pairwise alignment prelab.pdf

7/27/2019 Pairwise Alignment prelab.pdf

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PAIRWISE ALIGNMENT


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SEQUENCES ARE RELATED

Darwin: all organisms

are related through

descent withmodification

Sequences are related

through descent with

modification Similar molecules have

similar functions in

different organisms

Phylogenetic tree based onribosomal RNA: three domains of life


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WHY COMPARE SEQUENCES?

To determine evolutionary

relationships

To decide if two proteins

(or genes) are related

structurally or functionally

Protein 1: binds oxygen

Sequence similarity

Protein 2: binds oxygen ?


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WHY COMPARE SEQUENCES?

To identify domains or motifs that are shared between

proteins


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TERMINOLOGIES

Similaritythe extent to which nucleotide or protein

sequences are related. It is based upon identity plus

conservation. Identitythe extent to which two sequences are invariant.


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TERMINOLOGIES

Conservationchanges at a specific position of an amino

acid (or less commonly, DNA) sequences that preserve the

physicochemical properties of the original residue.


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CONSERVED RESIDUES

Residues conserved among various G protein coupled

receptors are highlighted in green


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CONSERVATION OF FUNCTION

Alignments can reveal which parts of the sequences arelikely to be important for the function, if the proteins are

involved in similar processes. In parts of the sequence of a protein which are not very

critical for its function, random mutations can easilyaccumulate.

In parts of the sequence that are critical for the function of

the protein, hardly any mutations will be accepted; nearly allchanges in such regions will destroy the function.


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INSERTIONS/DELETIONS AND

PROTEIN STRUCTURE

loop structures: insertions/deletions

here not so significant

Why is it that two similar sequences may have large

insertions/deletions?

some insertions and deletions may not significantlyaffect the structure of a protein


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COMPARING THE PROTEIN KINASE KRAF_HUMAN AND THE

UNCHARACTERIZED O22558 FROM ARABIDOPSIS USING BLAST546 AA

Score = 185 bits (464), Expect = 1e-45

Identities = 107/283 (37%), Positives = 172/283 (59%), Gaps = 15/283 (5%)

Query: 337 DSSYYWEIEASEVMLSTRIGSGSFGTVYKGKWHG-DVAVKILKVVDPTPEQFQAFRNEVA 395

D + WEI+ +++ + ++ SGS+G +++G + +VA+K LK E + F EV

Sbjct: 274 DGTDEWEIDVTQLKIEKKVASGSYGDLHRGTYCSQEVAIKFLKPDRVNNEMLREFSQEVF 333

Query: 396 VLRKTRHVNILLFMGYMTKD-NLAIVTQWCEGSSLYKHLHVQETKFQMFQLIDIARQTAQ 454

++RK RH N++ F+G T+ L IVT++ S+Y LH Q+ F++ L+ +A A+

Sbjct: 334 IMRKVRHKNVVQFLGACTRSPTLCIVTEFMARGSIYDFLHKQKCAFKLQTLLKVALDVAK 393

Query: 455 GMDYLHAKNIIHRDMKSNNIFLHEGLTVKIGDFGLATVKSRWSGSQQVEQPTGSVLWMAP 514

GM YLH NIIHRD+K+ N+ + E VK+ DFG+A V+ SG E TG+ WMAP

Sbjct: 394 GMSYLHQNNIIHRDLKTANLLMDEHGLVKVADFGVARVQIE-SGVMTAE--TGTYRWMAP 450

Query: 515 EVIRMQDNNPFSFQSDVYSYGIVLYELMTGELPYSHINNRDQIIFMVGRGYASPDLSKLY 574

EVI ++ P++ ++DV+SY IVL+EL+TG++PY+ + + +V +G P + K

Sbjct: 451 EVI---EHKPYNHKADVFSYAIVLWELLTGDIPYAFLTPLQAAVGVVQKG-LRPKIPK-- 504

Query: 575 KNCPKAMKRLVADCVKKVKEERPLFPQILSSIELLQHSLPKIN 617

K PK +K L+ C + E+RPLF +I IE+LQ + ++N

Sbjct: 505 KTHPK-VKGLLERCWHQDPEQRPLFEEI---IEMLQQIMKEVN 543


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SIMILARITY AND HOMOLOGY

Similaritycan be expressed as a percentage. It does not imply

any reasons for the observed sameness.

