1. Introduction to bioinformatics 2007E N T R F O I G A V B M S U
Introduction to bioinformatics 2007
Lecture 5
Pair-wise Sequence Alignment

2. Bioinformatics
“Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky ( ))
“Nothing in bioinformatics makes sense except in the light of Biology”

3. Divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø)
ACCD or ACCD Pairwise Alignment
AB─D A─BD
mutation deletion

4. Divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø)
ACCD or ACCD Pairwise Alignment
AB─D A─BD
mutation deletion
true alignment

5. What can be observed about divergent evolution
Ancestral sequence G C A C
One substitution -
one visible
Two substitutions -
one visible
Sequence 1
Sequence 2
(c) G
(d) G
1: ACCTGTAATC
2: ACGTGCGATC
* **
D = 3/10 (fraction different sites (nucleotides)) G A A A
Back
mutation -
not visible
Two substitutions -
none visible G

6. Convergent evolution Often with shorter motifs (e.g. active sites)
Motif (function) has evolved more than once independently, e.g. starting with two very different sequences adopting different folds
Sequences and associated structures remain different, but (functional) motif can become identical
Classical example: serine proteinase and chymotrypsin

7. Serine proteinase (subtilisin) and chymotrypsin
Different evolutionary origins
Similarities in the reaction mechanisms. Chymotrypsin, subtilisin and carboxypeptidase C have a catalytic triad of serine, aspartate and histidine in common: serine acts as a nucleophile, aspartate as an electrophile, and histidine as a base.
The geometric orientations of the catalytic residues are similar between families, despite different protein folds.
The linear arrangements of the catalytic residues reflect different family relationships. For example the catalytic triad in the chymotrypsin clan is ordered HDS, but is ordered DHS in the subtilisin clan and SDH in the carboxypeptidase clan.

8. Serine proteinase (subtilisin) and chymotrypsin
carboxypeptidase C
Catalytic triads
Read

9. Serine proteinase (subtilisin) and chymotrypsin

10. Serine proteinase (subtilisin) and chymotrypsin

11. Serine proteinase (subtilisin) and chymotrypsin There is also divergent evolution..
Proc Natl Acad Sci U S A December 19; 97(26): 14097–14102.
The structure of aspartyl dipeptidase reveals a unique fold with a Ser-His-Glu catalytic triad
Kjell Håkansson,* Andrew H.-J. Wang,† and Charles G. Miller*‡

12. A protein sequence alignment
MSTGAVLIY--TSILIKECHAMPAGNE-----
---GGILLFHRTHELIKESHAMANDEGGSNNS
* * * **** ***
A DNA sequence alignment
attcgttggcaaatcgcccctatccggccttaa
att---tggcggatcg-cctctacgggcc----
*** **** **** ** ******

13. Searching for similarities
What is the function of the new gene?
The “lazy” investigation (i.e., no biologial experiments, just bioinformatics techniques):
– Find a set of similar protein sequences to the unknown sequence
– Identify similarities and differences
– For long proteins: first identify domains

14. Intermezzo: what is a domain
A domain is a:
Compact, semi-independent unit (Richardson, 1981).
Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973).
Recurring functional and evolutionary module (Bork, 1992).
“Nature is a ‘tinkerer’ and not an inventor” (Jacob, 1977).

15. Protein domains recur in different combinations
The DEATH Domain (DD)
Present in a variety of Eukaryotic proteins involved with cell death.
Six helices enclose a tightly packed hydrophobic core.
Some DEATH domains form homotypic and heterotypic dimers.

16. Structural domain organisation can intricate…
Pyruvate kinase
Phosphotransferase
b barrel regulatory domain
a/b barrel catalytic substrate binding domain
a/b nucleotide binding domain
1 continuous + 2 discontinuous domains

17. Evolutionary and functional relationships
Reconstruct evolutionary relation:
Based on sequence
-Identity (simplest method)
-Similarity
Homology (common ancestry: the ultimate goal)
Other (e.g., 3D structure)
Functional relation:
Sequence Structure Function

18. Searching for similarities
Common ancestry is more interesting:
Makes it more likely that genes share
the same function
Homology: sharing a common ancestor
– a binary property (yes/no)
– it’s a nice tool:
When (an unknown) gene X is homologous to (a known) gene G it means that we gain a lot of information on X: what we know about G can be transferred to X as a good suggestion.

