1 / 30

Pair-wise alignment quality versus sequence identity (Vogt et al., JMB 249, 816-831,1995)

Pair-wise alignment quality versus sequence identity (Vogt et al., JMB 249, 816-831,1995). Strategies for progressive alignment optimisation. Heuristics Profile pre-processing Secondary structure- guided alignment Globalised local alignment Matrix extension

oral
Download Presentation

Pair-wise alignment quality versus sequence identity (Vogt et al., JMB 249, 816-831,1995)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Pair-wise alignment quality versus sequence identity(Vogt et al., JMB 249, 816-831,1995)

  2. Strategies for progressive alignment optimisation • Heuristics • Profile pre-processing • Secondary structure-guided alignment • Globalised local alignment • Matrix extension Objective: try to avoid (early) errors

  3. Clustal, 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)

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

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

  6. Flavodoxin-cheY NJ tree

  7. ClustalW Flavodoxin-cheY 1fx1 -PKALIVYGSTTGNTEYTAETIARQLANAG-Y-EVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSIE------LQDDFIPLFD-SLEETGAQGRK FLAV_DESVH MPKALIVYGSTTGNTEYTAETIARELADAG-Y-EVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSIE------LQDDFIPLFD-SLEETGAQGRK FLAV_DESGI MPKALIVYGSTTGNTEGVAEAIAKTLNSEG-M-ETTVVNVADVTAPGLAEGYDVVLLGCSTWGDDEIE------LQEDFVPLYE-DLDRAGLKDKK FLAV_DESSA MSKSLIVYGSTTGNTETAAEYVAEAFENKE-I-DVELKNVTDVSVADLGNGYDIVLFGCSTWGEEEIE------LQDDFIPLYD-SLENADLKGKK FLAV_DESDE MSKVLIVFGSSTGNTESIAQKLEELIAAGG-H-EVTLLNAADASAENLADGYDAVLFGCSAWGMEDLE------MQDDFLSLFE-EFNRFGLAGRK FLAV_CLOAB -MKISILYSSKTGKTERVAKLIEEGVKRSGNI-EVKTMNLDAVDKKFLQE-SEGIIFGTPTYYAN---------ISWEMKKWID-ESSEFNLEGKL FLAV_MEGEL --MVEIVYWSGTGNTEAMANEIEAAVKAAG-A-DVESVRFEDTNVDDVAS-KDVILLGCPAMGSE--E------LEDSVVEPFF-TDLAPKLKGKK 4fxn ---MKIVYWSGTGNTEKMAELIAKGIIESG-K-DVNTINVSDVNIDELLN-EDILILGCSAMGDE--V------LEESEFEPFI-EEISTKISGKK FLAV_ANASP SKKIGLFYGTQTGKTESVAEIIRDEFGNDVVT----LHDVSQAEVTDLND-YQYLIIGCPTWNIGELQ---SD-----WEGLYS-ELDDVDFNGKL FLAV_AZOVI -AKIGLFFGSNTGKTRKVAKSIKKRFDDETMSD---ALNVNRVSAEDFAQ-YQFLILGTPTLGEGELPGLSSDCENESWEEFLP-KIEGLDFSGKT 2fcr --KIGIFFSTSTGNTTEVADFIGKTLGAKADAP---IDVDDVTDPQALKD-YDLLFLGAPTWNTGADTERSGT----SWDEFLYDKLPEVDMKDLP FLAV_ENTAG MATIGIFFGSDTGQTRKVAKLIHQKLDGIADAP---LDVRRATREQFLS--YPVLLLGTPTLGDGELPGVEAGSQYDSWQEFTN-TLSEADLTGKT FLAV_ECOLI -AITGIFFGSDTGNTENIAKMIQKQLGKDVAD----VHDIAKSSKEDLEA-YDILLLGIPTWYYGEAQ-CD-------WDDFFP-TLEEIDFNGKL 3chy --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 VALFGCGDQEDYAEYFCDALGTIRDIIEPRGATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKWVKQISEELHLDEILNA 3chy AD--GAMSALPVL-----MVTAEAKKENIIAAAQAGAS----------------GYV-VKPFTAATLEEKLNKIFEKLGM-------------- . . : . .

