. Class 5: Multiple Sequence Alignment. Multiple sequence alignment VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWYQQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG.

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Presentation transcript:

. Class 5: Multiple Sequence Alignment

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

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

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

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

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

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

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

Multidimensional DP

Complexity Space: Time:

Pairwise projections of MA

MSA (i) [Carrillo and Lipman, 1988]

MSA (ii)

MSA (iii) Algorithm sketch

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

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

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

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

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