavodoxin-cheY Using predicted secondary structure
1fx PK-ALIVYGSTTGNTEYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACF
e eeee b ssshhhhhhhhhhhhhhttt eeeee stt tttttt seeee b ee sss ee ttthhhhtt ttss tt eeeee
FLAV_DESVH MPK-ALIVYGSTTGNTEYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACf
e eeeeee hhhhhhhhhhhhhhh eeeeee eeeeee hhhhhh eeeee
FLAV_DESGI MPK-ALIVYGSTTGNTEGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLYED-LDRAGLKDKKVGVf
e eeeeee hhhhhhhhhhhhhh eeeeee hhhhhh eeeeeee hhhhhh eeeeee
FLAV_DESSA MSK-SLIVYGSTTGNTETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLYDS-LENADLKGKKVSVf
eeeeee hhhhhhhhhhhhhh eeeee eeeee hhhhhhh h eeeee
FLAV_DESDE MSK-VLIVFGSSTGNTESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLFEE-FNRFGLAGRKVAAf
eeee hhhhhhhhhhhhhh eeeee hhhhhhhhhhheeeee hhhhhhh hh eeeee
2fcr K-IGIFFSTSTGNTTEVADFIGKTLGAK---ADAPIDVDDVTDPQALKDYDLLFLGAPTWNTGAD----TERSGTSWDEFLYDKLPEVDMKDLPVAIF
eeeee ssshhhhhhhhhhhhhggg b eeggg s gggggg seeeeeee stt s s s sthhhhhhhtggg tt eeeee
FLAV_ANASP SKK-IGLFYGTQTGKTESVaEIIRDEFGND--VVTL-HDVSQAE-VTDLNDYQYLIIgCPTWNIGEL QSDWEGLYSE-LDDVDFNGKLVAYf
eeeee hhhhhhhhhhhh eee hhh hhhhhhheeeeee hhhhhhhhh eeeeee
FLAV_ECOLI AI-TGIFFGSDTGNTENIaKMIQKQLGKD--VADV-HDIAKSS-KEDLEAYDILLLgIPTWYYGEA QCDWDDFFPT-LEEIDFNGKLVALf
eee hhhhhhhhhhhh eee hhh hhhhhhheeeee hhhhh eeeeee
FLAV_AZOVI AK-IGLFFGSNTGKTRKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFLPK-IEGLDFSGKTVALf
eee hhhhhhhhhhhhh hhh hhhhhhheeeee hhhhhhhhh eeeeee
FLAV_ENTAG MAT-IGIFFGSDTGQTRKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFTNT-LSEADLTGKTVALf
eeee hhhhhhhhhhhh hhh hhhhhhheeeee hhhhh eeeee
4fxn MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDVNIDELLNE-DILILGCSAMGDEVL------E-ESEFEPFIEE-IST-KISGKKVALF
eeeee ssshhhhhhhhhhhhhhhtt eeeettt sttttt seeeeee btttb ttthhhhhhh hst t tt eeeee
FLAV_MEGEL M---VEIVYWSGTGNTEAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVASK-DVILLgCPAMGSEEL------E-DSVVEPFFTD-LAP-KLKGKKVGLf
hhhhhhhhhhhhhh eeeee hhhhhhhh eeeee eeeee
FLAV_CLOAB M-K-ISILYSSKTGKTERVaKLIEEGVKRSGNIEVKTMNL-DAVDKKFLQESEGIIFgTPTY-YANI SWEMKKWIDE-SSEFNLEGKLGAAf
eee hhhhhhhhhhhhhh eeeeee hhhhhhhhhh eeee hhhhhhhhh eeeee
3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFNN-VEEAEDGV-DALNKLQAGGYGFVISD---WNMPNM DGLELLKTIRADGAMSALPVLMV
tt eeee s hhhhhhhhhhhhhht eeeesshh hhhhhhhh eeeee s sss hhhhhhhhhh ttttt eeee
1fx 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-TYFCGAVDVIEKKAEELgATLVAS SLKIDGE--P--DSAEVLDwAREVLARV
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 GLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVRD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------
eee ttt ttsttthhhhhhhhhhhtt eee b gggs s tteet teesseeeettt ss hhhhhhhhhhhhhhhht
FLAV_ANASP GTGDQIGYADNFQDAIGILEEKISQRgGKTVGYWSTDGYDFNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------
hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhh
FLAV_ECOLI GCGDQEDYAEYFCDALGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA
hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhhhh
FLAV_AZOVI GLGDQVGYPENYLDALGELYSFFKDRgAKIVGSWSTDGYEFESSEAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L--
e hhhhhhhhhhhhhh eeeee hhhhhhhhhhh
FLAV_ENTAG GLGDQLNYSKNFVSAMRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-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--QENEDENARIfGERiANkV--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

27. 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, ;2000

32. Matrix extension – T COFFEE2 1 3 1 4 1 3 2 4 2 4 3

33. 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. T-Coffee Using different sources of alignment information Clustal
Structure alignments
Dialign
Lalign
Manual
T-Coffee

35. 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

37. T-Coffee
Other sequences
Direct alignment

38. Search matrix extension

39. 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

43. Summary Profile pre-processing (global/local)
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,
Notredame, C., Higgins, D.G., Heringa, J. (2000) T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol., 302,
Heringa, J. (2002) Local weighting schemes for protein multiple sequence alignment. Comput. Chem., 26(5),

45. Where to find this…. http://www.ibivu.cs.vu.nl/teaching