Transcript Lesson 2 - Laboratory of Molecular Modelling

Lesson 2
Aligning sequences and searching
databases
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Homology and sequence
alignment.
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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.
VLSPAVKWAKVGAHAAGHG
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VLSEAVLWAKVEADVAGHG
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Sequence alignment
Alignment: Comparing two
(pairwise) or more (multiple)
sequences. Searching for a series
of identical or similar characters in
the sequences.
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Why align?
VLSPAVKWAKV
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VLSEAVLWAKV
1. To detect if two sequences are homologous. If so,
homology may indicate similarity in function (and
structure).
2. Required for evolutionary studies (e.g., tree
reconstruction).
3. To detect conservation (e.g., a tyrosine that is
evolutionary conserved is more likely to be a
phosphorylation site).
4. Given a sequenced DNA, from an unknown region,
align it to the genome.
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Insertions, deletions, and
substitutions
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Sequence alignment
If two sequences share a common
ancestor – for example human and
dog hemoglobin, we can represent
their evolutionary relationship
using a tree
VLSPAV-WAKV
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Perfect match
A perfect match suggests that no change
has occurred from the common ancestor
(although this is not always the case).
VLSPAV-WAKV
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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A substitution
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
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Indel
Option 1: The ancestor had L and it was
lost here. In such a case, the event was a
deletion.
VLSEAVLWAKV
VLSPAV-WAKV
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Indel
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
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Indel
Normally, given two sequences we cannot
tell whether it was an insertion or a
deletion, so we term the event as an indel.
Deletion?
VLSPAV-WAKV
Insertion?
VLSEAVLWAKV
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Indels in protein coding genes
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
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Global and Local pairwise
alignments
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Global vs. Local
• Global alignment – finds the best
alignment across the entire two
sequences.
ADLGAVFALCDRYFQ
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ADLGRTQN-CDRYYQ
• Local alignment – finds regions of
similarity in parts of the sequences.
ADLG
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ADLG
CDRYFQ
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CDRYYQ
Global
alignment:
forces
alignment in
regions which
differ
Local
alignment will
return only
regions of
good
alignment
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Global alignment
PTK2 protein tyrosine kinase 2 of human
and rhesus monkey
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Proteins are comprised of domains
Human PTK2 :
Domain A
Domain B
Protein tyrosine
kinase domain
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Protein tyrosine kinase domain
In leukocytes, a different gene for tyrosine
kinase is expressed.
Domain A
Domain X
Protein tyrosine
kinase domain
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The sequence similarity is
restricted to a single domain
Domain A
Protein tyrosine
Domain B
PTK2
kinase domain
Domain X
Protein tyrosine
kinase domain
Leukocyte TK
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Global alignment of PTK and LTK
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Local alignment of PTK and LTK
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Conclusions
Use global alignment when the two
sequences share the same overall
sequence arrangement.
Use local alignment to detect regions of
similarity.
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How alignments are computed
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Pairwise alignment
AAGCTGAATTCGAA
AGGCTCATTTCTGA
One possible alignment:
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
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AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
This alignment includes:
2 mismatches
4 indels (gap)
10 perfect matches
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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?
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Scoring system (naïve)
Perfect match: +1
Mismatch: -2
Indel (gap): -1
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGAScore: = (+1)x10 + (-2)x2 + (-1)x4 = 2
A-AGCTGAATTC--GAA
AG-GCTCA-TTTCTGAScore: = (+1)x9 + (-2)x2 + (-1)x6 = -1
Higher score  Better alignment
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Alignment scoring - scoring of
sequence similarity:
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
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Scoring systems
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Scoring system
•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…
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Scoring matrix
•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)
A
G
C
A
2
G
-6
2
C
-6
-6
2
T
-6
-6
-6
T
2
•symmetric
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DNA scoring matrices
• Uniform substitutions between all nucleotides:
From
To
A
A
2
G
-6
2
C
-6
-6
2
T
-6
-6
-6
Mismatch
G
C
Match
T
2
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DNA scoring matrices
Can take into account biological phenomena
such as:
• Transition-transversion
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Amino-acid scoring matrices
•
Take into account physico-chemical properties
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Scoring gaps (I)
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
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Scoring gaps (II)
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
AGGGT--—GA
AGGGTTCTGA
Score = -6
AGGG---—GA
AGGGTTCTGA
Score = -8
Linear penalty
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Scoring gaps (II)
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
AGGGT--—GA
AGGGTTCTGA
Score = -7
AGGG---—GA
AGGGTTCTGA
Score = -8
Non-linear penalty
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PAM AND BLOSUM
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Amino-acid substitution matrices
• Actual substitutions:
– Based on empirical data
– Commonly used by many bioinformatics
programs
– PAM & BLOSUM
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Protein matrices – actual
substitutions
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
M
M
M
M
M
M
M
G
G
G
G
G
G
G
G
Y
Y
Y
Y
Y
Y
Y
Y
D
D
E
D
Q
D
E
E
E
E
E
E
E
E
E
E
In the fourth column
E and D are found in 7 / 8
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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
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PAM Matrices
• Family of matrices PAM 80, PAM 120, PAM
250
• The number on the PAM matrix represents
evolutionary distance
• Larger numbers are for larger distances
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Example: PAM 250
Similar amino acids have greater
score
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PAM - limitations
• 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
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BLOSUM
• 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
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BLOSUM: Blocks Substitution
Matrix
• 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
CCBADA---DBBDCC
AAACAA----BBCCC
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BLOSUM
• 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
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BLOSUM Matrices
• BLOSUMn is based on sequences that
share at least n percent identity
• BLOSUM62 represents closer sequences
than BLOSUM45
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Example : Blosum62
Derived from blocks where the sequences
share at least 62% identity
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PAM vs. BLOSUM
PAM100 = BLOSUM90
PAM120 = BLOSUM80
PAM160 = BLOSUM60
PAM200 = BLOSUM52
PAM250 = BLOSUM45
More distant sequences
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Intermediate summary
1. Scoring system =
substitution matrix + gap penalty.
2. Used for both global and local alignment
3. For amino acids, there are two types of
substitution matrices: PAM and Blosum
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