1. Multiple sequence alignment (msa)
Lecture 8 CS566

2. Motivation “Two swallows do not make a summer”
Discover conserved regions
Predict important regions of the protein
Discover domains
Search for additional members of a protein family (profile-based searching)
Build phylogenetic trees
Lecture 8 CS566

3. Topics Scoring schemes Optimal Heuristic algorithms Pairwise N-way
Multidimensional dynamic programming
Heuristic algorithms
Progressive
Iterative
Lecture 8 CS566

4. Scoring schemes Alignment score = l Cl Column Score Cl Ideally
Based on n-way joint probability (n-generalized AAS)
Sum of Pairs
i<j sij Based on amino acid substitution matrices
Gap-gap = 0; Gap-char = -g
Commonest scheme used
Fallacious:
Assumes only 2-way and not n-way joint probabilities
Score not proportional to number of sequences in alignment
N-way sums
Need to know central point of reference (ancestral sequence)
Lecture 8 CS566

5. Multidimensional Dynamic Programming
Line up n sequences in a grid having n dimensions
Score each cell as the maximum of
Lining up all corresponding characters AND
All possible combinations of gaps and characters
Note choice made
Reconstruct alignment by traceback
Global or Local dynamic programming?
Space complexity?
Time complexity?
Lecture 8 CS566

6. MSA – Efficient Multidimensional Dynamic Programming
Carillo-Lipman MSA algorithm
Uses pair-wise dynamic programming to identify sub-matrix regions of near-optimality
n-dimensional dynamic programming carried out within space of intersection of near-optimal regions
Still limited to only a few sequences
Is this an optimal algorithm or not?
Lecture 8 CS566

7. Progressive alignment
New concepts
Consider aligning alignments to alignments/sequences en bloc
Hierarchical/Sequential order of alignment (“Once a cobbler, always a cobbler”)
Heuristic
Fast
Lecture 8 CS566

8. Progressive alignment - Clustal
Compute all pairwise alignments
Convert alignment scores into distances
Build guide tree (phylogenetic tree)
Align sequences in order suggested by ‘guide tree’
Position specific scoring system used
Gap costs depend on position
Composition based scoring system used
Percentage similarity dictates choice of scoring matrix
Weighting based on composition bias
Only ‘cross-terms’ (profile-profile) used in scoring
Lecture 8 CS566

9. Progressive alignment - Clustal
ClustalV (Now history!)
ClustalW (Takes weighting into account for composition bias)
ClustalX (Graphical interface)
Lecture 8 CS566

10. Iterative refinement-1
“Once a cobbler, now a king!”
Iterative algorithm:
Compute all pairwise similarities
Start with best pair
Add ‘most-similar’ sequence to profile successively till none left
Remove and re-align each sequence till convergence
Lecture 8 CS566

11. Iterative refinement-2
Genetic programming-based msa
Create initial random alignment
Score alignment
Retain better scoring half of alignment
Mutate remaining half of alignment with ideas from genetic recombination
Random gap insertion
En bloc shifts
Probabilistic order of alignment
Score resulting alignment
Iterate till convergence
Lecture 8 CS566