Sequence analysis courseSequence analysis course

Lecture 7Multiple sequence alignment 3 of 3

Optimizing progressive multiple alignment methods

Pair-wise alignment quality Pair-wise alignment quality versusversus sequence identity sequence identity

(Vogt et al., JMB 249, 816-831,1995)(Vogt et al., JMB 249, 816-831,1995)

• Heuristics

• Profile pre-processing

• Secondary structure-guided alignment

• Globalised local alignment

• Matrix extension

Objective: try to avoid (early) errors

Strategies for progressive Strategies for progressive alignment optimisationalignment optimisation

Clustal, ClustalW, ClustalXClustal, ClustalW, ClustalX• Neighbour Joining (NJ) algorithm (Saitou and Nei, 1984) to

construct guide tree.• Sequence blocks are represented by profiles• 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)

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

PRIMARY STRUCTURE (amino acid sequence)

QUATERNARY STRUCTURE (oligomers)

SECONDARY STRUCTURE (helices, strands)

TERTIARY STRUCTURE (fold)

Protein structure hierarchical levelsProtein structure hierarchical levels

Flavodoxin fold: (Helix-beta-helix)Flavodoxin fold: (Helix-beta-helix)

Flavodoxin-cheY NJ treeFlavodoxin-cheY NJ tree

1fx1 -PKALIVYGSTTGNTEYTAETIARQLANAG-Y-EVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSIE------LQDDFIPLFD-SLEETGAQGRKFLAV_DESVH MPKALIVYGSTTGNTEYTAETIARELADAG-Y-EVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSIE------LQDDFIPLFD-SLEETGAQGRKFLAV_DESGI MPKALIVYGSTTGNTEGVAEAIAKTLNSEG-M-ETTVVNVADVTAPGLAEGYDVVLLGCSTWGDDEIE------LQEDFVPLYE-DLDRAGLKDKKFLAV_DESSA MSKSLIVYGSTTGNTETAAEYVAEAFENKE-I-DVELKNVTDVSVADLGNGYDIVLFGCSTWGEEEIE------LQDDFIPLYD-SLENADLKGKKFLAV_DESDE MSKVLIVFGSSTGNTESIAQKLEELIAAGG-H-EVTLLNAADASAENLADGYDAVLFGCSAWGMEDLE------MQDDFLSLFE-EFNRFGLAGRKFLAV_CLOAB -MKISILYSSKTGKTERVAKLIEEGVKRSGNI-EVKTMNLDAVDKKFLQE-SEGIIFGTPTYYAN---------ISWEMKKWID-ESSEFNLEGKLFLAV_MEGEL --MVEIVYWSGTGNTEAMANEIEAAVKAAG-A-DVESVRFEDTNVDDVAS-KDVILLGCPAMGSE--E------LEDSVVEPFF-TDLAPKLKGKK4fxn ---MKIVYWSGTGNTEKMAELIAKGIIESG-K-DVNTINVSDVNIDELLN-EDILILGCSAMGDE--V------LEESEFEPFI-EEISTKISGKKFLAV_ANASP SKKIGLFYGTQTGKTESVAEIIRDEFGNDVVT----LHDVSQAEVTDLND-YQYLIIGCPTWNIGELQ---SD-----WEGLYS-ELDDVDFNGKLFLAV_AZOVI -AKIGLFFGSNTGKTRKVAKSIKKRFDDETMSD---ALNVNRVSAEDFAQ-YQFLILGTPTLGEGELPGLSSDCENESWEEFLP-KIEGLDFSGKT2fcr --KIGIFFSTSTGNTTEVADFIGKTLGAKADAP---IDVDDVTDPQALKD-YDLLFLGAPTWNTGADTERSGT----SWDEFLYDKLPEVDMKDLPFLAV_ENTAG MATIGIFFGSDTGQTRKVAKLIHQKLDGIADAP---LDVRRATREQFLS--YPVLLLGTPTLGDGELPGVEAGSQYDSWQEFTN-TLSEADLTGKTFLAV_ECOLI -AITGIFFGSDTGNTENIAKMIQKQLGKDVAD----VHDIAKSSKEDLEA-YDILLLGIPTWYYGEAQ-CD-------WDDFFP-TLEEIDFNGKL3chy --ADKELKFLVVDDFSTMRRIVRNLLKELG----FNNVEEAEDGVDALN------KLQAGGYGFV--I------SDWNMPNMDG-LELLKTIR--- . ... : . . :

