Introduction to bioinformatics Lecture 7 Multiple sequence alignment (1)

Global or Local Pairwise alignment Local Global A B A B A B BA AB Local Global A B A C C A BC ABC A B A C C

Globin fold  protein myoglobin PDB: 1MBN Helices are labelled ‘A’ (blue) to ‘H’ (red). D helix can be missing in some globins: what happens with alignment?

 sandwich  protein immunoglobulin PDB: 7FAB

TIM barrel  /  protein Triose phosphate IsoMerase PDB: 1TIM

Pyruvate kinase Phosphotransferase  barrel regulatory domain  barrel catalytic substrate binding domain  nucleotide binding domain

What does this mean for alignments? Alignments need to be able to skip secondary structural elements to complete domains (i.e. putting gaps opposite these motifs in the shorter sequence). Depending on gap penalties chosen, the algorithm might have difficulty with making such long gaps (for example when using high affine gap penalties), resulting in incorrect alignment.

There are three kinds of pairwise alignments Global alignment – align all residues in both sequences; all gaps are penalised Semi-global alignment – align all residues in both sequences; end gaps are not penalised (zero end gap penalties) Local alignment – align part of each sequence; end gaps are not applicable

Easy global DP recipe for using affine gap penalties (after Gotoh) M[i,j] is optimal alignment (highest scoring alignment until [i, j]) At each cell [i, j] in search matrix, check Max coming from: any cell in preceding row until j-2: add score for cell[i, j] minus appropriate gap penalties; any cell in preceding column until i-2: add score for cell[i, j] minus appropriate gap penalties; or cell[i-1, j-1]: add score for cell[i, j] Select highest scoring cell in bottom row and rightmost column and do trace-back i-1 j-1 Penalty = Pi + gap_length*Pe S i,j = s i,j + Max Max{S 0<x<i-1, j-1 - Pi - (i-x-1)Px} S i-1,j-1 Max{S i-1, 0<y<j-1 - Pi - (j-y-1)Px}

Let’s do an example: global alignment Gotoh’s DP algorithm with affine gap penalties (PAM250, Pi=10, Pe=2) Row and column ‘0’ are filled with 0, -12, -14, -16, … if global alignment is used (for N-terminal end-gaps); also extra row and column at the end to calculate the score including C-terminal end-gap penalties. DWVTALK T D W V L K DWVTALK T D W V L K PAM250 Cell (D2, T4) can alternatively come from two cells (same score): ‘high-road’ or ‘low-road’

Let’s do another example: semi-global alignment Gotoh’s DP algorithm with affine gap penalties (PAM250, Pi=10, Pe=2) Starting row and column ‘0’, and extra column at right or extra row at bottom is not necessary when using semi global alignment (zero end- gaps). Rest works as under global alignment. DWVTALK T D W V L K DWVTALK T0-503 D4-7 W V L K PAM250

Easy local DP recipe for using affine gap penalties (after Gotoh) M[i,j] is optimal alignment (highest scoring alignment until [i, j]) At each cell [i, j] in search matrix, check Max coming from: any cell in preceding row until j-2: add score for cell[i, j] minus appropriate gap penalties; any cell in preceding column until i-2: add score for cell[i, j] minus appropriate gap penalties; or cell[i-1, j-1]: add score for cell[i, j] Select highest scoring cell anywhere in matrix and do trace-back until zero-valued cell or start of sequence(s) i-1 j-1 Penalty = Pi + gap_length*Pe S i,j = Max S i,j + Max{S 0<x<i-1,j-1 - Pi - (i-x-1)Px} S i,j + S i-1,j-1 S i,j + Max {S i-1,0<y<j-1 - Pi - (j-y-1)Px} 0

Let’s do yet another example: local alignment Gotoh’s DP algorithm with affine gap penalties (PAM250, Pi=10, Pe=2) Extra start/end columns/rows not necessary (no end-gaps). Each negative scoring cell is set to zero. Highest scoring cell may be found anywhere in search matrix after calculating it. Trace highest scoring cell back to first cell with zero value (or the beginning of one or both sequences) DWVTALK T D W V L K DWVTALK T0003 D4000 W02100 V00259 L0011 K00 PAM250

For your first exam D1: Make sure you understand and can carry out Gotoh’s algorithm for global, semi-global and local alignment! This is the most general Dynamic Programming (DP) algorithm (and perhaps the easiest to understand) Gotoh, O. An Improved Algorithm for Matching Biological Sequences. J. Mol. Biol., 162, pp , 1982.

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…..

Multiple sequence alignment (MSA) Why One of he most important means to find out about: Conservation patterns leading to functional clues Possible protein structure Multiple sequence alignment contains far more information about conservation than pairwise alignment Many bioinformatics methods use MSA as input: e.g. secondary structure prediction (later lecture)

Multiple sequence alignment Wanted Quality Programs need to be fully automatic for genomic pipelines With available genomes (data explosion), speed becomes crucial

(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 (first complete MSA method ever; Hogeweg Hesper 1984)  MULTAL (Taylor 1987)  DIALIGN (Morgenstern 1996)  PRRP (Gotoh 1996)  Clustal (Thompson Higgins Gibson 1994)  Praline (Heringa 1999)  T-Coffee (Notredame et al. 2000)  HMMER (Eddy 1998) [Hidden Markov Models]  SAGA (Notredame 1996) [Genetic algorithm]  POA (Lee et al. 2002)  MUSCLE (Edgar 2004)

The following three slides are examples of multiple alignments of 13 flavodoxin and 1 cheY sequence (PDB code 3chy). The cheY sequence is a very distant relative of the flavodoxin family, but has the same basic fold

CLUSTAL X (1.64b) multiple sequence alignment 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 fxn 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 :..

