Transcript Lecture 7
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) Multiple sequence alignment (MSA) of 12 * Flavodoxin + cheY 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….. Multi-dimensional dynamic programming (Murata et al. 1985) 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 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] Progressive multiple alignment general principles 1 2 1 3 Score 1-2 4 5 Score 4-5 Score 1-3 Scores 5×5 Scores to distances Guide tree Similarity matrix Iteration possibilities Multiple alignment General progressive multiple alignment technique (follow generated tree) d 1 3 1 3 2 5 1 3 2 5 root 1 3 2 5 4 Progressive multiple alignment Problem: Accuracy is very important Errors are propagated into the progressive steps “Once a gap, always a gap” Feng & Doolittle, 1987 Pair-wise alignment quality versus sequence identity (Vogt et al., JMB 249, 816-831,1995) Multiple alignment profiles Gribskov et al. 1987 i A C D W Y Gap penalties 0.3 0.1 0 0.3 0.3 1.0 0.5 Position dependent gap penalties Profile-sequence alignment sequence profile ACD……VWY Profile-profile alignment profile A C D . . Y profile ACD……VWY 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) Strategies for multiple sequence alignment Profile pre-processing Secondary structure-induced alignment Globalised local alignment Matrix extension Objective: try to avoid (early) errors Pre-profile generation 1 2 1 3 Score 1-2 4 5 Score 4-5 Score 1-3 Cut-off 1 1 2 3 4 5 2 2 134 5 5 5 1 2 3 4 Pre-alignments A C D . . Y A C D . . Y A C D . . Y Pre-profiles Pre-profile alignment Pre-profiles 1 2 3 4 5 A C D . . Y A C D . . Y A C D . . Y A C D . . Y Final alignment A C D . . Y 1 2 3 4 5 Pre-profile alignment 1 2 3 4 5 12 3 4 5 21 3 4 5 31 2 4 5 41 2 3 5 5 1 2 3 4 Final alignment 1 2 3 4 5 Strategies for multiple sequence alignment Profile pre-processing Secondary structure-induced alignment Globalised local alignment Matrix extension Objective: try to avoid (early) errors Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH QUATERNARY STRUCTURE (oligomers) TERTIARY STRUCTURE (fold) One of the Molecular Biology Dogma’s “Structure more conserved than sequence” Secondary structure-induced alignment Using secondary structure for alignment Dynamic programming search matrix M D A A S T I L C G S Amino acid exchange weights matrices MDAGSTVILCFV HHHCCCEEEEEE H H H H H C C E E E C C H C C E E Default Flavodoxin-cheY Using predicted secondary structure 1fx1 FLAV_DESVH FLAV_DESGI FLAV_DESSA FLAV_DESDE 2fcr FLAV_ANASP FLAV_ECOLI FLAV_AZOVI FLAV_ENTAG 4fxn FLAV_MEGEL FLAV_CLOAB 3chy 1fx1 FLAV_DESVH FLAV_DESGI FLAV_DESSA FLAV_DESDE 2fcr FLAV_ANASP FLAV_ECOLI FLAV_AZOVI FLAV_ENTAG 4fxn FLAV_MEGEL FLAV_CLOAB 3chy -PK-ALIVYGSTTGNTEYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACF e eeee b ssshhhhhhhhhhhhhhttt eeeee stt tttttt seeee b ee sss ee ttthhhhtt ttss tt eeeee MPK-ALIVYGSTTGNTEYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACf e eeeeee hhhhhhhhhhhhhhh eeeeee eeeeee hhhhhh eeeee MPK-ALIVYGSTTGNTEGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLYED-LDRAGLKDKKVGVf e eeeeee hhhhhhhhhhhhhh eeeeee hhhhhh eeeeeee hhhhhh eeeeee MSK-SLIVYGSTTGNTETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLYDS-LENADLKGKKVSVf eeeeee hhhhhhhhhhhhhh eeeee eeeee hhhhhhh h eeeee MSK-VLIVFGSSTGNTESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLFEE-FNRFGLAGRKVAAf eeee hhhhhhhhhhhhhh eeeee hhhhhhhhhhheeeee hhhhhhh hh eeeee --K-IGIFFSTSTGNTTEVADFIGKTLGAK---ADAPIDVDDVTDPQALKDYDLLFLGAPTWNTGAD----TERSGTSWDEFLYDKLPEVDMKDLPVAIF eeeee ssshhhhhhhhhhhhhggg b eeggg s gggggg seeeeeee stt s s s sthhhhhhhtggg tt eeeee SKK-IGLFYGTQTGKTESVaEIIRDEFGND--VVTL-HDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLYSE-LDDVDFNGKLVAYf eeeee hhhhhhhhhhhh eee hhh hhhhhhheeeeee hhhhhhhhh eeeeee -AI-TGIFFGSDTGNTENIaKMIQKQLGKD--VADV-HDIAKSS-KEDLEAYDILLLgIPTWYYGEA--------QCDWDDFFPT-LEEIDFNGKLVALf eee hhhhhhhhhhhh eee hhh hhhhhhheeeee hhhhh eeeeee -AK-IGLFFGSNTGKTRKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFLPK-IEGLDFSGKTVALf eee hhhhhhhhhhhhh hhh hhhhhhheeeee hhhhhhhhh eeeeee MAT-IGIFFGSDTGQTRKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFTNT-LSEADLTGKTVALf eeee hhhhhhhhhhhh hhh hhhhhhheeeee hhhhh eeeee ----MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDVNIDELLNE-DILILGCSAMGDEVL------E-ESEFEPFIEE-IST-KISGKKVALF eeeee