19. How to go from DNA to protein sequence
A piece of double stranded DNA:
5’ attcgttggcaaatcgcccctatccggc 3’
3’ taagcaaccgtttagcggggataggccg 5’
DNA direction is from 5’ to 3’

20. How to go from DNA to protein sequence
6-frame translation using the codon table (last lecture):
5’ attcgttggcaaatcgcccctatccggc 3’
3’ taagcaaccgtttagcggggataggccg 5’

21. Evolution and three-dimensional protein structure information
Isocitrate
dehydrogenase:
The distance from
the active site
(in yellow) determines
the rate of evolution
(red = fast evolution, blue = slow evolution)
Dean, A. M. and G. B. Golding: Pacific Symposium on Bioinformatics 2000

22. Bioinformatics tool tool Algorithm Data Biological Interpretation
(model)

23. Example today: Pairwise sequence alignment needs sense of evolution
Global dynamic programming
MDAGSTVILCFVG
Evolution M D A S T I L C G
Amino Acid Exchange
Matrix
Search matrix
MDAGSTVILCFVG-
Gap penalties (open,extension)
MDAAST-ILC--GS

24. How to determine similarity
Frequent evolutionary events at the DNA level:
1. Substitution
2. Insertion, deletion
3. Duplication
4. Inversion
We will restrict ourselves to these events

25. A protein sequence alignment
MSTGAVLIY--TSILIKECHAMPAGNE-----
---GGILLFHRTHELIKESHAMANDEGGSNNS
* * * **** ***
A DNA sequence alignment
attcgttggcaaatcgcccctatccggccttaa
att---tggcggatcg-cctctacgggcc----
*** **** **** ** ******

26. Dynamic programming Scoring alignments
– Substitution (or match/mismatch)
• DNA
• proteins
– Gap penalty
• Linear: gp(k)=ak
• Affine: gp(k)=b+ak
• Concave, e.g.: gp(k)=log(k)
The score for an alignment is the sum of the scores of all alignment columns

27. Dynamic programming Scoring alignments
Sa,b =
gp(k) = gapinit + kgapextension affine gap penalties

28. DNA: define a score for match/mismatch of letters
Simple:
Used in genome alignments: A C G T 1 -1 A C G T 91
-114
-31
-123
100
-125

29. Dynamic programming Scoring alignments
T D W V T A L K
T D W L - - I K
2020 10 1
Affine gap penalties (open, extension)
Amino Acid Exchange Matrix
Score: s(T,T)+s(D,D)+s(W,W)+s(V,L)-Po-2Px +
+s(L,I)+s(K,K)

30. Amino acid exchange matrices
2020
How do we get one?
And how do we get associated gap penalties?
First systematic method to derive a.a. exchange matrices by Margaret Dayhoff et al. (1968) – Atlas of Protein Structure

31. amino acid exchange matrix (log odds)Q E G H I L K M F P S T W Y V B Z
A R N D C Q E G H I L K M F P S T W Y V B Z
PAM250 matrix
amino acid exchange matrix (log odds)
Positive exchange values denote mutations that are more likely than randomly expected, while negative numbers correspond to avoided mutations compared to the randomly expected situation

32. Amino acid exchange matrices
Amino acids are not equal:
1. Some are easily substituted because they have similar:
• physico-chemical properties
• structure
2. Some mutations between amino acids occur more often due to similar codons
The two above observations give us ways to define substitution matrices

33. Pair-wise alignment T D W V T A L K T D W L - - I K
Combinatorial explosion
- 1 gap in 1 sequence: n+1 possibilities
- 2 gaps in 1 sequence: (n+1)n
- 3 gaps in 1 sequence: (n+1)n(n-1), etc.
2n (2n)! n
= ~
n (n!) n
2 sequences of a.a.: ~1088 alignments
2 sequences of 1000 a.a.: ~10600 alignments!

34. Technique to overcome the combinatorial explosion: Dynamic Programming
Alignment is simulated as Markov process, all sequence positions are seen as independent
Chances of sequence events are independent
Therefore, probabilities per aligned position need to be multiplied
Amino acid matrices contain so-called log-odds values (log10 of the probabilities), so probabilities can be summed

35. To say the same more statistically…
To perform statistical analyses on messages or sequences, we need a reference model.
The model: each letter in a sequence is selected from a defined alphabet in an independent and identically distributed (i.i.d.) manner.
This choice of model system will allow us to compute the statistical significance of certain characteristics of a sequence, its subsequences, or an alignment.
Given a probability distribution, Pi, for the letters in a i.i.d. message, the probability of seeing a particular sequence of letters i, j, k, ... n is simply Pi Pj Pk···Pn.
As an alternative to multiplication of the probabilities, we could sum their logarithms and exponentiate the result. The probability of the same sequence of letters can be computed by exponentiating log Pi + log Pj + log Pk+ ··· + log Pn.
In practice, when aligning sequences we only add log-odds values (residue exchange matrix) but we do not exponentiate the final score.