1fx1 VACFGCGDSSYEYF--CGAVDAIEEKLKNLGAEIVQDG----------------LRIDGDPRAARDDIVGWAHDVRGAI---------------FLAV_DESVH VACFGCGDSSYEYF--CGAVDAIEEKLKNLGAEIVQDG----------------LRIDGDPRAARDDIVGWAHDVRGAI---------------FLAV_DESGI VGVFGCGDSSYTYF--CGAVDVIEKKAEELGATLVASS----------------LKIDGEPDSAE--VLDWAREVLARV---------------FLAV_DESSA VSVFGCGDSDYTYF--CGAVDAIEEKLEKMGAVVIGDS----------------LKIDGDPERDE--IVSWGSGIADKI---------------FLAV_DESDE VAAFASGDQEYEHF--CGAVPAIEERAKELGATIIAEG----------------LKMEGDASNDPEAVASFAEDVLKQL---------------FLAV_CLOAB GAAFSTANSIAGGS--DIALLTILNHLMVKGMLVYSGGVA----FGKPKTHLGYVHINEIQENEDENARIFGERIANKVKQIF-----------FLAV_MEGEL VGLFGSYGWGSGE-----WMDAWKQRTEDTGATVIGTA----------------IVN-EMPDNAPECKE-LGEAAAKA----------------4fxn VALFGSYGWGDGK-----WMRDFEERMNGYGCVVVETP----------------LIVQNEPDEAEQDCIEFGKKIANI----------------FLAV_ANASP VAYFGTGDQIGYADNFQDAIGILEEKISQRGGKTVGYWSTDGYDFNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSWVAQLKSEFGL------FLAV_AZOVI VALFGLGDQVGYPENYLDALGELYSFFKDRGAKIVGSWSTDGYEFESSEAVV-DGKFVGLALDLDNQSGKTDERVAAWLAQIAPEFGLSL----2fcr VAIFGLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVR-DGKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------FLAV_ENTAG VALFGLGDQLNYSKNFVSAMRILYDLVIARGACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSWLEKLKPAVL-------FLAV_ECOLI VALFGCGDQEDYAEYFCDALGTIRDIIEPRGATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKWVKQISEELHLDEILNA3chy AD--GAMSALPVL-----MVTAEAKKENIIAAAQAGAS----------------GYV-VKPFTAATLEEKLNKIFEKLGM-------------- . . : . .

ClustalW Flavodoxin-cheYClustalW Flavodoxin-cheY

Pre-profile generationPre-profile generation1213

45

Score 1-2

Score 1-3

Score 4-5

ACD..Y

12345

1ACD..Y

21345

2

Pre-profilesPre-alignments

512354

ACD..Y

Cut-off

Flavodoxin-cheY: Pre-processing Flavodoxin-cheY: Pre-processing (cut-off(cut-off1500)1500)

1fx1 -PKALIVYGSTTGNT-EYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACF

FLAV_DESDE MSKVLIVFGSSTGNT-ESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLF-EEFNRFGLAGRKVAAf

FLAV_DESVH MPKALIVYGSTTGNT-EYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACf

