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# Lesson 2 - PowerPoint PPT Presentation

Lesson 2. Aligning sequences and searching databases . Homology and sequence alignment. Homology. Homology = Similarity between objects due to a common ancestry. Hund = Dog, Schwein = Pig. Sequence homology. Similarity between sequences as a result of common ancestry. .

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### Lesson 2

Aligning sequences and searching databases

Homology = Similarity between objects due to a common ancestry

Hund = Dog,

Schwein = Pig

Similarity between sequences as a result of common ancestry.

VLSPAVKWAKVGAHAAGHG

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Alignment:Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences.

VLSPAVKWAKV

||| || |||| VLSEAVLWAKV

• To detect if two sequences are homologous. If so, homology may indicate similarity in function (and structure).

• Required for evolutionary studies (e.g., tree reconstruction).

• To detect conservation (e.g., a tyrosine that is evolutionary conserved is more likely to be a phosphorylation site).

• Given a sequenced DNA, from an unknown region, align it to the genome.

If two sequences share a common ancestor – for example human and dog hemoglobin, we can represent their evolutionary relationship using a tree

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

A perfect match suggests that no change has occurred from the common ancestor (although this is not always the case).

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

A substitution suggests that at least one change has occurred since the common ancestor (although we cannot say in which lineage it has occurred).

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

Option 1: The ancestor had L and it was lost here. In such a case, the event was a deletion.

VLSEAVLWAKV

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

Option 2: The ancestor was shorter and the L was inserted here. In such a case, the event was an insertion.

L

VLSEAVWAKV

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

Normally, given two sequences we cannot tell whether it was an insertion or a deletion, so we term the event as an indel.

Deletion?

Insertion?

VLSEAVLWAKV

VLSPAV-WAKV

Indels in protein coding genes are often of 3bp, 6bp, 9bp, etc...

Gene Search

In fact, searching for indels of length 3K (K=1,2,3,…) can help algorithms that search a genome for coding regions

Global alignment– finds the best alignment across the entire two sequences.

Local alignment– finds regions of similarity in parts of the sequences.

Global alignment: forces alignment in regions which differ

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Local alignment will return only regions of good alignment

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PTK2 protein tyrosine kinase 2 of human and rhesus monkey

Human PTK2 :

Domain A

Domain B

Protein tyrosine kinase domain

In leukocytes, a different gene for tyrosine kinase is expressed.

Domain A

Domain X

Protein tyrosine kinase domain

PTK2

Domain A

Protein tyrosine kinase domain

Domain B

Domain X

Protein tyrosine kinase domain

Leukocyte TK

Use global alignment when the two sequences share the same overall sequence arrangement.

Use local alignment to detect regions of similarity.

AAGCTGAATTCGAA

AGGCTCATTTCTGA

One possible alignment:

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

This alignment includes:

2mismatches

4 indels (gap)

10 perfect matches

Choosing an alignment for a pair of sequences

Many different alignments are

possible for 2 sequences:

AAGCTGAATTCGAA

AGGCTCATTTCTGA

A-AGCTGAATTC--GAA

AG-GCTCA-TTTCTGA-

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

Which alignment is better?

Perfect match: +1

Mismatch: -2

Indel (gap): -1

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

A-AGCTGAATTC--GAA

AG-GCTCA-TTTCTGA-

Score: =(+1)x10 + (-2)x2 + (-1)x4= 2

Score: =(+1)x9 + (-2)x2 + (-1)x6 = -1

Higher score  Better alignment

• Assumes independence between positions:

• each position is considered separately

• Scores each position:

• Positive if identical (match)

• Negative if different (mismatch or gap)

• Total score = sum of position scores

• Can be positive or negative

• In the example above, the choice of +1 for match,-2 for mismatch, and -1 for gap is quite arbitrary

• Different scoring systems  different alignments

• We want a good scoring system…

• Representing the scoring system as a table or matrix n X n (n is the number of letters the alphabet contains. n=4 for nucleotides, n=20 for amino acids)

• symmetric

Uniform substitutions between all nucleotides:

Match

Mismatch

Can take into account biological phenomena such as:

Transition-transversion

Take into account physico-chemical properties

In advanced algorithms, two gaps of one amino-acid are given a different score than one gap of two amino acids. This is solved by giving a penalty to each gap that is opened.

Gap extension penalty < Gap opening penalty

The dependency between the penalty and the length of the gap need not to be linear.

AGGGTTC—GA

AGGGTTCTGA

Score = -2

AGGGTT-—GA

AGGGTTCTGA

Score = -4

Linear penalty

AGGGT--—GA

AGGGTTCTGA

Score = -6

AGGG---—GA

AGGGTTCTGA

Score = -8

The dependency between the penalty and the length of the gap need not to be linear.

AGGGTTC—GA

AGGGTTCTGA

Score = -4

AGGGTT-—GA

AGGGTTCTGA

Score = -6

Non-linear penalty

AGGGT--—GA

AGGGTTCTGA

Score = -7

AGGG---—GA

AGGGTTCTGA

Score = -8

Actual substitutions:

Based on empirical data

Commonly used by many bioinformatics programs

PAM & BLOSUM

The idea: Given an alignment of a large number of closely related sequences we can score the relation between amino acids based on how frequently they substitute each other

M G Y D E

M G Y D E

M G Y E E

M G Y D E

M G Y Q E

M G Y D E

M G Y E E

M G Y E E

In the fourth column

E and D are found in 7 / 8

PAM Matrix - Point Accepted Mutations

• The Dayhoff PAM matrix is based on a database of 1,572 changes in 71 groups of closely related proteins (85% identity => Alignment was easy and reliable).

• Counted the number of substitutions per amino-acid pair (20 x 20)

• Found that common substitutions occurred between chemically similar amino acids

Family of matrices PAM 80, PAM 120, PAM 250

The number on the PAM matrix represents evolutionary distance

Larger numbers are for larger distances

Similar amino acids have greater score

Based only on a single, and limited dataset

Examines proteins with few differences (85% identity)

Based mainly on small globular proteins so the matrix is biased

Henikoff and Henikoff (1992) derived a set of matrices based on a much larger dataset

BLOSUM observes significantly more replacements than PAM, even for infrequent pairs

BLOSUM:BlocksSubstitutionMatrix

Based on BLOCKS database

~2000 blocks from 500 families of related proteins

Families of proteins with identical function

Blocks are short conserved patterns of 3-60 amino acids without gaps

AABCDA----BBCDA

DABCDA----BBCBB

BBBCDA-AA-BCCAA

AAACDA-A--CBCDB

AAACAA----BBCCC

Each block represents a sequence alignment with different identity percentage

For each block the amino-acid substitution rates were calculated to create the BLOSUM matrix

BLOSUMn is based on sequences that share at least n percent identity

BLOSUM62 represents closer sequences than BLOSUM45

Derived from blocks where the sequences

share at least 62% identity

PAM100 = BLOSUM90

PAM120 = BLOSUM80

PAM160 = BLOSUM60

PAM200 = BLOSUM52

PAM250 = BLOSUM45

More distant sequences

• Scoring system = substitution matrix + gap penalty.

• Used for both global and local alignment

• For amino acids, there are two types of substitution matrices: PAM and Blosum