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Pairwise sequence alignment

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**Pairwise alignment: 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

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**Pairwise alignment: protein sequences can be more informative than DNA**

Many times, DNA alignments are appropriate --to confirm the identity of a cDNA --to study noncoding regions of DNA --to study DNA polymorphisms --example: Neanderthal vs modern human DNA Query: 181 catcaactacaactccaaagacacccttacacccactaggatatcaacaaacctacccac 240 |||||||| |||| |||||| ||||| | ||||||||||||||||||||||||||||||| Sbjct: 189 catcaactgcaaccccaaagccacccct-cacccactaggatatcaacaaacctacccac 247

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**Definitions Similarity Identity Conservation**

The extent to which nucleotide or protein sequences are related. It is based upon identity plus conservation. Identity The extent to which two sequences are invariant. Conservation Changes at a specific position of an amino acid or (less commonly, DNA) sequence that preserve the physico-chemical properties of the original residue.

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**Pairwise alignment of retinol-binding protein**

and b-lactoglobulin 1 MKWVWALLLLAAWAAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDPEG 50 RBP . ||| | | | : .||||.:| : 1 ...MKCLLLALALTCGAQALIVT..QTMKGLDIQKVAGTWYSLAMAASD. 44 lactoglobulin 51 LFLQDNIVAEFSVDETGQMSATAKGRVR.LLNNWD..VCADMVGTFTDTE 97 RBP : | | | | :: | .| . || |: || |. 45 ISLLDAQSAPLRV.YVEELKPTPEGDLEILLQKWENGECAQKKIIAEKTK 93 lactoglobulin 98 DPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAV QYSC 136 RBP || || | :.|||| | | 94 IPAVFKIDALNENKVL VLDTDYKKYLLFCMENSAEPEQSLAC 135 lactoglobulin 137 RLLNLDGTCADSYSFVFSRDPNGLPPEAQKIVRQRQ.EELCLARQYRLIV 185 RBP . | | | : || | || | 136 QCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI lactoglobulin

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**Gaps • Positions at which a letter is paired with a null**

are called gaps. • Gap scores are typically negative. • Since a single mutational event may cause the insertion or deletion of more than one residue, the presence of a gap is ascribed more significance than the length of the gap. Thus there are separate penalties for gap creation and gap extension. • In BLAST, it is rarely necessary to change gap values from the default.

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**Outline: pairwise alignment**

Overview and examples Definitions: homologs, paralogs, orthologs Assigning scores to aligned amino acids: Dayhoff’s PAM matrices Alignment algorithms: Needleman-Wunsch, Smith-Waterman Statistical significance of pairwise alignments

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**General approach to pairwise alignment**

Choose two sequences Select an algorithm that generates a score Allow gaps (insertions, deletions) Score reflects degree of similarity Alignments can be global or local Estimate probability that the alignment occurred by chance

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**Calculation of an alignment score**

Source:

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PAM250 log odds scoring matrix Page 58

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PAM10 log odds scoring matrix Page 59

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Page 54

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**Dayhoff’s numbers of “accepted point mutations”: **

what amino acid substitutions occur in proteins? Page 52

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**Dayhoff’s mutation probability matrix **

for the evolutionary distance of 1 PAM We have considered three kinds of information: a table of number of accepted point mutations (PAMs) relative mutabilities of the amino acids normalized frequencies of the amino acids in PAM data This information can be combined into a “mutation probability matrix” in which each element Mij gives the probability that the amino acid in column j will be replaced by the amino acid in row i after a given evolutionary interval (e.g. 1 PAM). Page 50

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**Dayhoff’s PAM1 mutation probability matrix**

Original amino acid Page 55

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**Dayhoff’s PAM1 mutation probability matrix**

Each element of the matrix shows the probability that an original amino acid (top) will be replaced by another amino acid (side)

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**Substitution Matrix A substitution matrix contains values proportional**

to the probability that amino acid i mutates into amino acid j for all pairs of amino acids. Substitution matrices are constructed by assembling a large and diverse sample of verified pairwise alignments (or multiple sequence alignments) of amino acids. 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.