ssshhhhhhhhhhhhhhhtt eeeettt sttttt seeeeee btttb ttthhhhhhh hst t tt eeeee M---VEIVYWSGTGNTEAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVASK-DVILLgCPAMGSEEL------E-DSVVEPFFTD-LAP-KLKGKKVGLf hhhhhhhhhhhhhh eeeee hhhhhhhh eeeee eeeee M-K-ISILYSSKTGKTERVaKLIEEGVKRSGNIEVKTMNL-DAVDKKFLQESEGIIFgTPTY-YANI--------SWEMKKWIDE-SSEFNLEGKLGAAf eee hhhhhhhhhhhhhh eeeeee hhhhhhhhhh eeee hhhhhhhhh eeeee ADKELKFLVVDDFSTMRRIVRNLLKELGFNN-VEEAEDGV-DALNKLQAGGYGFVISD---WNMPNM----------DGLELLKTIRADGAMSALPVLMV tt eeee s hhhhhhhhhhhhhht eeeesshh hhhhhhhh eeeee s sss hhhhhhhhhh ttttt eeee GCGDS-SY-EYFCGAVDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI-------eee s ss sstthhhhhhhhhhhttt ee s eeees gggghhhhhhhhhhhhhh GCGDS-SY-EYFCGAVDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI-------eee hhhhhhhhhhhh eeeee eeeee hhhhhhhhhhhhhh GCGDS-SY-TYFCGAVDVIEKKAEELgATLVAS---------------------SLKIDGE--P--DSAEVLDwAREVLARV-------eee hhhhhhhhhhhh eeeee hhhhhhhhhhh GCGDS-DY-TYFCGAVDAIEEKLEKMgAVVIGD---------------------SLKIDGD--P--ERDEIVSwGSGIADKI-------hhhhhhhhhhhh eeeee e eee ASGDQ-EY-EHFCGAVPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL-------e hhhhhhhhhhhhhh eeeee ee hhhhhhhhhhh GLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVRD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV-----eee ttt ttsttthhhhhhhhhhhtt eee b gggs s tteet teesseeeettt ss hhhhhhhhhhhhhhhht GTGDQIGYADNFQDAIGILEEKISQRgGKTVGYWSTDGYDFNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL-----hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhh GCGDQEDYAEYFCDALGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhhhh GLGDQVGYPENYLDALGELYSFFKDRgAKIVGSWSTDGYEFESSEAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L-e hhhhhhhhhhhhhh eeeee hhhhhhhhhhh GLGDQLNYSKNFVSAMRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L-----hhhhhhhhhhhhhhh eeee hhhhhhh hhhhhhhhhhhh G-----SYGWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI--------e eesss shhhhhhhhhhhhtt ee s eeees ggghhhhhhhhhhhht G-----SYGWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNAPE-CKElGEAAAKA--------hhhhhhhhhhh eeeee eeee h hhhhhhhh STANSIA-GGSDIALLTILNHLMVK-gMLVYSG----GVAFGKPKTHLG-----YVHINEI--QENEDENARIfGERiANkV--KQIF-hhhhhhhhhhhhhh eeeee hhhh hhh hhhhhhhhhhhh h -----------TAEAKKENIIAAAQAGASGY-------------------------VVK----P-FTAATLEEKLNKIFEKLGM-----ess hhhhhhhhhtt see ees s hhhhhhhhhhhhhhht Strategies for multiple sequence alignment Profile pre-processing Secondary structure-induced alignment Globalised local alignment Matrix extension Objective: try to avoid (early) errors Globalised local alignment 1. Local (SW) alignment (M + Po,e) + = 2. Global (NW) alignment (no M or Po,e) Double dynamic programming M = BLOSUM62, Po= 0, Pe= 0 M = BLOSUM62, Po= 12, Pe= 1 M = BLOSUM62, Po= 60, Pe= 5 Strategies for multiple sequence alignment Profile pre-processing Secondary structure-induced alignment Globalised local alignment Matrix extension Objective: try to avoid (early) errors Matrix extension T-Coffee Tree-based Consistency Objective Function For alignmEnt Evaluation Cedric Notredame Des Higgins Jaap Heringa J. Mol. Biol., 302, 205-217;2000 Matrix extension – T COFFEE 2 1 3 1 4 1 3 2 4 2 4 3 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 Using different sources of alignment information Clustal Clustal Structure alignments Dialign Lalign Manual T-Coffee Search matrix extension 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 seqAseqI-seqB: W’(A(x), B(y)) = W(A(x), B(y)) + IA,BMin(W(A(x), I(z)), W(I(z), B(y))) – Summation over all third sequences I other than A or B T-Coffee Other sequences Direct alignment Search matrix extension 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 Evaluating multiple alignments As a standard of truth, often a reference alignment based on structural superpositioning is taken Evaluation measures Query Reference Column score Sum-of-Pairs score Evaluating multiple alignments SP BAliBASE alignment nseq * len Summary Weighting schemes simulating simultaneous multiple alignment Profile pre-processing (global/local) Matrix extension (well balanced scheme) Smoothing alignment signals Using additional information globalised local alignment secondary structure driven alignment Schemes strike balance between speed and sensitivity 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. Where to find this…. http://www.ibivu.cs.vu.nl/teaching