FLAV_DESSA MSKSLIVYGSTTGNT-ETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLY-DSLENADLKGKKVSVf

FLAV_DESGI MPKALIVYGSTTGNT-EGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLY-EDLDRAGLKDKKVGVf

2fcr --KIGIFFSTSTGNT-TEVADFIGKTLGA---KADAPIDVDDVTDPQALKDYDLLFLGAPTWNTG----ADTERSGTSWDEFLYDKLPEVDMKDLPVAIF

FLAV_AZOVI -AKIGLFFGSNTGKT-RKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFL-PKIEGLDFSGKTVALf

FLAV_ENTAG MATIGIFFGSDTGQT-RKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFT-NTLSEADLTGKTVALf

FLAV_ANASP SKKIGLFYGTQTGKT-ESVaEIIRDEFGN---DVVTLHDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLY-SELDDVDFNGKLVAYf

FLAV_ECOLI -AITGIFFGSDTGNT-ENIaKMIQKQLGK---DVADVHDIAKSS-KEDLEAYDILLLgIPTWYYGE--------AQCDWDDFF-PTLEEIDFNGKLVALf

4fxn -MK--IVYWSGTGNT-EKMAELIAKGIIESG-KDVNTINVSDVNIDELL-NEDILILGCSAMGDEVL-------EESEFEPFI-EEIS-TKISGKKVALF

FLAV_MEGEL MVE--IVYWSGTGNT-EAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVA-SKDVILLgCPAMGSEEL-------EDSVVEPFF-TDLA-PKLKGKKVGLf

FLAV_CLOAB -MKISILYSSKTGKT-ERVaKLIEEGVKRSGNIEVKTMNLDAVD-KKFLQESEGIIFgTPTYYAN---------ISWEMKKWI-DESSEFNLEGKLGAAf

3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFN--NVEEAEDGVDALNKLQAGGYGFVI---SDWNMPNM----------DGLELL-KTIRADGAMSALPVLM

T

1fx1 GCGDS-SY-EYFCGA-VDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI--------

FLAV_DESDE ASGDQ-EY-EHFCGA-VPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL--------

FLAV_DESVH GCGDS-SY-EYFCGA-VDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI--------

FLAV_DESSA GCGDS-DY-TYFCGA-VDAIEEKLEKMgAVVIGD---------------------SLKIDGD--PE--RDEIVSwGSGIADKI--------

FLAV_DESGI GCGDS-SY-TYFCGA-VDVIEKKAEELgATLVAS---------------------SLKIDGE--PD--SAEVLDwAREVLARV--------

2fcr GLGDAEGYPDNFCDA-IEEIHDCFAKQGAKPVGFSNPDDYDYEESKS-VRDGKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------

FLAV_AZOVI GLGDQVGYPENYLDA-LGELYSFFKDRgAKIVGSWSTDGYEFESSEA-VVDGKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L--

FLAV_ENTAG GLGDQLNYSKNFVSA-MRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L------

FLAV_ANASP GTGDQIGYADNFQDA-IGILEEKISQRgGKTVGYWSTDGYDFNDSKA-LRNGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------

FLAV_ECOLI GCGDQEDYAEYFCDA-LGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA

4fxn G-----SY-GWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI---------

FLAV_MEGEL G-----SY-GWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNA-PECKElGEAAAKA---------

FLAV_CLOAB STANSIAGGSDIA---LLTILNHLMVKgMLVYSG----GVAFGKPKTHLGYVHINEIQENEDENARIfGERiANkVKQIF-----------

3chy VTAEAKK--ENIIAA---------AQAGAS-------------------------GYVV-----KPFTAATLEEKLNKIFEKLGM------

G

Using structural infoUsing structural informationrmation

• 10 years SS prediction method development: Accuracy ± 5%

• 10 years MSA method development: Accuracy can be ± 40%

• Amino acid patterns• Secondary structure patterns• Super-secondary structure patterns• Alternate matrices with associated gap

penalties according to region

How How to combine ss and aa infoto combine ss and aa info

Dynamic programmingsearch matrix

Amino acid substitution

matricesMDAGSTVILCFVHHHCCCEEEEEE

MDAASTILCGS

HHHHCCEEECC

C

H

E

H C

E Default

In terms of scoring…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.