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**Point-accepted mutations**

PAM matrices: Point-accepted mutations PAM matrices are based on global alignments of closely related proteins. The PAM1 is the matrix calculated from comparisons of sequences with no more than 1% divergence. At an evolutionary interval of PAM1, one change has occurred over a length of 100 amino acids. Other PAM matrices are extrapolated from PAM1. For PAM250, 250 changes have occurred for two proteins over a length of 100 amino acids. All the PAM data come from closely related proteins (>85% amino acid identity).

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**Why do we go from a mutation probability matrix to a log odds matrix?**

We want a scoring matrix so that when we do a pairwise alignment (or a BLAST search) we know what score to assign to two aligned amino acid residues. 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). Page 57

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**How do we go from a mutation probability matrix to a log odds matrix?**

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: S(a,b) = 10 log10 (Mab/pb) As an example, for tryptophan, S(a,tryptophan) = 10 log10 (0.55/0.010) = 17.4 Page 57

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**Normalized frequencies of amino acids**

Arg 4.1% Asn 4.0% Phe 4.0% Gln 3.8% Ile 3.7% His 3.4% Cys 3.3% Tyr 3.0% Met 1.5% Trp 1.0%

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**What do the numbers mean**

in a log odds matrix? S(a,tryptophan) = 10 log10 (0.55/0.010) = 17.4 A score of +17 for tryptophan means that this alignment is 50 times more likely than a chance alignment of two Trp residues. S(a,b) = 17 Probability of replacement (Mab/pb) = x Then 17 = 10 log10 x 1.7 = log10 x 101.7 = x = 50 Page 58

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**What do the numbers mean**

in a log odds matrix? A score of +2 indicates that the amino acid replacement occurs 1.6 times as frequently as expected by chance. A score of 0 is neutral. A score of –10 indicates that the correspondence of two amino acids in an alignment that accurately represents homology (evolutionary descent) is one tenth as frequent as the chance alignment of these amino acids. Page 58

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PAM250 log odds scoring matrix Page 58

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PAM10 log odds scoring matrix Page 59

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More conserved Less conserved Rat versus mouse RBP Rat versus bacterial lipocalin

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**Comparing two proteins with a PAM1 matrix **

gives completely different results than PAM250! Consider two distantly related proteins. A PAM40 matrix is not forgiving of mismatches, and penalizes them severely. Using this matrix you can find almost no match. hsrbp, 136 CRLLNLDGTC btlact, 3 CLLLALALTC * ** * ** A PAM250 matrix is very tolerant of mismatches. 24.7% identity in 81 residues overlap; Score: 77.0; Gap frequency: 3.7% rbp4 26 RVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDV btlact 21 QTMKGLDIQKVAGTWYSLAMAASD-ISLLDAQSAPLRVYVEELKPTPEGDLEILLQKWEN * **** * * * * ** * rbp CADMVGTFTDTEDPAKFKM btlact 80 GECAQKKIIAEKTKIPAVFKI ** * ** ** Page 60

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**two nearly identical proteins**

two distantly related proteins

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**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 no less than 62% divergence. Page 60

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**BLOSUM Matrices 100 collapse Percent amino acid identity 62 30**

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**BLOSUM Matrices 100 100 100 collapse collapse 62 62 62 collapse**

Percent amino acid identity 30 30 30 BLOSUM80 BLOSUM62 BLOSUM30

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**BLOSUM Matrices All BLOSUM matrices are based on observed alignments;**

they are not extrapolated from comparisons of closely related proteins. The BLOCKS database contains thousands of groups of multiple sequence alignments. BLOSUM62 is the default matrix in BLAST 2.0. Though it is tailored for comparisons of moderately distant proteins, it performs well in detecting closer relationships. A search for distant relatives may be more sensitive with a different matrix. Page 60