Homologyis an evolutionary term used to describerelationship via descent from a common ancestor.

Homologous things are often similar, but not always

(e.g. whale flipper human arm)

Homology is NEVER expressed as a percentage


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HOMOLOGY

Homologous sequences can be divided into three groups:

Orthologous sequencessequences that diverged due to

a speciation event (e.g. human -globin and mouse -globin).

Paralogous sequencessequences that diverged due to a

gene duplication event (e.g. human -globin and human

-globin, various versions of both).

Xenologous sequencessequences for which the history

of one of them involves interspecies transfer since the time

of their common ancestor.


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HOMOLOGY


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SIMILARITY AND HOMOLOGY

Sequence homology can be reliably inferred from

statistically significant similarity over a majority of the

sequence length. Non-homology CANNOT be inferred from non-similarity

because non-similar things can still share a common

ancestor.

Homologous proteins share common structures, but notnecessarily common sequence or function

Homology is all or nothing. There is no such thing as 50%

homology


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QUESTION 1

True or False. Homology is synonymous with similarity


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SEARCHING SEQUENCE DATABASES

When we search a sequence database, we are usually

looking for related sequences.

Unfortunately, the algorithms that we have for searchingdatabases, do not search for homology, they search for

similarity.

When similarity is found, we must determine if this similarity

is a result of homology or if it comes from another source.


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WHY SEARCH FOR SIMILARITY?

I have just sequenced something. What is known about the

thing I sequenced?

I have a unique sequence. Is there similarity to another genethat has a known function?

I found a new protein in a lower organism. Is it similar to a

protein from another species?

I have decided to work on a new gene. The people in thefield will not give me the plasmid. I need the complete

cDNA sequence to perform RT-PCR or some other

experiment.


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SEQUENCE ALIGNMENT: DEFINITION

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 thedegree of similarity and the possibility of homology.


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SEQUENCE ALIGNMENT

Comparing sequences provides information as to which

genes or proteins have the same function

Sequences are compared by aligning themsliding themalong each other to find the most matches with a few gaps

An alignment can be scoredcount matches, and can

penalize mismatches and gaps


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QUESTION 2

Whenever possible, it is better to

A. Compare proteins than to compare genes

B. Compare genes than to compare proteins

Discuss as a group and cite points that defend your argument


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IT IS MUCH EASIER TO ALIGN PROTEINS

4 DNA bases vs. 20 amino acidsless chance similarity

There are varying degrees of similarity between different

AAs

Protein databanks are much smaller than DNA databanks


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PAIRWISE ALIGNMENT

The alignment of two sequences (DNA or protein) is a

relatively straightforward computational problem.

Two sequences can always be aligned. Sequence alignments have to be scored.

Often there is more than one solution with the same

score.


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PAIRWISE ALIGNMENTS: PURPOSE

identification of sequences with significant similarity to (a)

sequence(s) in a sequence repository

identification of all homologous sequences within therepository

identification of domains with sequence similarity


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METHODS OF ALIGNMENT

By handslide sequences on two lines of a word processor

Dot plot

Rigorous mathematical approach

Dynamic programming (slow, optimal)

Heuristic methods (fast, approximate)

BLAST and FASTA (uses word matching and hash tables)


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ALIGNMENT BY HAND

GATCGCCTA_TTACGTCCTGGAC AGGCATACGTA_GCCCTTTCGC

A scoring system is still essential to find the best alignment


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DOTPLOTS

Not technically an

alignment

Gives picture ofcorrespondence between

pairs of sequences

Dot represents similarity

between segments of thetwo sequences


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QUESTION 3

Do diagonals correspond

to conserved regions?

A. Yes

B. No


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QUESTION 3 REDUX

Take note that the dots

are placed at grid points

where two sequenceshave identical residues.

Do diagonals correspond

to conserved regions?

A. Yes

B. No


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A LIMITATION TO DOT MATRIX COMPARISON

Where part of one sequence shares a long stretch ofsimilarity with the other sequence, a diagonal of dots will beevident in the matrix.

However, when single bases are compared at each position,most of the dots in the matrix will be due to backgroundsimilarity.

That is, for any two nucleotides compared between the two

sequences, there is a 1 in 4 chance of a match, assumingequal frequencies of A,G,C and T.