Sequences to be aligned

Predict secondary structure

HHHHCCEEECCCEEECCHH

HHHCCCCEECCCEEHHH

HHHHHHHHHHHHHCCCEEEE

CCCCCCEECCCEEEECCHH

HHHHHCCEEEECCCEECCC

Align sequences using secondary structure

Secondary structure

Multiple alignment

Using predicted secondary structureUsing predicted secondary structure1fx1 -PK-ALIVYGSTTGNTEYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACF e eeee b ssshhhhhhhhhhhhhhttt eeeee stt tttttt seeee b ee sss ee ttthhhhtt ttss tt eeeeeFLAV_DESVH MPK-ALIVYGSTTGNTEYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACf e eeeeee hhhhhhhhhhhhhhh eeeeee eeeeee hhhhhh eeeeeFLAV_DESGI MPK-ALIVYGSTTGNTEGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLYED-LDRAGLKDKKVGVf e eeeeee hhhhhhhhhhhhhh eeeeee hhhhhh eeeeeee hhhhhh eeeeeeFLAV_DESSA MSK-SLIVYGSTTGNTETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLYDS-LENADLKGKKVSVf eeeeee hhhhhhhhhhhhhh eeeee eeeee hhhhhhh h eeeeeFLAV_DESDE MSK-VLIVFGSSTGNTESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLFEE-FNRFGLAGRKVAAf eeee hhhhhhhhhhhhhh eeeee hhhhhhhhhhheeeee hhhhhhh hh eeeee2fcr --K-IGIFFSTSTGNTTEVADFIGKTLGAK---ADAPIDVDDVTDPQALKDYDLLFLGAPTWNTGAD----TERSGTSWDEFLYDKLPEVDMKDLPVAIF eeeee ssshhhhhhhhhhhhhggg b eeggg s gggggg seeeeeee stt s s s sthhhhhhhtggg tt eeeeeFLAV_ANASP SKK-IGLFYGTQTGKTESVaEIIRDEFGND--VVTL-HDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLYSE-LDDVDFNGKLVAYf eeeee hhhhhhhhhhhh eee hhh hhhhhhheeeeee hhhhhhhhh eeeeeeFLAV_ECOLI -AI-TGIFFGSDTGNTENIaKMIQKQLGKD--VADV-HDIAKSS-KEDLEAYDILLLgIPTWYYGEA--------QCDWDDFFPT-LEEIDFNGKLVALf eee hhhhhhhhhhhh eee hhh hhhhhhheeeee hhhhh eeeeeeFLAV_AZOVI -AK-IGLFFGSNTGKTRKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFLPK-IEGLDFSGKTVALf eee hhhhhhhhhhhhh hhh hhhhhhheeeee hhhhhhhhh eeeeeeFLAV_ENTAG MAT-IGIFFGSDTGQTRKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFTNT-LSEADLTGKTVALf eeee hhhhhhhhhhhh hhh hhhhhhheeeee hhhhh eeeee4fxn ----MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDVNIDELLNE-DILILGCSAMGDEVL------E-ESEFEPFIEE-IST-KISGKKVALF eeeee ssshhhhhhhhhhhhhhhtt eeeettt sttttt seeeeee btttb ttthhhhhhh hst t tt eeeeeFLAV_MEGEL M---VEIVYWSGTGNTEAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVASK-DVILLgCPAMGSEEL------E-DSVVEPFFTD-LAP-KLKGKKVGLf