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Introduction to bioinformatics lecture 9

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1 Introduction to bioinformatics lecture 9
Multiple sequence alignment (II) In this lecture we will start with the first part of the “multiple sequence alignment chapter”: amino acid substitution matrices.

2 Scoring a profile position
D . Y A C D . Y At each position (column) we have different residue frequencies for each amino acid (rows) SO: Instead of saying S=M(aa1, aa2) (one residue pair) For frequency f>0 (amino acid is actually there) we take:

3 Progressive alignment
Perform pair-wise alignments of all of the sequences; Use the alignment scores to produces a dendrogram using neighbour-joining methods (guide-tree); Align the sequences sequentially, guided by the relationships indicated by the tree. Biopat (first method ever) MULTAL (Taylor 1987) DIALIGN (1&2, Morgenstern 1996) PRRP (Gotoh 1996) ClustalW (Thompson et al 1994) PRALINE (Heringa 1999) T Coffee (Notredame 2000) POA (Lee 2002) MUSCLE (Edgar 2004)

4 Progressive multiple alignment
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

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

6 PRALINE progressive strategy
d 1 3 1 3 2 1 3 2 PRALINE is a global progressive alignment algorithm that re-evaluates at each alignment step which sequences or blocks of sequences should be aligned, and hence determines the order in which sequences should be aligned on the fly. Second, by creating pre-profiles, distant sequences are no longer considered independently at the last alignment step. 5 4 1 3 2 5 4

7 “Once a gap, always a gap”
There are problems … Accuracy is very important !!!! Alignment errors during the construction of the MSA cannot be repaired anymore: propagated into the progressive steps. The comparisons of sequences at early steps during progressive alignments cannot make use of information from other sequences. It is only later during the alignment progression that more information from other sequences (e.g. through profile representation) becomes employed in the alignment steps. MAIN PROBLMES: 1) The progressive alignment protocol suffers from its greediness and is not able to revise any of the alignments made earlier, so that any alignment errors during the construction of the MSA cannot be repaired anymore ) The comparisons of sequences at early steps during progressive alignments cannot make use of information from other sequences, so that proper positional information required for correct matching is not available at early stages ) It is only later during the alignment progression that more information from other sequences (e.g. through profile representation) becomes employed in the alignment steps, but quite possibly after misalignment has already taken place. “Once a gap, always a gap” Feng & Doolittle, 1987

8 Additional strategies for multiple sequence alignment
Profile pre-processing Secondary structure-induced alignment Globalised local alignment Matrix extension Objective: try to avoid (early) errors

9 Profile pre-processing
1 Score 1-2 2 1 Score 1-3 3 4 5 Score 4-5 1 Key Sequence 2 1 3 Pre-alignment 4 5 Master-slave (N-to-1) alignment A C D . Y 1 Pre-profile Pi Px

10 Pre-profile generation
1 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

11 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

12 Pre-profile alignment
1 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

13 Pre-profile alignment Alignment consistency
Ala131 1 1 1 2 3 A131 L133 C126 4 5 2 2 1 2 3 4 5 3 3 1 2 4 5 4 4 1 2 5 3 5 5 5 1 2 3 4

14 PRALINE pre-profile generation
Idea: use the information from all query sequences to make a pre-profile for each query sequence that contains information from other sequences You can use all sequences in each pre-profile, or use only those sequences that will probably align ‘correctly’. Incorrectly aligned sequences in the pre-profiles will increase the noise level. Select using alignment score: only allow sequences in pre-profiles if their alignment with the score higher than a given threshold value. In PRALINE, this threshold is given as prepro=1500 (alignment score threshold value is 1500 – see next two slides)