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SIMPLE DOT PLOT

G G C T T G A C C G G

G

G

A

T

T

G

A

C

C

C

G


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A SOLUTION

This background noise can be filtered out by comparing

groups oflnucleotides, rather than single nucleotides, at

each position. For example, if we compare dinucleotides (l= 2), the

probability of two dinucleotides chosen at random from

each sequence matching is 1/16, rather than 1/4.

Therefore, the number of background matches will be lower:


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A FILTERED DOT PLOT


G

G

A

T

T

G

A

C

C

C

G


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THE DOT MATRIX ALGORITHM

The dot-matrix algorithm can be generalized for sequences sand t of sizes m and n, respectively, and window size l.

For each position in sequence s, compare a window oflnucleotides centered at that position with each window oflnucleotides in sequence t.

Conceptually, you can think of windows of length lslidingalong each axis, so that all possible windows oflnucleotidesare compared between the two sequences.


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I = 3


G

G

A

T

T

G

A

C

C

C

G


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DOT MATRIX SEQUENCE

COMPARISON EXAMPLES


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COMPARING A PROTEIN WITH ITSELF

Proteins can be compared with themselves to show internal

duplications or repeating sequences.

A self-matrix produces a central diagonal line through theorigin, indicating an exact match between the x and y axes.

The parallel diagonals that appear off the central line are

indicative of repeated sequence elements in different

locations of the same protein.


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HAPTOGLOBIN Haptoglobin is a protein that is secreted into the blood by the

liver. This protein binds free hemoglobin.

The concentration of "free" hemoglobin (that is, outside redblood cells) in plasma (the fluid portion of blood) is ordinarilyvery low.

However, free hemoglobin is released when red blood cellshemolyze for any reason.

After haptoglobin binds hemoglobin, it is taken up by the liver.

The liver recycles the iron, heme, and amino acids contained inthe hemoglobin protein.


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OUR COMPARISON

Files used

1006264A Haptoglobin H2

DNA sequencing shows that the intragenic duplication withinthe human haptoglobin Hp2 allele was formed by a non-

homologous, probably random, crossing-over within different

introns of two Hp1 genes.

A repeated sequence (starting with ADDGCP...) is observedbeginning at positions 30-90 and 90-150 - probably due to a

duplication event in one of these locations.


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Window: 30 Stringency: 3

Blosum 62 matrix


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SEARCHING FOR REPEATS IN DOTPLOTS

One of the strengths of dot-matrix searches is that they

make repeats easy to detect by comparing a sequence

against itself. In self comparisons, direct repeats appear as diagonals

parallel to the main line of identity.


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COMPARISON OF TWO SIMILAR SEQUENCES Files Used:

P03035

Repressor protein from E. coliPhage p22

RPBPL Repressor protein from E. coliphage Lambda

Lambda phages infect E. coli. They can be lytic and destroysthe host cell, making hundreds of progeny.

They can also be lysogenic, and live quietly within the DNA

of the bacteria. A gene makes the repressor protein that prevents the phage

from going destructively lytic.

Phage p22 is a related phage that also makes a repressor.

Both proteins form a dimer and bind DNA to prevent lysis.


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LAMBDA REPRESSOR/OPERATOR COMPLEX (1LMB)


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DOT MATRIX SEQUENCE COMPARISON

A row of dots represents a region of sequence similarity.

Background matching also appears as scattered dots.


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Blosum 62 matrix


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Blosum 62 matrix


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Blosum 62 matrix


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Blosum 62 matrix


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QUESTION 4

Which of the following combinations of parameters will

produce the least background noise?

A. Low window, low stringency

B. Low window, high stringency

C. High window, low stringency

D. High window, high stringency


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DISADVANTAGES TO DOT PLOTS

While dot-matrix searches provide a great deal of

information in a visual fashion, they can only be considered

semi-quantitative, and therefore do not lend themselves tostatistical analysis.

Also, dot-matrix searches do not provide a precise

alignment between two sequences.


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RIGOROUS ALGORITHMSDYNAMIC PROGRAMMING


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ALGORITHM

An algorithm is a complete, unambiguous procedure for

solving a specified problem in a finite number of steps.

Algorithms leave nothing undefined and require no intuitionto achieve their end.


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FIVE FEATURES OF AN ALGORITHM:

An algorithm must stop after a finite number of steps.

All steps of the algorithm must be precisely defined.

Input to the algorithm must be specified.