hhhhhhhhhhhhhh eeeee hhhhhhhh eeeee eeeeeFLAV_CLOAB M-K-ISILYSSKTGKTERVaKLIEEGVKRSGNIEVKTMNL-DAVDKKFLQESEGIIFgTPTY-YANI--------SWEMKKWIDE-SSEFNLEGKLGAAf eee hhhhhhhhhhhhhh eeeeee hhhhhhhhhh eeee hhhhhhhhh eeeee3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFNN-VEEAEDGV-DALNKLQAGGYGFVISD---WNMPNM----------DGLELLKTIRADGAMSALPVLMV 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 gggghhhhhhhhhhhhhhFLAV_DESVH GCGDS-SY-EYFCGAVDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI-------- eee hhhhhhhhhhhh eeeee eeeee hhhhhhhhhhhhhhFLAV_DESGI GCGDS-SY-TYFCGAVDVIEKKAEELgATLVAS---------------------SLKIDGE--P--DSAEVLDwAREVLARV-------- eee hhhhhhhhhhhh eeeee hhhhhhhhhhhFLAV_DESSA GCGDS-DY-TYFCGAVDAIEEKLEKMgAVVIGD---------------------SLKIDGD--P--ERDEIVSwGSGIADKI-------- hhhhhhhhhhhh eeeee e eeeFLAV_DESDE ASGDQ-EY-EHFCGAVPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL-------- e hhhhhhhhhhhhhh eeeee ee hhhhhhhhhhh2fcr GLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVRD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------ eee ttt ttsttthhhhhhhhhhhtt eee b gggs s tteet teesseeeettt ss hhhhhhhhhhhhhhhhtFLAV_ANASP GTGDQIGYADNFQDAIGILEEKISQRgGKTVGYWSTDGYDFNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------ hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhhFLAV_ECOLI GCGDQEDYAEYFCDALGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhhhhFLAV_AZOVI GLGDQVGYPENYLDALGELYSFFKDRgAKIVGSWSTDGYEFESSEAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L-- e hhhhhhhhhhhhhh eeeee hhhhhhhhhhhFLAV_ENTAG GLGDQLNYSKNFVSAMRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L------ hhhhhhhhhhhhhhh eeee hhhhhhh hhhhhhhhhhhh4fxn G-----SYGWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI--------- e eesss shhhhhhhhhhhhtt ee s eeees ggghhhhhhhhhhhhtFLAV_MEGEL G-----SYGWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNAPE-CKElGEAAAKA--------- hhhhhhhhhhh eeeee eeee h hhhhhhhhFLAV_CLOAB STANSIA-GGSDIALLTILNHLMVK-gMLVYSG----GVAFGKPKTHLG-----YVHINEI--QENEDENARIfGERiANkV--KQIF-- hhhhhhhhhhhhhh eeeee hhhh hhh hhhhhhhhhhhh h3chy -----------TAEAKKENIIAAAQAGASGY-------------------------VVK----P-FTAATLEEKLNKIFEKLGM------ ess hhhhhhhhhtt see ees s hhhhhhhhhhhhhhht