15 Flavodoxin-cheY consistency scores (PRALINE prepro=0)
1fx TEYTAETIARQL VL999ST AQGRKVACF FLAV_DESVH TEYTAETIAREL VL999ST AQGRKVACF FLAV_DESDE YDAVL999SAW GRKVAAF FLAV_DESGI TEGVAEAIAKTL DVVL999ST FLAV_DESSA STW 4fxn FLAV_MEGEL 2fcr TEVADFIGK DLLF FLAV_ANASP LFYGTQTGKTESVAEIIR FLAV_ECOLI GSDTGNTENIAKMIQ FLAV_AZOVI IGLFFGSNTGKTRKVAKSIK FLAV_ENTAG FLAV_CLOAB ILYSSKTGKTERVAK 3chy Avrg Consist Conservation 1fx G FLAV_DESVH G FLAV_DESDE A FLAV_DESGI FLAV_DESSA 4fxn FLAV_MEGEL 2fcr FLAV_ANASP FLAV_ECOLI FLAV_AZOVI FLAV_ENTAG FLAV_CLOAB 3chy Avrg Consist Conservation * Iteration 0 SP= AvSP= SId= AvSId= 0.297 Consistency values are scored from 0 to 10; the value 10 is represented by the corresponding amino acid (red)

16 Flavodoxin-cheY consistency scores
(PRALINE prepro=1500) 1fx IVYGSTTGNTEYTAETIARQL DLVLLGCSTW AQGRKVACF FLAV_DESVH IVYGSTTGNTEYTAETIAREL DLVLLGCSTW AQGRKVACF FLAV_DESSA IVYGSTTGNTET YDIVLFGCSTW SL98ADLKGKKVSVF FLAV_DESGI IVYGSTTGNTEGVA DVVLLGCSTW KKVGVF FLAV_DESDE IVFGSSTGNTE YDAVLFGCSAW GRKVAAF 4fxn IVYWSGTGNTE NI DILILGCSA ISGKKVALF FLAV_MEGEL IVYWSGTGNTEAMA DVILLGCPAMGSE GKKVGLF 2fcr IFFSTSTGNTTEVA YDLLFLGAPT DKLPEVDMKDLPVAIF FLAV_ANASP LFYGTQTGKTESVAEII YQYLIIGCPTW W GKLVAYF FLAV_AZOVI LFFGSNTGKTRKVAKSIK YQFLILGTPTLGEG KTVALF FLAV_ENTAG IGIFFGSDTGQTRKVAKLIHQKL DVRRATR88888SYPVLLLGTPT WQEF8-8NTLSEADLTGKTVALF FLAV_ECOLI IFFGSDTGNTENIAKMI YDILLLGIPT KLVALF FLAV_CLOAB ILYSSKTGKTERVAKLIE LQESEGIIFGTPTY SWE GKLGAAF 3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFNNVEEAEDGVDALNKLQ-AGGYGFVI---SDWNMPNM DGLEL--LKTIRADGAMSALPVLM Avrg Consist Conservation fx G FLAV_DESVH G FLAV_DESSA G FLAV_DESGI G GATLV FLAV_DESDE AS fxn GS FLAV_MEGEL G MD--AWKQRTEDTGATVI fcr GLGDA5-8Y5DNFC FLAV_ANASP GTGDQ5-GY EEKISQRGG FLAV_AZOVI GLGDQ FLAV_ENTAG GLGDQL-NYSKNFVSA-MR--ILYDLVIARGACVVG8888EGYKFSFSAA6664NEFVGLPLDQEN88888EERIDSWLE FLAV_ECOLI GC FLAV_CLOAB STANS EDENARIFGERIANKVKQI chy VTAEA---KKENIIAA AQAGAS GYVVK-----PFTAATLEEKLNKIFEKLGM Avrg Consist Conservation * Iteration 0 SP= AvSP= SId= AvSId= 0.308 Consistency values are scored from 0 to 10; the value 10 is represented by the corresponding amino acid (red)

17 Strategies for multiple sequence alignment
Profile pre-processing Secondary structure-induced alignment Globalised local alignment Matrix extension Objective: integrate secondary structure information to anchor alignments and avoid errors

18 Protein structure hierarchical levels
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) QUATERNARY STRUCTURE (oligomers) TERTIARY STRUCTURE (fold)

19 Why use (predicted) structural information
“Structure more conserved than sequence” Many structural protein families (e.g. globins) have family members with very low sequence similarities. For example, globin sequences identities can be as low as 10% while still having an identical fold. This means that you can still observe equivalent secondary structures in homologous proteins even if sequence similarities are extremely low. But you are dependent on the quality of prediction methods. For example, secondary structure prediction is currently at 76% correctness. So, 1 out of 4 predicted amino acids is still incorrect.