Output of the algorithm must be specified. There must be at

least one output.

An algorithm must be effective - i.e. its operations must be

basic and doable.


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DYNAMIC PROGRAMMING

Algorithmic technique for optimization problems that have

two properties:

Optimal substructure: Optimal solution can be computed from optimalsolutions to subproblems

Overlapping subproblems: Subproblems overlap such that the total

number of distinct subproblems to be solved is relatively small

1

3

2

7

6

8

5

4


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RIGOROUS ALGORITHMS

Needleman-Wunsch (Global)

Smith-Waterman (Local)


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GLOBAL VS. LOCAL ALIGNMENT

Global alignment algorithms start at the beginning of two

sequences and add gaps to each until the end of one is

reached.

Local alignment algorithms finds the region (or regions)

of highest similarity between two sequences and build thealignment outward from there.


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GLOBAL VS. LOCAL ALIGNMENT


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GLOBAL ALIGNMENT

The Needleman-Wunsch algorithm creates a globalalignment over the length of both sequences (needle)

Global algorithms are often not effective for highly divergedsequences - do not reflect the biological reality that twosequences may only share limited regions of conservedsequence.

Sometimes two sequences may be derived from ancient

recombination events where only a single functional domain is shared. Global methods are useful when you want to force two

sequences to align over their entire length


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LOCAL ALIGNMENT

Identify the most similar sub-region shared between two

sequences

There is no attempt to force entire sequences into analignment, just those parts that appear to have good

similarity, according to some criterion.

Smith-Waterman (water)


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LOCAL ALIGNMENTS

It may seem that one should always use local alignments.

However, it may be difficult to spot an overall similarity, as

opposed to just a domain-to-domain similarity, if one usesonly local alignment.

So global alignment is useful in some cases.

The popular programs BLAST and FASTA for searching

sequence databases produce local alignments.


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GAPS AND INSERTIONS

In an alignment, much better correspondence can beobtained between two sequences if a gap can be introducedin one sequence.

Alternatively, an insertion could be allowed in the othersequence.

Biologically, this corresponds to a mutation event thateliminates a part of a gene, or introduces new DNA into a

gene.


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GAPS

Positions at which a letter is paired with a null are called

gaps.

Gap scores are typically negative.


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QUESTION 5

Which is more significant? The presence of a gap or the

length of a gap?

A. The presence of a gap

B. The length of a gap


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GAPS

Since a single mutational event may cause the insertion or

deletion of more than one residue, the presence of a gap is

considered more significant than the length of the gap.


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OPTIMAL ALIGNMENT

The alignment that is the best, given a defined set ofrules and parameter values for comparing different

alignments. There is no such thing as the single best

alignment, since optimality always depends on theassumptions one bases the alignment on.

For example, what penalty should gaps carry?

All sequence alignment procedures make some suchassumptions.


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PARAMETERS OF SEQUENCE ALIGNMENT

Scoring systems:

Each symbol pairing is assigned a numerical value, based on asymbol comparison table.

Gap penalties:

Opening: The cost to introduce a gap

Extension: The cost to elongate a gap


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DNA SCORING SYSTEMSVERY SIMPLE

Match: 1

Mismatch: 0

Score = 5


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PROTEIN SCORING SYSTEMS


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Amino acids have different biochemical and physical

properties that influence their relative replaceability in

evolution.


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Scoring matrices reflect:

Number of mutations to convert one to another

Chemical similarity Observed mutation frequencies

The probability of occurrence of each amino acid

Widely used scoring matrices:

PAM

BLOSUM


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PAM MATRICES

Point Accepted Mutation

Family of matrices: PAM 80, PAM 120, PAM 250

The number with a PAM matrix represents the evolutionarydistance between the sequences on which the matrix is

based.

PAM 250 = 250 mutations per 100 residues

Greater numbers denote greater evolutionary distance


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PAM MATRICES

Derived from global alignments ofprotein families. Family

members share at least 85% identity

Construction of phylogenetic tree and ancestral sequences

of each protein family

Computation of number of replacements for each pair ofamino acids


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PAM 250 MATRIX


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PAMLIMITATIONS

Based on only one original dataset

Based mainly on small globular proteins so the matrix is biased

Examines proteins with few differences (85% identity)


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BLOSUM MATRICES

BLOcks SUbstitution Matrix

Derived from alignments of domains of distantly related

proteins Different BLOSUMn matrices are calculated independently

from blocks (ungapped local alignments)

BLOSUMn is based on a cluster of BLOCKS of sequences

that share at least n percent identity BLOSUM 62 represents closer sequences than BLOSUM 45


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BLOSUM MATRICES

Built from BLOCKS database: from the most conservedregions of aligned sequences

~2000 blocks from 500 families have been used BLOSUM 62 is the most popular matrix and is the default

matrix for the standard BLAST program.


