1. . Class 5: Multiple Sequence Alignment

2. Multiple sequence alignment VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWYQQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG LSLTCTVSGTSFDD--YYSTWVRQPPG PEVTCVVVDVSHEDPQVKFNWYVDG-- ATLVCLISDFYPGA--VTVAWKADS-- AALGCLVKDYFPEP--VTVSWNSG--- VSLTCLVKGFYPSD--IAVEWESNG-- Homologous residues are aligned together in columns  Homologous - in the structural and evolutionary sense Ideally, a column of aligned residues occupy similar 3d structural positions

3. Multiple alignment – why? u Identify sequence that belongs to a family  Family – a collection of homologous, with similar sequence, 3d structure, function or evolutionary history u Find features that are conserved in the whole family  Highly conserved regions, core structural elements

4. The relation between the divergence of sequence and structure [Durbin p. 137, redrawn from data in Chothia and Lesk (1986)]

5. Scoring a multiple alignment (1) Important features of multiple alignment: uSuSome positions are more conserved than others  Position specific scoring uSuSequences are not independent (related by phylogenetic tree) Ideally, specify a complete model of molecular sequence evolution

6. Scoring a multiple alignment (2) Unfortunately, not enough data … Assumption (1) Columns of alignment are statistically independent.

7. Minimum entropy Assumption (2) Symbols within columns are independent Entropy measure

8. Sum of pairs (SP) Columns are scored by a “sum of pairs” function, using a substitution scoring matrix Note:

9. Multidimensional DP

11. Complexity Space: Time:

12. Pairwise projections of MA

13. MSA (i) [Carrillo and Lipman, 1988]

14. MSA (ii)

15. MSA (iii) Algorithm sketch

16. Progressive alignment methods (i) Basic idea: construct a succession of PW alignments Variatoins: u PW alignment order u One growing alignment or subfamilies u Alignment and scoring procedure

17. Progressive alignment methods (ii) Most important heuristic – align the most similar pairs first. Many algorithms build a “guide tree”: u Leaves – sequence u Interior nodes – alignments u Root – complete multiple alignment

18. Feng-Doolittle (1987) u Calculate all pairwise distances using alignment scores: u Construct a guide tree using hierarchical clustering u Highest scoring pairwise alignment determines sequence to group alignment

19. Profile alignment u Use profiles for group to sequence and group to group alignments  CLUSTALW (Thompson et al., 1994):  Similar to Feng-Doolittle, but uses profile alignment methods  Numerous heuristics

20. Iterative Refinement u Addresses “frozen” sub-alignment problem u Iteratively realign sequences or groups to a profile of the rest u Barton and Sternberg (1987)  Align two most similar sequences  Align current profile to most similar sequence  Remove each sequence and align it to profile