20 Two superposed protein structures with two well-superposed helices
Red: well superposed Blue: low match quality C5 anaphylatoxin -- human (PDB code 1kjs) and pig (1c5a)) proteins are superposed

21 How to combine ss and aa info
Dynamic programming search matrix Amino acid substitution matrices MDAGSTVILCFV HHHCCCEEEEEE M D A S T I L C G H C E H H C C E E Default

22 In terms of scoring… So how would you score a profile using this extra information? Same formula as in lecture 6, but you can use sec. struct. specific substitution scores in various combinations. Where does it fit in? Very important: structure is always more conserved than sequence so it can help with the insertion(or not) of gaps.

23 Sequences to be aligned
Predict secondary structure HHHHCCEEECCCEEECCHH HHHCCCCEECCCEEHHH HHHHHHHHHHHHHCCCEEEE CCCCCCEECCCEEEECCHH HHHHHCCEEEECCCEECCC Secondary structure Align sequences using secondary structure Multiple alignment

24 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 Objectives: Instead of single amino acid positions, focus on local alignments Consider best local alignment through each cell in DP matrix 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 Strategies for multiple sequence alignment
Profile pre-processing Secondary structure-induced alignment Globalised local alignment Matrix extension Objective: try to avoid (early) errors

28 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

29 Matrix extension T-Coffee
Tree-based Consistency Objective Function For alignmEnt Evaluation Cedric Notredame Des Higgins Jaap Heringa J. Mol. Biol., 302, ;2000

