1. Pairwise Sequence Alignment (PSA)
Why and How

2. Goals of sequence alignment
From the alignment we can learn about:
The function of a new protein
New members of a gene family
Evolutionary relationships between genes
Position and function of coding genes and of regulatory regions in a genomic sequence
Comparison of sequences between individuals can detect changes that are related to diseases
Slide by Vered Caspi, BGU

3. Similarity vs. homology
Sequence alignment algorithms enable us to identify similarity between sequences
From sequence similarity (and additional biological knowledge) we may deduce sequence homology.

4. Homology: common ancestry of genes

5. Homologous genes: orthologs and paralogs
speciation

6. Homologous genes possible similarity in structure/function
Similar genes
Retinol-binding protein, human
(NP_006735)
b-lactoglobulin, cow
(P02754)
Slide from J. Pevsner - Page 42

7. Pairwise Sequence Alignment
EEELTKPRLLWALYFNMRDALSSG
VEKPRILYALYFNMRDSSDE
You can find a list of abbreviations at:

8. Pairwise Sequence Alignment
EEELTKPRLLWALYFNMRDALSSG
VEKPRILYALYFNMRDSSDE
EEELTKPRLLWALYFNMRDALSSG-
---VEKPRILYALYFNMRD--SSDE
Alignment The process of lining up two or more sequences to achieve maximal levels of identity (and conservation, in the case of amino acid sequences) for the purpose of assessing the degree of similarity and the possibility of homology.
Slide by Vered Caspi, BGU

9. Pairwise Sequence Alignment
EEELTKPRLLWALYFNMRDALSSG
VEKPRILYALYFNMRDSSDE
EEELTKPRLLWALYFNMRDALSSG-
---VEKPRILYALYFNMRD--SSDE
end gap
conserved
substitution
gap
mismatch
match
Slide by Vered Caspi, BGU

10. Examples Pairwise alignment servers:
LALIGN (
NEEDLE / WATER global and local PSA (

11. Pairwise sequence alignment
NEEDLE results for example:
EMBOSS_001 1 EEELTKPRLLWALYFNMRDALSSG 24
:.|||:|:||||||||:...
EMBOSS_ VEKPRILYALYFNMRDSSDE 20
Alternative:
:.|||:|:|||||||| ||.
EMBOSS_ VEKPRILYALYFNMRD--SSDE 20
small positive score
Score >1.0
mismatch
gap
identity

12. A different format you might find
Tyrosine (Y)
Tryptophan(W)
gap
identity
mismatch
Similarity

13. How do we find the best alignment?
One can find the optimal alignment by trying all possible alignments and choosing the best one.
There are two approaches to do that:
Graphically display the sequences in a way that will help us find the best alignment by eye
Let the computer compute a score for each possible alignment and choose the alignment with the highest score.

14. Sequence alignment process
Choose strategy
Compare DNA or protein sequences
Global or local alignment
Execute an algorithm to determine the optimal alignment of the sequences
Choose algorithm
Give parameters to the algorithm (gap penalties, scoring matrix)
Interpret the results
Is the alignment “good” (score, % identity)
Is it possible that the alignment was achieved by chance (statistical significance, e-value)
Does the alignment represent a true biological relationship between the sequence

15. Graphical representation of sequence alignment
Dot Plot

16. DotPlot of Sequences Mismatch Match Gap E L T K P R W A Y F N M D S GE L T K P R W A Y F N M D S G V n I
Mismatch
Match
Gap

17. Remove noise: Windows
Usually, one AA identity holds little biological meaning
We are interested in contiguous identities.
Window size
background noise
missed identites
Large
Small

18. Remove noise: Windows
For nucleotide sequences, there are only 4 possible letters so windows should be larger
For AA sequences, there are 20 possible letters and windows can be smaller.

19. Remove noise: Windows
Window size: 2
Window size: 3

20. DotPlots for detection of Repeats

21. Dotlet
An application for viewing dotplots.

22. Dot Plots
More on dot plots in the hands-on session.

23. Principles of sequence alignment

24. Major strategies for SA
Global alignment
attempt to align every residue in the sequences.
Local alignment
Identify regions of similarity within their larger sequence context.

25. Global vs. Local Alignment
Global alignment advantages:
Easy to understand, complete seqs. in output.
Checking minor differences between 2 seqs.
Finding polymorphisms between 2 seqs.
Local alignment advantages:
mRNA vs. Genomic DNA: introns/exons
Genes/proteins are modular
Finding repeat elements within 1 sequence.
Possible to determine E-values.