36. Sequence alignment History of Dynamic Programming algorithm
1970 Needleman-Wunsch global pair-wise alignment
Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins, J Mol Biol. 48(3):
1981 Smith-Waterman local pair-wise alignment
Smith, TF, Waterman, MS (1981) Identification of common molecular subsequences. J. Mol. Biol. 147,

37. Pairwise sequence alignment
Global dynamic programming
MDAGSTVILCFVG
Evolution M D A S T I L C G
Amino Acid Exchange
Matrix
Search matrix
MDAGSTVILCFVG-
Gap penalties (open,extension)
MDAAST-ILC--GS

38. Global dynamic programming
j-1 j
i-1 i
Value from residue exchange matrix
H(i-1,j-1) + S(i,j)
H(i-1,j) - g
H(i,j-1) - g
diagonal
vertical
horizontal
H(i,j) = Max
This is a recursive formula

39. Global dynamic programming PAM250, Gap =6 (linear)S P E A R 2 1 H -1 -2 K 3 4 S P E A R -6
-12
-18
-24
-30
-36 2 -4
-10
-16
-22
-28 H -3 -9
-14
-20 -1 -7
-13 K
-15 -5 -2 6
These values are copied from the PAM250 matrix (see earlier slide)
The extra bottom row and rightmost column give the penalties that would need to be applied due to end gaps
Higgs & Attwood, p. 124

40. Global dynamic programming Affine gap penalties
j-1
i-1
Gap opening penalty
Max{S0<x<i-1, j-1 - Pi - (i-x-1)Px}
Si-1,j-1
Max{Si-1, 0<y<j-1 - Pi - (j-y-1)Px}
Si,j = si,j + Max
Gap extension penalty

41. Global dynamic programming Gapo=10, Gape=2W V T A L K 8 3 11 9 12 1 6 4 25 2 5 10 14 7 13 D W V T A L K
-12
-14
-16
-18
-20
-22
-24 8 -9 -6 -5
-11 9 2 3 -3
-34
-13 25 11 5 4
-21
-10 -4 37 21 19 15 -2 23 46 31 26 1 17 33 53 39 50 14
-29 -1 27
These values are copied from the PAM250 matrix (see earlier slide), after being made non-negative by adding 8 to each PAM250 matrix cell (-8 is the lowest number in the PAM250 matrix)
The extra bottom row and rightmost column give the final global alignment scores

42. Easy DP recipe for using affine gap penalties
j-1
i-1
M[i,j] is optimal alignment (highest scoring alignment until [i,j])
Check
preceding row until j-2: apply appropriate gap penalties
preceding row until i-2: apply appropriate gap penalties
and cell[i-1, j-1]: apply score for cell[i-1, j-1]

43. DP is a two-step process
Forward step: calculate scores
Trace back: start at highest score and reconstruct the path leading to the highest score
These two steps lead to the highest scoring alignment (the optimal alignment)
This is guaranteed when you use DP!

44. Global dynamic programming

45. Semi-global pairwise alignment
Global alignment: all gaps are penalised
Semi-global alignment: N- and C-terminal gaps (end-gaps) are not penalised
MSTGAVLIY--TS-----
---GGILLFHRTSGTSNS
End-gaps
End-gaps

46. Semi-global dynamic programming - two examples with different gap penalties -
These values are copied from the PAM250 matrix (see earlier slide), after being made non-negative by adding 8 to each PAM250 matrix cell (-8 is the lowest number in the PAM250 matrix)
Global score is 65 –10 – 1*2 –10 – 2*2

47. Semi-global pairwise alignment
Applications of semi-global:
– Finding a gene in genome
– Placing marker onto a chromosome
– One sequence much longer than the other
Danger: if gap penalties high -- really bad alignments for divergent sequences

48. Local dynamic programming (Smith & Waterman, 1981)
LCFVMLAGSTVIVGTR E D A S T I L C G
Negative
numbers
Amino Acid
Exchange Matrix
Search matrix
Gap penalties
(open, extension)
AGSTVIVG
A-STILCG

49. Local dynamic programming (Smith & Waterman, 1981)
j-1
i-1
Gap opening penalty
Si,j + Max{S0<x<i-1,j-1 - Pi - (i-x-1)Px}
Si,j + Si-1,j-1
Si,j + Max {Si-1,0<y<j-1 - Pi - (j-y-1)Px}
Si,j = Max
Gap extension penalty

50. Local dynamic programming

51. Dot plots
Way of representing (visualising) sequence similarity without doing dynamic programming (DP)
Make same matrix, but locally represent sequence similarity by averaging using a window

52. Comparing two sequences
We want to be able to choose the best alignment between two sequences.
A simple method of visualising similarities between two sequences is to use dot plots. The first sequence to be compared is assigned to the horizontal axis and the second is assigned to the vertical axis.

53. Dot plots can be filtered by window approaches (to calculate running averages) and applying a threshold
They can identify insertions, deletions, inversions