G

TT--COFFEECOFFEE

• 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

Search matrix extensionSearch matrix extension

12

13

14

23

24

34

Search matrix extensionSearch matrix extension

Search matrix extensionSearch matrix extension

The T-coffee effectThe T-coffee effect

Direct alignment

Other sequences

Here you see that although a direst alignment might choose one path, using information from the other sequences (T-COFFEE) finds an alternate and usually better one

but.....but.....T-COFFEE (V1.23) multiple sequence alignmentFlavodoxin-cheY1fx1 ----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 :. . . : . :: 1fx1 ---------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-LGTIRDIIEPRGATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKWVKQISEELHLDEILNA3chy TAEAKKENIIAAAQAGASGYVVKPFT---AATLEEKLNKIFEKLGM----------------------------------------------------------

.

Multiple alignment methodsMultiple alignment methods

• Multi-dimensional dynamic programming• Progressive alignment• Iterative alignment

FROM HERE ON REFER TO THE FROM HERE ON REFER TO THE

PRALINE PAPER FOR HELPPRALINE PAPER FOR HELP

See further reading section onlineSee further reading section online

Iteration fatesIteration fates

Convergence

Limit cycle

Divergence

Iterative schemesIterative schemes

• Do an alignment• Learn from it• Do it better next time round

HOW????• Consistency iteration• Pre-profile update iteration• Improved secondary structure iteration

Consistency scoringConsistency scoring

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5

Ala131A131A131L133C126A131

5

Consistency iterationConsistency iteration

Pre-profilesPre-profiles

Multiple Multiple alignmentalignmentpositionalpositionalconsistencyconsistencyscoresscores

Pre-profile update iterationPre-profile update iteration

Pre-profilesPre-profiles

Multiple Multiple alignmentalignment

Is the Is the initial initial ss ss prediction good prediction good enough?enough?

3chy-AA SEQUENCE|| AA |ADKELKFLVVDDFSTMRRIVRNLLKELGFNNVEEAEDGVDALNKLQAGGYGFVISDWNMP|

3chy-AA SEQUENCE|| AA |NMDGLELLKTIRADGAMSALPVLMVTAEAKKENIIAAAQAGASGYVVKPFTAATLEEKLNKIFEKLGM|

3chy-ITERATION-0|| PHD | EEEEEEE HHHHHHHHHHHHHHHHH E HHHHHHHHHH HHHEEE |

3chy-ITERATION-0|| PHD | HHHHHHEEEEEE HHHHHHHHHHHHHHHHH HHHHHHHHHHHHHH |

3chy-ITERATION-1|| PHD | EEEEEEEE HHHHHHHHHHHHHHH HHHHHHHH EEEEEE |

3chy-ITERATION-1|| PHD | HHHHHHEEEEEE HHH HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-2|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHHH EEEEEE |

3chy-ITERATION-2|| PHD | HHHHHHEEEEEE HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-3|| PHD | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE |

3chy-ITERATION-3|| PHD | HHHHHHHHHHHH HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-4|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHH EEEEE |

3chy-ITERATION-4|| PHD | HHHHH EEEEE HHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-5|| PHD | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE |

3chy-ITERATION-5|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-6|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHH EEEEEE |

3chy-ITERATION-6|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH |

3chy-ITERATION-7|| PHD | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE |

3chy-ITERATION-7|| PHD | HHHHHHHH EEEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-8|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHH EEEEEE |

3chy-ITERATION-8|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-9|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHHHH EEEEE |

3chy-ITERATION-9|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH |

Sequences to be aligned

Align using secondary structure

Predict secondary structure

HHHHCCEEECCCEEECCHH

HHHCCCCEECCCEEHHH

HHHHHHHHHHHHHCCCEEEE

CCCCCCEECCCEEEECCHH

HHHHHCCEEEECCCEECCCSecondary structure

Multiple alignment

HHHHHCCEEEECCCEECCC

Single Sequence MA-based

Muscle MSA methodMuscle MSA method

PRALINE and Muscle MSA PRALINE and Muscle MSA methodmethod

PRALINE and MUSCLE use almost the same formalism to compare two profiles:

PRALINE uses

Scorexy = (1 – f xG) (1 – f y

G) i j f xi f y

j log pij/pi pj

The difference is the position of the log in the above equations: Edgar (Muscle creator) calls the Muscle scoring scheme “Log-expectation score (LE)”

So what do we do???So what do we do???

• A single shot for a good alignment without thinking: MUSCLE, T-Coffee (maybe POA)

• If you want to experiment with making alignments for a given sequence set: PRALINE– Profile pre-processing– Iteration– Secondary structure-induced alignment– Globalised local alignment

• There is no single method that always generates the best alignment

• Therefore best is to use more than one method: e.g. include Dialign2 (local)

SummarySummary• Weighting schemes simulating

simultaneous multiple alignment– Profile pre-processing (global/local)– Matrix extension (well balanced

scheme)

• Using additional information– secondary structure driven alignment

• Schemes strike balance between speed and sensitivity