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BLOSUM 50 MATRIX

A 5

R -2 7

N -1 -1 7

D -2 -2 2 8

C -1 -4 -2 -4 13Q -1 1 0 0 -3 7

E -1 0 0 2 -3 2 6

G 0 -3 0 -1 -3 -2 -3 8

H -2 0 1 -1 -3 1 0 -2 10

I -1 -4 -3 -4 -2 -3 -4 -4 -4 5

L -2 -3 -4 -4 -2 -2 -3 -4 -3 2 5

K -1 3 0 -1 -3 2 1 -2 0 -3 -3 6M -1 -2 -2 -4 -2 0 -2 -3 -1 2 3 -2 7

F -3 -3 -4 -5 -2 -4 -3 -4 -1 0 1 -4 0 8

P -1 -3 -2 -1 -4 -1 -1 -2 -2 -3 -4 -1 -3 -4 10

S 1 -1 1 0 -1 0 -1 0 -1 -3 -3 0 -2 -3 -1 5

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 2 5

W -3 -3 -4 -5 -5 -1 -3 -3 -3 -3 -2 -3 -1 1 -4 -4 -3 15

Y -2 -1 -2 -3 -3 -1 -2 -3 2 -1 -1 -2 0 4 -3 -2 -2 2 8

V 0 -3 -3 -4 -1 -3 -3 -4 -4 4 1 -3 1 -1 -3 -2 0 -3 -1 5

A R N D C Q E G H I L K M F P S T W Y V

Positive scores on diagonal

(identities)

Similar residues get higher

(positive) scores

Dissimilar residues get smaller

(negative) scores


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QUESTION 6

Which is the appropriate matrix to use when comparing

highly divergent sequences?

A. BLOSUM with a lower n

B. PAM with lower n

C. BLOSUM with a higher n

D. Both B and C


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PAM VS. BLOSUM

PAM 100 = BLOSUM 90

PAM 120 = BLOSUM 80

PAM 160 = BLOSUM 60PAM 200 = BLOSUM 52

PAM 250 = BLOSUM 45

More distant

sequences

PAM 120 for general use BLOSUM 62 for general use

PAM 160 for close relations BLOSUM 80 for close relations

PAM 250 for distant relations BLOSUM 45 for distant relations


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TIPS ON CHOOSING A SCORING MATRIX

Generally, BLOSUM matrices perform better than PAM

matrices for local similarity searches (Henikoff & Henikoff,

1993).

When comparing closely related proteins one should use

lower PAM or higher BLOSUM matrices, for distantly

related proteins higher PAM or lower BLOSUM matrices.

For database searching the commonly used matrix isBLOSUM62.


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Lower BLOSUM series meansmore divergence

Higher PAM series meansmore divergence

better for finding local

alignments

better for finding global

alignments and remotehomologs

based on groups of relatedsequences counted as one

based on minimumreplacement or maximumparsimony

Built from vast amout of dataBuilt from small amout ofdata

Built from local alignmentsBuilt from global alignments

BLOSUMPAM


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SCORING INSERTIONS AND DELETIONS

The creation of a gap is penalized with a negative score

value


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WHY GAP PENALTIES?


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WHY GAP PENALTIES?

The optimal alignment of two similar sequences is usually

that which

Maximizes the number of matches and Minimizes the number of gaps

Permitting the insertion of arbitrarily many gaps can lead to

high scoring alignments of non-homologous sequences.

Penalizing gaps forces alignments to have relatively few gaps.


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BALANCING GAPS WITH MISMATCHES

Gaps must get a steep penalty, or else youll end up with

nonsense alignments.

In real sequences, multi-base (or amino acid) gaps are quitecommon

Affine gap penalties give a big penalty for each new gap,

but a much smaller gap extension penalty.


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SCORING INSERTIONS AND DELETIONS

pairwise alignment prelab.pdf

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