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Blosum62 scoring matrix Page 61

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Blosum62 scoring matrix Page 61

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Rat versus mouse globin Rat versus bacterial globin Page 61

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**Two randomly diverging protein sequences change **

in a negatively exponential fashion Percent identity “twilight zone” Evolutionary distance in PAMs Page 62

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**At PAM1, two proteins are 99% identical **

At PAM10.7, there are 10 differences per 100 residues At PAM80, there are 50 differences per 100 residues At PAM250, there are 80 differences per 100 residues Percent identity “twilight zone” Differences per 100 residues Page 62

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**PAM: “Accepted point mutation”**

Two proteins with 50% identity may have 80 changes per 100 residues. (Why? Because any residue can be subject to back mutations.) Proteins with 20% to 25% identity are in the “twilight zone” and may be statistically significantly related. PAM or “accepted point mutation” refers to the “hits” or matches between two sequences (Dayhoff & Eck, 1968) Page 62

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**Ancestral sequence Sequence 1 ACCGATC Sequence 2 AATAATC ACCCTAC**

A no change A C single substitution C --> A C multiple substitutions C --> A --> T C --> G coincidental substitutions C --> A T --> A parallel substitutions T --> A A --> C --> T convergent substitutions A --> T C back substitution C --> T --> C Sequence 1 ACCGATC Sequence 2 AATAATC Li (1997) p.70

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**Percent identity between two proteins: What percent is significant?**

100% 80% 65% 30% 23% 19% We will see in the BLAST lecture that it is appropriate to describe significance in terms of probability (or expect) values. As a rule of thumb, two proteins sharing > 30% over a substantial region are usually homologous.

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**Outline: pairwise alignment**

Overview and examples Definitions: homologs, paralogs, orthologs Assigning scores to aligned amino acids: Dayhoff’s PAM matrices Alignment algorithms: Needleman-Wunsch, Smith-Waterman Statistical significance of pairwise alignments

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**An alignment scoring system is required **

to evaluate how good an alignment is • positive and negative values assigned • gap creation and extension penalties • positive score for identities • some partial positive score for conservative substitutions • global versus local alignment • use of a substitution matrix

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**Calculation of an alignment score**

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**Two kinds of sequence alignment:**

global and local We will first consider the global alignment algorithm of Needleman and Wunsch (1970). We will then explore the local alignment algorithm of Smith and Waterman (1981). Finally, we will consider BLAST, a heuristic version of Smith-Waterman. We will cover BLAST in detail on Monday. Page 63

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**Global alignment with the algorithm of Needleman and Wunsch (1970)**

• Two sequences can be compared in a matrix along x- and y-axes. • If they are identical, a path along a diagonal can be drawn • Find the optimal subpaths, and add them up to achieve the best score. This involves --adding gaps when needed --allowing for conservative substitutions --choosing a scoring system (simple or complicated) N-W is guaranteed to find optimal alignment(s) Page 63

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**Three steps to global alignment with the Needleman-Wunsch algorithm**

[1] set up a matrix [2] score the matrix [3] identify the optimal alignment(s) Page 63

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**Four possible outcomes in aligning two sequences**

1 2 [1] identity (stay along a diagonal) [2] mismatch (stay along a diagonal) [3] gap in one sequence (move vertically!) [4] gap in the other sequence (move horizontally!) Page 64

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Page 64

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**Fill in the matrix starting from the bottom right**

Page 65

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Page 66

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**Page 66 sequence 1 ABCNJ-RQCLCR-PM sequence 2 AJC-JNR-CKCRBP-**

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**Needleman-Wunsch: dynamic programming**

N-W is guaranteed to find optimal alignments, although the algorithm does not search all possible alignments. It is an example of a dynamic programming algorithm: an optimal path (alignment) is identified by incrementally extending optimal subpaths. Thus, a series of decisions is made at each step of the alignment to find the pair of residues with the best score. Page 67

