Multiple Sequence Alignment

1
Chapter 5
Multiple Sequence Alignment
2

• Multiple alignment is an extension of pairwise
  alignment where multiple sequences are aligned
• This alignment provides insights not possible in
  pairwise alignments, such as
• Conserved sequence patterns
• Conserved and functionally critical amino acid
  residues
• Prerequisite for phylogenetic analyses
• Prediction of protein secondary and tertiary
  structures
• Design of degenerate PCR primers

3
Scoring Function

• The purpose of multiple alignment is to line up
  sequences in a way so that a maximum number of
  residues from each sequence are matched according
  to a scoring function
• The scoring function is generally based on sum
  of pairs (SP)
• The SP is the sum of all pairwise scores for all
  residues in the alignment

Sequence 1 G K N Sequence 2 T R N Sequence
3 S H E GT 1 KR2 NN6 TS 1 RH0
NE0 GS 0 KH-1 NE0 Total2 1
6 9
Blosum62 substitution matrix
Thus 29 512 times more likely than by random
chance
4
Exhaustive Algorithms
Brute Force Algorithm Similar to dynamic
programming algorithms that searches for the best
solution, examining every possible solution In
pairwise alignment use a 2D matrix For N
sequences, use an N-dimensional matrix Number of
calculations increase exponentially
(NNNN) Generally only useful for lt10 short
sequences Divide and Conquer Alignment
(DCA) Identify regional similarities in multiple
sequences Do a brute force alignment of the
similar regions Join the independently aligned
regions http//bibiserv.techfak.uni-bielefeld.de/d
ca/
5
(No Transcript)
6
Heuristic Algorithm
Progressive Alignment Method

• Pairwise alignment by Needleman-Wunsch of all
  pairs
• Records similarity scores of aligned pairs
• Scores entered into matrix
• Guide tree constructed that reflects similarity
  between aligned pairs
• Most closely related sequences re-aligned with
  Needleman-Wunsch
• Different substitution matrices are selected
  depending on evolutionary distance between
  sequences to be aligned
• Aligned pair converted to consensus sequence
  with fixed gaps
• Consensus sequences treated as ordinary sequence
  for next step which is pairwise alignment with
  most related sequence in guide tree
• Next consensus sequence is calculated and
  process repeated until all sequences are aligned
• Most famous clustalW (command line) clustalX
  (GUI)
• http//www.ebi.ac.uk/Tools/clustalw2/index.html

7
Download and install clustW from ftp//ftp.ebi.ac
.uk/pub/software/clustalw2/2.0.9/ Spend a few
minutes entering sequences and doing alignments
8

• ClustalW uses gap penalties that is context
  sensitive
• Gaps count more close to runs of hydrophobic
  amino acids (more likely to be in internal
  conserved regions of a protein) compared to next
  to hydrophilic regions or G, likely to be on the
  outside in loops
• Weighing scheme closely related sequences are
  gived a lower weighting score
• The weighting score is dependent upon the branch
  length divided by the number of shared branches
• This has the effect of minimizing a possible
  dominating effect of common sequences

9
Drawbacks and Solutions

• Based on global alignment thus only sequences
  of similar length can be aligned
• Long gaps required for alignment of dissimilar
  sequence length penalized
• Greedy algorithm once gaps are introduced,
  they stay in subsequence consensus sequences

10
T-Coffee

• Tree-based Consistency Objective Function for
  alignment Evaluation
• http//www.ebi.ac.uk/Tools/t-coffee/
• http//tcoffee.vital-it.ch/cgi-bin/Tcoffee/tcoffee
  _cgi/index.cgi
• Performs global alignment with clustal
• Local pairwise alignment with Lalign
• Global and ten best local alignments are pooled
  to form a library
• All pairwise alignments are then aligned with a
  third possible sequence
• Distance matrix calculated to build a guide tree
• Guide tree used for final multiple alignment
• Does not get stuck in sub-optimal initial
  alignments
• Slower than clustal

11
dbClustal

• First performs BLASTP search for a query sequence
• Aligned pairs are analyzed to obtain anchor
  points (local conserved regions) using a program
  called Ballast
• Global alignment generated by Clustal, weighed to
  anchor points
• Initial local alignment minimizes errors in
  divergent sequences
• Multiple alignment subsequently evaluated by
  NorMD which removes poorly aligned sequences
• http//bips.u-strasbg.fr/PipeAlign/jump_to.cgi?DbC
  lustalnoid

12
Partial Order Alignment (POA)

• http//bioinformatics.ucla.edu/poa/
• Multiple alignments performed on more and more
  sequences from a list
• Identical residues condensed to nodes
• Each new sequence aligned with each sequence of
  the graph model
• Eliminates the problem of error fixation
• Faster and more accurate than clustal

13
PRALINE

• http//zeus.cs.vu.nl/programs/pralinewww/
• Builds profiles of sequences to be aligned
• Profiles generated by PSI-BLAST
• Because profiles contain information on close
  relatives, divergent sequences are more
  accurately aligned
• Program can incorporate secondary protein
  structure
• Very sophisticated but very slow

14
Iterative Alignment

• PRRN
• Find optimal solution by iteratively modifying
  sub-optimal solutions
• http//prrn.ims.u-tokyo.ac.jp/
• Multiple alignment is performed on whole group of
  sequences
• Sequences randomly distributed into two groups
• Dynamic programming applied to consensus
  sequences derived from each group
• The random split is repeated and another round of
  dynamic programming alignment performed
• This is repeated until the alignment score no
  longer increases
• A multiple alignment of the sequences are then
  again performed
• Process repeated until multiple alignment score
  no longer improves

15
Iterative Alignment

• DIALIGN2
• http//mobyle.pasteur.fr/cgi-bin/MobylePortal/port
  al.py?formdialign
• Breaks all sequences down into segments, and
  performs alignment between segments
• High-scoring segments are progressively assembled
  into larger and larger sequences
• The score of an alignment is calculated from the
  block and not from individual residues
• Sequence regions between block are left unaligned
• Very suited to alignment of divergent sequences

16
Practical Issues

• DNA alignments are only based on 4 nucleotides,
  and are less reliable than protein sequence
  alignments
• Alignments of DNA sequence does not consider
  functional issues, suchas gene boundaries
• Insertion of gaps may break codons or cause
  frameshift that will not be tolerated in the
  protein, and is functional nonsense
• Thus, always better toalign protein sequences
• Possible to convert DNA to amino acid sequence,
  then align, and then decode back to DNA
• RevTrans (http//www.cbs.dtu.dk/services/RevTrans/
  )
• PROTA2DNA (missing link)

17
Editing and Format

• Most alignment programs require final editing by
  a human to ensure that there are no problems in
  functionality
• Finding badly aligned regions
• Removing non-sensical gaps etc.
• http//www.mbio.ncsu.edu/bioEdit/bioedit.html
• Need to convert one sequence format to another
  http//iubio.bio.indiana.edu/cgi-bin/readseq.cgi/