Flavodoxin-cheY: Global Pre-processing (prepro  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 fcr 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 chy VTAEAKK--ENIIAA AQAGAS GYVV-----KPFTAATLEEKLNKIFEKLGM G Iteration 0 SP= AvSP= SId= 4009 AvSId= 0.313

Flavodoxin-cheY: Local Pre-processing (locprepro  300) 1fx1 --PKALIVYGSTTGNTEYTAETIARQLANAGYEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPL--FDSLEETGAQGRKVACF FLAV_DESVH -MPKALIVYGSTTGNTEYTaETIARELADAGYEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPL--FDSLEETGAQGRKVACf FLAV_DESSA -MSKSLIVYGSTTGNTETAaEYVAEAFENKEIDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPL--YDSLENADLKGKKVSVf FLAV_DESGI -MPKALIVYGSTTGNTEGVaEAIAKTLNSEGMETTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPL--YEDLDRAGLKDKKVGVf FLAV_DESDE -MSKVLIVFGSSTGNTESIaQKLEELIAAGGHEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSL--FEEFNRFGLAGRKVAAf 4fxn --MK--IVYWSGTGNTEKMAELIAKGIIESGKDVNTINVSDVNIDELLN-EDILILGCSAMGDEVL------E-ESEFEPF--IEEIS-TKISGKKVALF FLAV_MEGEL -MVE--IVYWSGTGNTEAMaNEIEAAVKAAGADVESVRFEDTNVDDVAS-KDVILLgCPAMGSEEL------E-DSVVEPF--FTDLA-PKLKGKKVGLf 2fcr ---KIGIFFSTSTGNTTEVADFIGKTLGAKADAPI--DVDDVTDPQALKDYDLLFLGAPTWNTGAD----TERSGTSWDEFL-YDKLPEVDMKDLPVAIF FLAV_ANASP -SKKIGLFYGTQTGKTESVaEIIRDEFGNDVVTLH--DVSQAEV-TDLNDYQYLIIgCPTWNIGEL QSDWEGL--YSELDDVDFNGKLVAYf FLAV_AZOVI --AKIGLFFGSNTGKTRKVaKSIKKRFDDETMSDA-LNVNRVSA-EDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEF--LPKIEGLDFSGKTVALf FLAV_ENTAG -MATIGIFFGSDTGQTRKVaKLIHQKLDG--IADAPLDVRRATR-EQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEF--TNTLSEADLTGKTVALf FLAV_ECOLI --AITGIFFGSDTGNTENIaKMIQKQLGKDVADVH--DIAKSSK-EDLEAYDILLLgIPTWYYGEA QCDWDDF--FPTLEEIDFNGKLVALf FLAV_CLOAB --MKISILYSSKTGKTERVaKLIEEGVKRSGNIEVKTMNLDAVDKKFLQESEGIIFgTPTYYA NISWEMKKWIDESSEFNLEGKLGAAf 3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFNNVEEAEDGVDALNKLQ-AGGYGFVI---SDWNMPNM DGLEL--LKTIRADGAMSALPVLM 1fx1 GCGDS--SY-EYFCGA-VD--AIEEKLKNLGAEIVQD GLRID--GDPRAARDDIVGWAHDVRGAI FLAV_DESVH GCGDS--SY-EYFCGA-VD--AIEEKLKNLgAEIVQD GLRID--GDPRAARDDIVGwAHDVRGAI FLAV_DESSA GCGDS--DY-TYFCGA-VD--AIEEKLEKMgAVVIGD SLKID--GDPE--RDEIVSwGSGIADKI FLAV_DESGI GCGDS--SY-TYFCGA-VD--VIEKKAEELgATLVAS SLKID--GEPD--SAEVLDwAREVLARV FLAV_DESDE ASGDQ--EY-EHFCGA-VP--AIEERAKELgATIIAE GLKME--GDASNDPEAVASfAEDVLKQL fxn GS------Y-GWGDGKWMR--DFEERMNGYGCVVVET PLIVQ--NEPDEAEQDCIEFGKKIANI FLAV_MEGEL GS------Y-GWGSGEWMD--AWKQRTEDTgATVIGT AI-VN--EMPDNA-PECKElGEAAAKA fcr GLGDAE-GYPDNFCDA-IE--EIHDCFAKQGAKPVGFSNPDDYDYEESKSVRD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV FLAV_ANASP GTGDQI-GYADNFQDA-IG--ILEEKISQRgGKTVGYWSTDGYDFNDSKALRN-GKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL FLAV_AZOVI GLGDQV-GYPENYLDA-LG--ELYSFFKDRgAKIVGSWSTDGYEFESSEAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L-- FLAV_ENTAG GLGDQL-NYSKNFVSA-MR--ILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L FLAV_ECOLI GCGDQE-DYAEYFCDA-LG--TIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA FLAV_CLOAB STANSIAGGSDIALLTILNHLMVKgMLVYSGGVAFGKPKTHLGYVH INEIQENEDENARIfGERiANkVKQIF chy VTAEA---KKENIIAA AQAGAS GYVVK-----PFTAATLEEKLNKIFEKLGM G

Flavodoxin-cheY: Pre-processing (prepro  1500)