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Try using needle to implement a Needleman-Wunsch global alignment algorithm to find the optimum alignment (including gaps):

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Queries: beta globin (NP_000509) alpha globin (NP_000549)

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**Global alignment versus local alignment**

Global alignment (Needleman-Wunsch) extends from one end of each sequence to the other. Local alignment finds optimally matching regions within two sequences (“subsequences”). Local alignment is almost always used for database searches such as BLAST. It is useful to find domains (or limited regions of homology) within sequences. Smith and Waterman (1981) solved the problem of performing optimal local sequence alignment. Other methods (BLAST, FASTA) are faster but less thorough. Page 69

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**Rapid, heuristic versions of Smith-Waterman:**

FASTA and BLAST Smith-Waterman is very rigorous and it is guaranteed to find an optimal alignment. But Smith-Waterman is slow. It requires computer space and time proportional to the product of the two sequences being aligned (or the product of a query against an entire database). Gotoh (1982) and Myers and Miller (1988) improved the algorithms so both global and local alignment require less time and space. FASTA and BLAST provide rapid alternatives to S-W. Page 71

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**How FASTA works [1] A “lookup table” is created. It consists of short**

stretches of amino acids (e.g. k=3 for a protein search). The length of a stretch is called a k-tuple. The FASTA algorithm finds the ten highest scoring segments that align to the query. [2] These ten aligned regions are re-scored with a PAM or BLOSUM matrix. [3] High-scoring segments are joined. [4] Needleman-Wunsch or Smith-Waterman is then performed. Page 72

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**Outline: pairwise alignment**

Overview and examples Definitions: homologs, paralogs, orthologs Assigning scores to aligned amino acids: Dayhoff’s PAM matrices Alignment algorithms: Needleman-Wunsch, Smith-Waterman Statistical significance of pairwise alignments

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**Statistical significance of pairwise alignment**

We will discuss the statistical significance of alignment scores in the next lecture (BLAST). A basic question is how to determine whether a particular alignment score is likely to have occurred by chance. According to the null hypothesis, two aligned sequences are not homologous (evolutionarily related). Can we reject the null hypothesis at a particular significance level alpha?

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**True positives False positives False negatives True negatives Page 76**

Sequences reported as related True positives False positives Sequences reported as unrelated False negatives True negatives Page 76

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**homologous sequences non-homologous sequences**

Sequences reported as related True positives False positives Sequences reported as unrelated False negatives True negatives Page 76

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**homologous sequences non-homologous sequences True positives**

Sequences reported as related True positives False positives Sequences reported as unrelated False negatives True negatives Sensitivity: ability to find true positives Specificity: ability to minimize false positives

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**Randomization test: scramble a sequence**

• First compare two proteins and obtain a score RBP: RVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWD- 84 + K GTW++MA L + A V T L+ W+ glycodelin: QTKQDLELPKLAGTWHSMAMA-TNNISLMATLKAPLRVHITSLLPTPEDNLEIVLHRWEN 81 • Next scramble the bottom sequence 100 times, and obtain 100 “randomized” scores (+/- standard deviation) • Composition and length are maintained • If the comparison is “real” we expect the authentic score to be several standard deviations above the mean of the “randomized” scores Page 76

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**A randomization test shows that RBP **

is significantly related to b-lactoglobulin 100 random shuffles Mean score = 8.4 Std. dev. = 4.5 Number of instances Real comparison Score = 37 Quality score But this test assumes a normal distribution of scores! In the BLAST lecture we will consider the distribution of scores and more appropriate significance tests. Page 77

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**Z = (Sreal – Xrandomized score) standard deviation**

You can perform this randomization test using the UVA FASTA programs (http://fasta.bioch.virginia.edu/). Z = (Sreal – Xrandomized score) standard deviation

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**The PRSS program performs**

a scramble test for you (http://fasta.bioch.virginia.edu /fasta/prss.htm) Bad scores Good scores But these scores are not normally distributed!

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