Class%205:%20Multiple%20Sequence%20Alignment

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Class 5Multiple Sequence Alignment
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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

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Multiple alignment why?

• Identify sequence that belongs to a family
• Family a collection of homologous, with similar
  sequence, 3d structure, function or evolutionary
  history
• Find features that are conserved in the whole
  family
• Highly conserved regions, core structural
  elements

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The relation between the divergence of sequence
and structure
Durbin p. 137, redrawn from data in Chothia and
Lesk (1986)
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Scoring a multiple alignment (1)

• Important features of multiple alignment
• Some positions are more conserved than others
• ? Position specific scoring
• Sequences are not independent (related by
  phylogenetic tree)
• Ideally, specify a complete model of molecular
  sequence evolution

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Scoring a multiple alignment (2)

• Unfortunately, not enough data
• Assumption (1)
• Columns of alignment are statistically
  independent.

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Minimum entropy

• Assumption (2)
• Symbols within columns are independent

Entropy measure
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Sum of pairs (SP)

• Columns are scored by a sum of pairs function,
  using a substitution scoring matrix
• Note

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Multidimensional DP
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Multidimensional DP
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Multidimensional DP
Complexity Space Time
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Pairwise projections of MA
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MSA (i)

• Carrillo and Lipman, 1988

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MSA (ii)
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MSA (iii)

• Algorithm sketch

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Progressive alignment methods (i)

• Basic idea construct a succession of PW
  alignments
• Variatoins
• PW alignment order
• One growing alignment or subfamilies
• Alignment and scoring procedure

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Progressive alignment methods (ii)

• Most important heuristic align the most similar
  pairs first.
• Many algorithms build a guide tree
• Leaves sequence
• Interior nodes alignments
• Root complete multiple alignment

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Feng-Doolittle (1987)

• Calculate all pairwise distances using alignment
  scores
• Construct a guide tree using hierarchical
  clustering
• Highest scoring pairwise alignment determines
  sequence to group alignment

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Profile alignment

• 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

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Iterative Refinement

• Addresses frozen sub-alignment problem
• Iteratively realign sequences or groups to a
  profile of the rest
• Barton and Sternberg (1987)
• Align two most similar sequences
• Align current profile to most similar sequence
• Remove each sequence and align it to profile