pairwise sequence alignment (psa) why and how. goals of sequence alignment from the alignment we can...

Pairwise Sequence Alignment(PSA)

Why and How

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. 21.12.2005

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.

Homology: common ancestry of genes

http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/Orthology.html

speciation

Homologous genes: orthologs and paralogs

Retinol-binding protein, human(NP_006735)

b-lactoglobulin, cow(P02754)

Slide from J. Pevsner - Page 42

Similar genespossible similarity in

structure/function

Homologous genes

EEELTKPRLLWALYFNMRDALSSG

VEKPRILYALYFNMRDSSDE

Pairwise Sequence Alignment

You can find a list of abbreviations at:http://en.wikipedia.org/wiki/Amino_acids#Table_of_standard_amino_acid_abbreviations_and_side_chain_properties

http://en.wikipedia.org/wiki/Amino_acids#Table_of_standard_amino_acid_abbreviations_and_side_chain_properties

http://en.wikipedia.org/wiki/Amino_acids#Table_of_standard_amino_acid_abbreviations_and_side_chain_properties

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.



EEELTKPRLLWALYFNMRDALSSG----VEKPRILYALYFNMRD--SSDE





EEELTKPRLLWALYFNMRDALSSG----VEKPRILYALYFNMRD--SSDE

end gap

mismatchmatch

conservedsubstitution

gap



Examples

• Pairwise alignment servers:– LALIGN (

http://www.ch.embnet.org/software/LALIGN_form.html)

– NEEDLE / WATER global and local PSA (http://www.ebi.ac.uk/Tools/emboss/align/index.html)

http://www.ch.embnet.org/software/LALIGN_form.html

http://www.ch.embnet.org/software/LALIGN_form.html

http://www.ebi.ac.uk/Tools/emboss/align/index.html

http://www.ebi.ac.uk/Tools/emboss/align/index.html

Pairwise sequence alignment

• NEEDLE results for example:EMBOSS_001 1 EEELTKPRLLWALYFNMRDALSSG 24

:.|||:|:||||||||:...

EMBOSS_001 1 VEKPRILYALYFNMRDSSDE 20

• Alternative:EMBOSS_001 1 EEELTKPRLLWALYFNMRDALSSG 24

:.|||:|:|||||||| ||.

EMBOSS_001 1 VEKPRILYALYFNMRD--SSDE 20

mismatch gapsmall

positive score

Score >1.0 identity

A different format you might find

mismatch Similarity

identity

Tyrosine (Y)

Tryptophan(W)

gap

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.

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

Graphical representation of sequence alignment

Dot Plot

DotPlot of Sequences E E E L T K P R L L W A L Y F N M R D A L S S G

V

E

K

P

R

I

L

Y

A

L

Y

F

N

M

R

D

S

S

D

E

Match

Mismatch

Gap

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


• 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.

Window size: 2

Window size: 3


DotPlots for detection of Repeats

Dotlet

• An application for viewing dotplots.

Dot Plots

• More on dot plots in the hands-on session.

Principles of sequence alignment

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.

• 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.

Global vs. Local Alignment

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.

Orengo, Jones & Thornton (2003) “Bioinformatics. Genes, Proteins & Computers” BIOS. p. 30

Aligned residues


• 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.


Evolutionary distance

% I

dent

ity

From Pevsner

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.

• The score depends on penalizing two kinds of differences between the sequences:– Point mutations (with a substitution matrix)– Indels (Gap penalties)


Scoring a sequence alignment

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


• 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.



• 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.

From Pevsner

BLOSUM62

BLOCKS Database: multiply aligned ungapped segments corresponding to the most highly conserved regions of proteins

BLOSUM62 Matrix

Small hydrophylic

Acid, acid amide and hydrophilic

Basic

Small hydrophobic

Aromatic

BLOSUM62 Substitution

Matrix

Common amino acids have low weights

A 4R -1 5 N -2 0 6D -2 -2 1 6C 0 -3 -3 -3 9Q -1 1 0 0 -3 5E -1 0 0 2 -4 2 5G 0 -2 0 -1 -3 -2 -2 6H -2 0 1 -1 -3 0 0 -2 8I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 A R N D C Q E G H I L K M F P S T W Y V X

Rare amino acids have high weights

Negative for less likely substitutions

Positive for more likely substitutions

Scoring Systems - Proteins

From NCBI field guides

NC

BI

Fie

ldG

uid

e

Gap penalties

Example gap penalties:Gap opening: -10Gap extension: -0.5


• 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.

Score= (4+2+4+9+7-2+3)-(10+0.5+0.5) = 16


Alignment scoring

penaltiesgapmismatchesidentitiesS ,

V D S - - - C Y H V

V E S L T G C Y A L

4 2 4 -10 -0.5 -0.5 9 7 -2 3

Substitution Matrices

• In principle, there are two approaches to construct an AA substitution matrix:1.Based on careful study of the physico-

chemical structures of the amino acid.2.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.


Substitution Matrices

1.Based on careful study of the physico-chemical structures of the amino acid.

Conservative Substitutions - Definition

• Substitutions that – Conserve the physical and chemical

properties of the amino acids – Limit disruptions in protein structure/function.


Slide from S. Pietrokovsky

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).

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.

Percent accepted mutations matrix (PAMS)

original amino acid

replacement amino acid

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)

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%

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).

b

ab

P

MbaS 10log10),(

Pevsner Page 57

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

b

ab

P

MbaS 10log10),(

Percent accepted mutations matrix (PAMS)

original amino acid

replacement amino acid

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%

What do the numbers meanin 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) = 17x = Probability of replacement (Mab/pb)Then10 log10 x = 17log10 x = 1.7

x = 101.7 = 50

What do the numbers meanin 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.

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)

Closely related proteins

Distant proteins(“Twilight zone”)

Substitution matrices

Slide by Jonathan Pevsner

Comparing protein sequencescan 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

Comparing protein sequencescan 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

Summary

• Graphical alignment: Dot plots

• Algorithmic alignment:– Global alignment (=“needle”)– Local alignment (=“water”)

• For proteins: Based on substitution matrices:– PAM– BLOSUM

pairwise sequence alignment (psa) why and how. goals of sequence alignment from the alignment we can...

Documents

sequence alignment slide

best alignment

identity slide

sequence alignment psa

sequence alignment process

goals of sequence alignment

local alignment

optimal alignment