26. What degree of similarity between sequences indicates homology?
It has been shown empirically that protein sequences which can be aligned along 100 amino acids or more, where in the aligned region at least 35% of the amino acids are identical, are homologous.
Aligned residues
Orengo, Jones & Thornton (2003) “Bioinformatics. Genes, Proteins & Computers” BIOS. p. 30

27. What degree of similarity between sequences indicates homology?
Usually, PSA is used to identify or study close homologs (>35% identity).
Twilight zone: Seqs. with 25%-35% identity. They may have evolutionary relatedness, but this has to be checked carefully.
To study about evolutionary relatedness of more distant proteins, one has to apply more advanced methods such as multiple sequence alignment (MSA), profile searches, threading and so on. Some of these methods will be taught later in the course.

28. What degree of similarity between sequences indicates homology?
% Identity
Evolutionary distance
From Pevsner

29. Scoring a sequence alignment
Quantitative indication of the quality of the alignment.
Quantitative comparison of alignments in search algorithm.
Nucleotide sequence: Nucleotides are either identical or not.
Amino acid sequence: AAs may also be similar, i.e. close chemical properties.

30. Scoring a sequence alignment
The score depends on penalizing two kinds of differences between the sequences:
Point mutations (with a substitution matrix)
Indels (Gap penalties)
Slide by Vered Caspi, BGU

31. Amino Acid Substitution Matrices
We use a two-dimensional matrix (table) of 20 X 20, where each cell in the matrix contains a number indicating the similarity between a pair of amino acids.
Positive values indicate high similarity.
Negative values indicate low similarity.
We will see later how the matrices are developed.
Many different matrices exist.
Orengo, Jones & Thornton (2003) “Bioinformatics. Genes, Proteins & Computers” BIOS. Chapter 4

32. Amino Acid Substitution Matrices
A positive score is given to the more likely substitutions while a negative score is given to the less likely substitutions.
Every identity or substitution is assigned a score based on its observed frequencies in the alignment of related proteins.
Scores within a BLOSUM are log-odds scores that measure the log for the ratio of the likelihood of one AAs substituting another with a biological sense and the likelihood of the same AAs appearing by chance.
Orengo, Jones & Thornton (2003) “Bioinformatics. Genes, Proteins & Computers” BIOS. Chapter 4

33. Amino Acid Substitution Matrices
BLOCKS Database: multiply aligned ungapped segments corresponding to the most highly conserved regions of proteins
Substitution matrices are constructed by assembling a large and diverse sample of verified pairwise alignments (or multiple sequence alignments) of AAs.
Substitution matrices should reflect the true probabilities of mutations occurring through a period of evolution.
The two major types of substitution matrices are PAM and BLOSUM.
BLOSUM62
From Pevsner

34. Acid, acid amide and hydrophilic
BLOSUM62 Matrix
Small hydrophylic
Acid, acid amide and hydrophilic
Basic
Small hydrophobic
Aromatic

35. BLOSUM62 Substitution Matrix
Scoring Systems - Proteins
NCBI FieldGuide
A 4
R -1 5 N D C Q E G H I L K M F P S T W Y V X
A R N D C Q E G H I L K M F P S T W Y V X
Common amino acids have low weights
BLOSUM62 Substitution Matrix
Rare amino acids have high weights
Negative for less likely substitutions
Positive for more likely substitutions
From NCBI field guides

36. Gap penalties
The presence of a gap is ascribed more significance than the length of the gap. (Because a single mutational event may cause the insertion or deletion of more than one residue.)
Gap opening – penalty for presence of gap
Gap extension – penalty for gap length.
Example gap penalties:
Gap opening: -10
Gap extension: -0.5
Slide by Vered Caspi, BGU

37. Alignment scoring V D S - C Y H E L T G A 4 2 -10 -0.5 9 7 -2 3
Score= ( )-( ) = 16
Slide by Vered Caspi, BGU

38. Substitution Matrices
In principle, there are two approaches to construct an AA substitution matrix:
Based on careful study of the physico-chemical structures of the amino acid.
To use a more empirical approach, based on inspection of groups of proteins whom we know in advance to be homologous.
The second approach was found to give better results, and is the one used today in popular BLOSUM substitution matrices.
Slide by Vered Caspi, BGU

39. Substitution Matrices
Based on careful study of the physico-chemical structures of the amino acid.

40. Conservative Substitutions - Definition
Substitutions that
Conserve the physical and chemical properties of the amino acids
Limit disruptions in protein structure/function.
Orengo, Jones & Thornton (2003) “Bioinformatics. Genes, Proteins & Computers” BIOS. Chapter 4

41. Slide from S. Pietrokovsky

42. Percent accepted mutations matrix (PAMs)
A matrix of weights that is derived from how often different AAs replace other AAs in evolution.
Based on a database of 1,572 changes in 71 groups of closely related proteins.
PAM-1 would correspond to roughly 1% divergence in a protein (one amino acid replacement per hundred).

