Multiple sequence alignment Why?

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, PNAS86, 4412) • extremely slow and memory intensive • up to 8-9 sequences of ~250 residues DCA (Stoye et al., 1997, CABIOS13, 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 5×5 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

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., 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 1.0 0.5 Position dependent gap penalties

12. Profile-sequence alignment sequence profile ACD……VWY

13. Profile-profile alignment profile A C 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 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

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 alignment 1 2 1 3 4 5 2 2 1 3 4 Final alignment 5 3 1 1 3 2 2 4 3 5 4 5 4 4 1 2 3 5 5 1 5 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 SECONDARY STRUCTURE (helices, strands) PRIMARY STRUCTURE (amino acid sequence) VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH 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 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-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 eeeeeehhhhhhhhhhhhhhh eeeeeeeeeeeehhhhhh eeeee FLAV_DESGI MPK-ALIVYGSTTGNTEGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLYED-LDRAGLKDKKVGVf e eeeeeehhhhhhhhhhhhhheeeeeehhhhhh eeeeeeehhhhhh eeeeee FLAV_DESSA MSK-SLIVYGSTTGNTETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLYDS-LENADLKGKKVSVf eeeeeehhhhhhhhhhhhhheeeeeeeeeehhhhhhh heeeee FLAV_DESDE MSK-VLIVFGSSTGNTESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLFEE-FNRFGLAGRKVAAf eeeehhhhhhhhhhhhhheeeeehhhhhhhhhhheeeeehhhhhhh hheeeee 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 hhhhhhhhhhhheeehhhhhhhhhheeeeeehhhhhhhhheeeeee FLAV_ECOLI -AI-TGIFFGSDTGNTENIaKMIQKQLGKD--VADV-HDIAKSS-KEDLEAYDILLLgIPTWYYGEA--------QCDWDDFFPT-LEEIDFNGKLVALf eee hhhhhhhhhhhheee hhh hhhhhhheeeee hhhhheeeeee FLAV_AZOVI -AK-IGLFFGSNTGKTRKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFLPK-IEGLDFSGKTVALf eeehhhhhhhhhhhhhhhhhhhhhhheeeeehhhhhhhhheeeeee FLAV_ENTAG MAT-IGIFFGSDTGQTRKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFTNT-LSEADLTGKTVALf eeeehhhhhhhhhhhhhhhhhhhhhheeeee hhhhheeeee 4fxn ----MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDVNIDELLNE-DILILGCSAMGDEVL------E-ESEFEPFIEE-IST-KISGKKVALF eeeee ssshhhhhhhhhhhhhhhtt eeeettt sttttt seeeeee btttb ttthhhhhhhhst t tt eeeee FLAV_MEGEL M---VEIVYWSGTGNTEAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVASK-DVILLgCPAMGSEEL------E-DSVVEPFFTD-LAP-KLKGKKVGLf hhhhhhhhhhhhhheeeee hhhhhhhh eeeeeeeeee FLAV_CLOAB M-K-ISILYSSKTGKTERVaKLIEEGVKRSGNIEVKTMNL-DAVDKKFLQESEGIIFgTPTY-YANI--------SWEMKKWIDE-SSEFNLEGKLGAAf eeehhhhhhhhhhhhhh eeeeee hhhhhhhhhheeee hhhhhhhhheeeee 3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFNN-VEEAEDGV-DALNKLQAGGYGFVISD---WNMPNM----------DGLELLKTIRADGAMSALPVLMV tt eeee s hhhhhhhhhhhhhht eeeesshhhhhhhhhheeeee 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 hhhhhhhhhhhheeeeeeeeeehhhhhhhhhhhhhh FLAV_DESGI GCGDS-SY-TYFCGAVDVIEKKAEELgATLVAS---------------------SLKIDGE--P--DSAEVLDwAREVLARV-------- eee hhhhhhhhhhhheeeeehhhhhhhhhhh FLAV_DESSA GCGDS-DY-TYFCGAVDAIEEKLEKMgAVVIGD---------------------SLKIDGD--P--ERDEIVSwGSGIADKI-------- hhhhhhhhhhhh eeeeee eee FLAV_DESDE ASGDQ-EY-EHFCGAVPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL-------- e hhhhhhhhhhhhhheeeeeeehhhhhhhhhhh 2fcr GLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVRD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------ eee ttt ttsttthhhhhhhhhhhtt eee b gggs s tteet teesseeeettt ss hhhhhhhhhhhhhhhht FLAV_ANASP GTGDQIGYADNFQDAIGILEEKISQRgGKTVGYWSTDGYDFNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------ hhhhhhhhhhhhhheeeehhhhhhhhhhhhhhhh FLAV_ECOLI GCGDQEDYAEYFCDALGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA hhhhhhhhhhhhhheeeehhhhhhhhhhhhhhhhhh FLAV_AZOVI GLGDQVGYPENYLDALGELYSFFKDRgAKIVGSWSTDGYEFESSEAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L-- ehhhhhhhhhhhhhheeeee hhhhhhhhhhh FLAV_ENTAG GLGDQLNYSKNFVSAMRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L------ hhhhhhhhhhhhhhheeeehhhhhhh hhhhhhhhhhhh 4fxn G-----SYGWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI--------- e eesss shhhhhhhhhhhhtt ee s eeees ggghhhhhhhhhhhht FLAV_MEGEL G-----SYGWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNAPE-CKElGEAAAKA--------- hhhhhhhhhhheeeee eeeeh hhhhhhhh FLAV_CLOAB STANSIA-GGSDIALLTILNHLMVK-gMLVYSG----GVAFGKPKTHLG-----YVHINEI--QENEDENARIfGERiANkV--KQIF-- hhhhhhhhhhhhhheeeeehhhh hhhhhhhhhhhhhhh h 3chy -----------TAEAKKENIIAAAQAGASGY-------------------------VVK----P-FTAATLEEKLNKIFEKLGM------ ess hhhhhhhhhtt seeees 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 HeringaJ. Mol. Biol., 302, 205-217;2000

32. Matrix extension – T COFFEE 2 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. Using different sources of alignment information Structure alignments Clustal Clustal Dialign Lalign Manual T-Coffee

35. Search matrix extension

36. T-Coffee • Combine different alignment techniquesby 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 • 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.

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