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

31 Search matrix extension – alignment transitivity

32 T-Coffee Other sequences Direct alignment

33 Search matrix extension

34 but..... T-COFFEE (V1.23) multiple sequence alignment Flavodoxin-cheY
1fx PKALIVYGSTTGNTEYTAETIARQLANAG-YEVDSRDAASVE-AGGLFEGFDLVLLGCSTWGDDSIE------LQDDFIPL-FDSLEETGAQGRK----- FLAV_DESVH MPKALIVYGSTTGNTEYTAETIARELADAG-YEVDSRDAASVE-AGGLFEGFDLVLLGCSTWGDDSIE------LQDDFIPL-FDSLEETGAQGRK----- FLAV_DESGI MPKALIVYGSTTGNTEGVAEAIAKTLNSEG-METTVVNVADVT-APGLAEGYDVVLLGCSTWGDDEIE------LQEDFVPL-YEDLDRAGLKDKK----- FLAV_DESSA MSKSLIVYGSTTGNTETAAEYVAEAFENKE-IDVELKNVTDVS-VADLGNGYDIVLFGCSTWGEEEIE------LQDDFIPL-YDSLENADLKGKK----- FLAV_DESDE MSKVLIVFGSSTGNTESIAQKLEELIAAGG-HEVTLLNAADAS-AENLADGYDAVLFGCSAWGMEDLE------MQDDFLSL-FEEFNRFGLAGRK----- 4fxn MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDVN-IDELL-NEDILILGCSAMGDEVLE ESEFEPF-IEEIS-TKISGKK----- FLAV_MEGEL MVEIVYWSGTGNTEAMANEIEAAVKAAG-ADVESVRFEDTN-VDDVA-SKDVILLGCPAMGSEELE DSVVEPF-FTDLA-PKLKGKK----- FLAV_CLOAB MKISILYSSKTGKTERVAKLIEEGVKRSGNIEVKTMNLDAVD-KKFLQ-ESEGIIFGTPTYYAN ISWEMKKW-IDESSEFNLEGKL----- 2fcr KIGIFFSTSTGNTTEVADFIGKTLGAKA---DAPIDVDDVTDPQAL-KDYDLLFLGAPTWNTGA----DTERSGTSWDEFLYDKLPEVDMKDLP----- FLAV_ENTAG MATIGIFFGSDTGQTRKVAKLIHQKLDGIA---DAPLDVRRAT-REQF-LSYPVLLLGTPTLGDGELPGVEAGSQYDSWQEF-TNTLSEADLTGKT----- FLAV_ANASP SKKIGLFYGTQTGKTESVAEIIRDEFGNDV---VTLHDVSQAE-VTDL-NDYQYLIIGCPTWNIGEL QSDWEGL-YSELDDVDFNGKL----- FLAV_AZOVI AKIGLFFGSNTGKTRKVAKSIKKRFDDET-M-SDALNVNRVS-AEDF-AQYQFLILGTPTLGEGELPGLSSDCENESWEEF-LPKIEGLDFSGKT----- FLAV_ECOLI AITGIFFGSDTGNTENIAKMIQKQLGKDV---ADVHDIAKSS-KEDL-EAYDILLLGIPTWYYGEA QCDWDDF-FPTLEEIDFNGKL----- 3chy ADKELKFLVVD--DFSTMRRIVRNLLKELGFN-NVE-EAEDGVDALNKLQ-AGGYGFVISDWNMPNMDGLE LLKTIRADGAMSALPVLMV : : :: 1fx VACFGCGDSS--YEYFCGA-VDAIEEKLKNLGAEIVQDG LRIDGDPRAA--RDDIVGWAHDVRGAI FLAV_DESVH VACFGCGDSS--YEYFCGA-VDAIEEKLKNLGAEIVQDG LRIDGDPRAA--RDDIVGWAHDVRGAI FLAV_DESGI VGVFGCGDSS--YTYFCGA-VDVIEKKAEELGATLVASS LKIDGEPDSA----EVLDWAREVLARV FLAV_DESSA VSVFGCGDSD--YTYFCGA-VDAIEEKLEKMGAVVIGDS LKIDGDPE----RDEIVSWGSGIADKI FLAV_DESDE VAAFASGDQE--YEHFCGA-VPAIEERAKELGATIIAEG LKMEGDASND--PEAVASFAEDVLKQL 4fxn VALFGS------YGWGDGKWMRDFEERMNGYGCVVVETP LIVQNEPD--EAEQDCIEFGKKIANI FLAV_MEGEL VGLFGS------YGWGSGEWMDAWKQRTEDTGATVIGTA IV--NEMP--DNAPECKELGEAAAKA FLAV_CLOAB GAAFSTANSI--AGGSDIA-LLTILNHLMVKGMLVY----SGGVAFGKPKTHLGYVHINEIQENEDENARIFGERIANKVKQIF 2fcr VAIFGLGDAEGYPDNFCDA-IEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVRDG-KFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------ FLAV_ENTAG VALFGLGDQLNYSKNFVSA-MRILYDLVIARGACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSWLEKLKPAVL FLAV_ANASP VAYFGTGDQIGYADNFQDA-IGILEEKISQRGGKTVGYWSTDGYDFNDSKALRNG-KFVGLALDEDNQSDLTDDRIKSWVAQLKSEFGL------ FLAV_AZOVI VALFGLGDQVGYPENYLDA-LGELYSFFKDRGAKIVGSWSTDGYEFESSEAVVDG-KFVGLALDLDNQSGKTDERVAAWLAQIAPEFGLSL---- FLAV_ECOLI VALFGCGDQEDYAEYFCDA-LGTIRDIIEPRGATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKWVKQISEELHLDEILNA 3chy TAEAKKENIIAAAQAGASGYVVKPFT---AATLEEKLNKIFEKLGM .

35 Multiple alignment methods
Multi-dimensional dynamic programming > extension of pairwise sequence alignment. Progressive alignment > incorporates phylogenetic information to guide the alignment process Iterative alignment > correct for problems with progressive alignment by repeatedly realigning subgroups of sequence

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