43. Percent accepted mutations matrix (PAMs)
To derive a mutational probability matrix for a protein sequence that has undergone N percent accepted mutations, a PAM-N matrix, the PAM-1 matrix is multiplied by itself N times.
This results in a family of scoring matrices. PAM matrices.
By trial and error it was found that for weighting purposes a PAM-250 matrix works well.

44. Percent accepted mutations matrix (PAMS)
original amino acid
replacement amino acid

45. Odds matrix
What? The ratio Ma,b/Pb: The probability that some AA a will change to AA b in some PAM interval.
Ma,b - The probability that the aligned pair a and b represent an authentic alignment.
Pb – The probability that residue b was aligned by chance (=the normalized frequency)

46. Normalized Frequencies of Amino Acids
Gly (G) 8.9%
Arg (R) 4.1%
Ala (A) 8.7%
Asn (N) 4.0%
Leu (L) 8.5%
Phe (F) 4.0%
Lys (K) 8.1%
Gln (Q) 3.8%
Ser (S) 7.0%
Ile (I) 3.7%
Val (V) 6.5%
His (H) 3.4%
Thr (T) 5.8%
Cys (C) 3.3%
Pro (P) 5.1%
Tyr (Y) 3.0%
Glu (E) 5.0%
Met (M) 1.5%
Asp (D) 4.7%
Trp (W) 1.0%

47. Log odds matrix
Why? Logarithms are easier to use for a scoring system. They allow us to sum the scores of aligned residues (rather than having to multiply them).

48. How do we go from mutation-probability to log-odds matrices?
The cells in a log odds matrix consist of an “odds ratio”:
the probability that an alignment is authentic
the probability that the alignment was random
The score S for an alignment of residues a,b is given by:
Example, for tryptophan (W):
S(W,W) = 10 log10 (0.55/0.010) = 17.4
Probability of alignment W-W: 0.55 (According to PAM250 matrix)
Probability of chance appearance of Trp: 0.01
Pevsner Page 57

49. Percent accepted mutations matrix (PAMS)
original amino acid
replacement amino acid

50. Normalized Frequencies of Amino Acids
Gly (G) 8.9%
Arg (R) 4.1%
Ala (A) 8.7%
Asn (N) 4.0%
Leu (L) 8.5%
Phe (F) 4.0%
Lys (K) 8.1%
Gln (Q) 3.8%
Ser (S) 7.0%
Ile (I) 3.7%
Val (V) 6.5%
His (H) 3.4%
Thr (T) 5.8%
Cys (C) 3.3%
Pro (P) 5.1%
Tyr (Y) 3.0%
Glu (E) 5.0%
Met (M) 1.5%
Asp (D) 4.7%
Trp (W) 1.0%

51. What do the numbers mean
in a log odds matrix?
S(W,W) = 10 log10 (0.55/0.01) = 17.4
A score of +17 for tryptophan (W) means that this alignment is 50 times more likely than a chance alignment of two Trp residues.
S(a,b) = 17
x = Probability of replacement (Mab/pb)
Then
10 log10 x = 17
log10 x = 1.7
x = = 50

52. What do the numbers mean in a log odds matrix?
A score of +2 : The AA replacement occurs 1.6 times as frequently as expected by chance.
A score of 0 : Replacement is as frequent as chance alignment.
A score of –10 : The correspondence of the two AAs in an alignment that accurately represents homology (evolutionary descent) is one tenth as frequent as the chance alignment of these AAs.

53. BLOSUM Matrices BLOSUM matrices are based on local alignments.
BLOSUM stands for Blocks Substitution Matrix.
BLOSUM62 is a matrix calculated from comparisons of sequences with more than 62% divergence.
BLOSUM matrix values are given as the log-odds scores (Same as PAM matrices)

54. Substitution matrices
Closely related proteins
Distant proteins
(“Twilight zone”)

55. Comparing protein sequences can be more informative than DNA
Protein is more informative (20 vs. 4 characters);
Many amino acids share related biophysical properties.
Codons are degenerate: changes in the third position often do not alter the amino acid that is specified.
Protein sequences offer a longer “look-back” time.
DNA sequences can be translated into protein and then used in pairwise alignments.
Slide by Jonathan Pevsner

56. Comparing protein sequences can be more informative than DNA
However, many times, DNA alignments are appropriate
to confirm the identity of a cDNA
to study noncoding regions of DNA
to study DNA polymorphisms
Slide by Jonathan Pevsner

57. Summary Graphical alignment: Dot plots Algorithmic alignment:
Global alignment (=“needle”)
Local alignment (=“water”)
For proteins: Based on substitution matrices:
PAM
BLOSUM