  8. 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

  9. Flavodoxin-cheY: Pre-processing (cut-off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

  10. Using structural information • 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

  11. How to combine ss and aa info Amino acid substitution matrices Dynamic programming search matrix 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

  12. 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.

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

  14. Using 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 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 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-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

  15. 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

  16. Search matrix extension 2 1 3 1 4 1 3 2 4 2 4 3

  17. Search matrix extension

  18. Search matrix extension

  19. The T-coffee effect Other sequences Direct alignment 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

  20. but..... T-COFFEE (V1.23) multiple sequence alignment Flavodoxin-cheY 1fx1 ----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-LGTIRDIIEPRGATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKWVKQISEELHLDEILNA 3chy TAEAKKENIIAAAQAGASGYVVKPFT---AATLEEKLNKIFEKLGM---------------------------------------------------------- .

  21. Multiple alignment methods • Multi-dimensional dynamic programming • Progressive alignment • Iterative alignment FROM HERE ON REFER TO THE PRALINE PAPER FOR HELP See further reading section online

  22. Iteration fates Convergence Limit cycle Divergence

  23. Iterative schemes • Do an alignment • Learn from it • Do it better next time round HOW???? • Consistency iteration • Pre-profile update iteration • Improved secondary structure iteration

  24. Consistency scoring Ala131 1 1 2 1 3 A131 A131 L133 C126 A131 4 5 2 1 2 2 3 4 5 3 1 3 2 4 5 4 1 4 5 2 3 5 5 5 1 2 3 4

  25. Consistency iteration Pre-profiles Multiple alignment positional consistency scores

  26. Pre-profile update iteration Pre-profiles Multiple alignment

  27. 3chy-ITERATION-0|| PHD | EEEEEEE HHHHHHHHHHHHHHHHH E HHHHHHHHHH HHHEEE | 3chy-ITERATION-1|| PHD | EEEEEEEE HHHHHHHHHHHHHHH HHHHHHHH EEEEEE | 3chy-ITERATION-2|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHHH EEEEEE | 3chy-ITERATION-3|| PHD | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE | 3chy-ITERATION-4|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHH EEEEE | 3chy-ITERATION-5|| PHD | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE | 3chy-ITERATION-6|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHH EEEEEE | 3chy-ITERATION-7|| PHD | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE | 3chy-ITERATION-8|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHH EEEEEE | 3chy-ITERATION-9|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHHHH EEEEE | 3chy-ITERATION-0|| PHD | HHHHHHEEEEEE HHHHHHHHHHHHHHHHH HHHHHHHHHHHHHH | 3chy-ITERATION-1|| PHD | HHHHHHEEEEEE HHH HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | 3chy-ITERATION-2|| PHD | HHHHHHEEEEEE HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | 3chy-ITERATION-3|| PHD | HHHHHHHHHHHH HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | 3chy-ITERATION-4|| PHD | HHHHH EEEEE HHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | 3chy-ITERATION-5|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | 3chy-ITERATION-6|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH | 3chy-ITERATION-7|| PHD | HHHHHHHH EEEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | 3chy-ITERATION-8|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | 3chy-ITERATION-9|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH | Is the initial ss prediction good enough? 3chy-AA SEQUENCE|| AA |ADKELKFLVVDDFSTMRRIVRNLLKELGFNNVEEAEDGVDALNKLQAGGYGFVISDWNMP| 3chy-AA SEQUENCE|| AA |NMDGLELLKTIRADGAMSALPVLMVTAEAKKENIIAAAQAGASGYVVKPFTAATLEEKLNKIFEKLGM|

  28. Sequences to be aligned Align using secondary structure Multiple alignment Predict secondary structure HHHHCCEEECCCEEECCHH HHHCCCCEECCCEEHHH HHHHHHHHHHHHHCCCEEEE CCCCCCEECCCEEEECCHH HHHHHCCEEEECCCEECCC Secondary structure HHHHHCCEEEECCCEECCC Single Sequence MA-based

  29. 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)

  30. Summary • 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

More Related