1. Protein Sequence Alignment Multiple Sequence Alignment
Part 3
Protein Sequence Alignment
Multiple Sequence Alignment

2. Table 3.1. Web sites for alignment of sequence pairs
Name of site
Bayes block alignera
Zhu et al. (1998)
Likelihood-weighted sequence alignmentb
see Web site
PipMaker (percent identity plot), a graphical tool for assessing long alignments
Schwartz et al. (2000)
BCM Search Launcherc
SIM—Local similarity program for finding alternative alignments
Huang et al. (1990); Huang and Miller (1991); Pearson and Miller (1992)
Global alignment programs (GAP, NAP)
Huang (1994)
FASTA program suited
Pearson and Miller (1992); Pearson (1996)
Pairwise BLASTe
Altschul et al. (1990)
AceViewf shows alignment of mRNAs and ESTs to the genome sequence
BLATf Fast alignment for finding genes in genome
Kent (2002)
GeneSeqerf predicts genes and aligns mRNA and genome sequences
Usuka et al. (2000)
SIM4f
Floria et al. (1998)

3. Protein Sequence Alignment

4. Protein Pairwise Sequence Alignment
The alignment tools are similar to the DNA alignment tools
BLASTP, FASTA
Main difference: instead of scoring match (+2) and mismatch (-1) we have similarity scores:
Score s(i,j) > 0 if amino acids i and j have similar properties
Score s(i,j) is  0 otherwise
How should we score s(i,j)?

5. The 20 Amino Acids

6. Chemical Similarities Between Amino Acids
Acids & Amides DENQ (Asp, Glu, Asn, Gln)
Basic HKR (His, Lys, Arg)
Aromatic FYW (Phe, Tyr, Trp)
Hydrophilic ACGPST (Ala, Cys, Gly, Pro, Ser, Thr)
Hydrophobic ILMV (Ile, Leu, Met, Val)

7. Amino Acid Substitutions Matrices
For aligning amino acids, we need a scoring matrix of 20 rows  20 columns
Matrices represent biological processes
Mutation causes changes in sequence
Evolution tends to conserve protein function
Similar function requires similar amino acids
Could base matrix on amino acid properties
In practice: based on empirical data

8. identity
similarity

9. Given an alignment of closely related sequences
we can score the relation between amino acids
based on how frequently they substitute each other
AGHKKKR
D SFHRRRAGC D E - S
In this column
E & D are found
8/10

10. Amino Acid Matrices
Symmetric matrix of 20x20 entries: entry (i,j)=entry(j,i)
Entry (i,i) is greater than any entry (i,j), ji.
Entry (i,j): the score of aligning amino acid i against amino acid j.

11. PAM - Point Accepted Mutations
Developed by Margaret Dayhoff, 1978.
Analyzed very similar protein sequences
Proteins are evolutionary close.
Alignment is easy.
Point mutations - mainly substitutions
Accepted mutations - by natural selection.
Used global alignment.
Counted the number of substitutions (i,j) per amino acid pair: Many i<->j substitutions => high score s(i,j)
Found that common substitutions occurred involving chemically similar amino acids.

12. PAM 250 Similar amino acids are close to each other.
Regions define conserved substitutions.

13. Selecting a PAM Matrix
Low PAM numbers: short sequences, strong local similarities.
High PAM numbers: long sequences, weak similarities.
PAM120 recommended for general use (40% identity)
PAM60 for close relations (60% identity)
PAM250 for distant relations (20% identity)
If uncertain, try several different matrices
PAM40, PAM120, PAM250 recommended

14. BLOSUM Blocks Substitution Matrix
Steven and Jorga G. Henikoff (1992)
Based on BLOCKS database (
Families of proteins with identical function
Highly conserved protein domains
Ungapped local alignment to identify motifs
Each motif is a block of local alignment
Counts amino acids observed in same column
Symmetrical model of substitution
AABCDA… BBCDA
DABCDA. A.BBCBB
BBBCDABA.BCCAA
AAACDAC.DCBCDB
CCBADAB.DBBDCC
AAACAA… BBCCC

15. BLOSUM Matrices
Different BLOSUMn matrices are calculated independently from BLOCKS
BLOSUMn is based on sequences that are at most n percent identical.

16. Selecting a BLOSUM Matrix
For BLOSUMn, higher n suitable for sequences which are more similar
BLOSUM62 recommended for general use
BLOSUM80 for close relations
BLOSUM45 for distant relations

17. Multiple Sequence Alignment

18. Multiple Alignment Like pairwise alignment
n input sequences instead of 2
Add indels to make same length
Local and global alignments
Score columns in alignment independently
Seek an alignment to maximize score

19. Alignment Example
GTCGTAGTCGGCTCGAC GTCTAGCGAGCGTGAT GCGAAGAGGCGAGC GCCGTCGCGTCGTAAC
1*1
2*0.75
11*0.5
Score=8
GTCGTAGTCG-GC-TCGAC GTC-TAG-CGAGCGT-GAT GC-GAAG-AG-GCG-AG-C GCCGTCG-CG-TCGTA-AC
4*1
11*0.75
2*0.5
Score=13.25
Score : 4/4 =1 , 3/4 =0.75 , 2/4=0.5 , 1/4= 0

20. Dynamic Programming Pairwise A–B alignment table
Cell (i,j) = score of best alignment between first i elements of A and first j elements of B
Complexity: length of A  length of B
3-way A–B–C alignment table
Cell (i,j,k) = score of best alignment between first i elements of A, first j of B, first k of C
Complexity: length A  length B  length C

21. MSA Complexity n-way S1–S2–…–Sn-1–Sn alignment table
Cell (x1,…,xn) = best alignment score between first x1 elements of S1, …, xn elements of Sn
Complexity: length S1  …  length Sn
Example: protein family alignment
100 proteins, 1000 amino acids each
Complexity: table cells
Calculation time: beyond the big bang!

22. Feasible Approach Based on pairwise alignment scores
Build n by n table of pairwise scores
Align similar sequences first
After alignment, consider as single sequence
Continue aligning with further sequences

23. Sum of pairwise alignment scores
For n sequences, there are n(n-1)/2 pairs
GTCGTAGTCG-GC-TCGAC GTC-TAG-CGAGCGT-GAT GC-GAAG-AG-GCG-AG-C GCCGTCG-CG-TCGTA-AC

24. 1 GTCGTAGTCG-GC-TCGAC 2 GTC-TAG-CGAGCGT-GAT 3 GC-GAAGAGGCG-AGC 4 GCCGTCGCGTCGTAAC

25. ClustalW Algorithm
Progressive Sequences Alignment (Higgins and Sharp 1988)
Compute pairwise alignment for all the pairs of sequences.
Use the alignment scores to build a phylogenetic tree such that
similar sequences are neighbors in the tree
distant sequences are distant from each other in the tree.
The sequences are progressively aligned according to the branching order in the guide tree.

26. Progressive Sequence Alignment
(Protein sequences example)
N Y L S
N K Y L S
N F S
N F L S
N K/- Y L S
N F L/- S
N K/- Y/F L/- S

27. Treating Gaps in ClustalW
Penalty for opening gaps and additional penalty for extending the gap
Gaps found in initial alignment remain fixed
New gaps are introduced as more sequences are added (decreased penalty if gap exists)
Decreased within stretches of hydrophilic residues

28. MSA Approaches Progressive approach CLUSTALW (CLUSTALX) PILEUP
T-COFFEE
Iterative approach: Repeatedly realign subsets of sequences. MultAlin, DiAlign.
Statistical Methods:
Hidden Markov Models
SAM2K
Genetic algorithm
SAGA