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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.

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Presentation on theme: "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."— Presentation transcript:

1 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…..

2 Multi-dimensional dynamic programming (Murata et al. 1985)

3 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

4 Alternative multiple alignment methods  Biopat (first method ever)  MULTAL (Taylor 1987)  DIALIGN (Morgenstern 1996)  PRRP (Gotoh 1996)  Clustal (Thompson Higgins Gibson 1994)  Praline (Heringa 1999)  T Coffee (Notredame 2000)  HMMER (Eddy 1998) [Hidden Marcov Models]  SAGA (Notredame 1996) [Genetic algorithms]

5 Progressive multiple alignment general principles 1 2 1 3 4 5 Guide treeMultiple alignment Score 1-2 Score 1-3 Score 4-5 Scores Similarity matrix 5×5 Scores to distancesIteration possibilities

6 General progressive multiple alignment technique (follow generated tree) 1 3 2 5 1 3 1 3 1 3 2 5 2 5 4 d root

7 Progressive multiple alignment Problem: Accuracy is very important Errors are propagated into the progressive steps “Once a gap, always a gap” Feng & Doolittle, 1987

8 Multiple alignment profiles Gribskov et al. 1987 ACDWYACDWY Gap penalties i 0.3 0.1 0  0.3 0.51.0 Position dependent gap penalties

9 ACD……VWY sequence profile Profile-sequence alignment

10 ACD..YACD..Y ACD……VWY profile Profile-profile alignment

11 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)

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

13 Pre-profile generation 1 2 1 3 4 5 Score 1-2 Score 1-3 Score 4-5 ACD..YACD..Y 1 2 3 4 5 1 ACD..YACD..Y 2 1 3 4 5 2 Pre-profiles Pre-alignments 5 1 2 3 5 4 ACD..YACD..Y Cut-off

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

15 VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH PRIMARY STRUCTURE (amino acid sequence) QUATERNARY STRUCTURE (oligomers) SECONDARY STRUCTURE (helices, strands) TERTIARY STRUCTURE (fold) Protein structure hierarchical levels

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

17 Globalised local alignment += 1. Local (SW) alignment (M + P o,e ) 2. Global (NW) alignment (no M or P o,e ) Double dynamic programming

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

19 Matrix extension – T COFFEE 1 2 1 3 1 4 2 3 2 4 3 4

20 Summary Weighting schemes simulating simultaneous multiple alignment  Profile pre-processing (global/local)  Matrix extension (well balanced scheme) Smoothing alignment signals  globalised local alignment Using additional information  secondary structure driven alignment Schemes strike balance between speed and sensitivity

21 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.

22 Where to find this…. http://www.cs.vu.nl/~ibivu/teaching

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