Download Sequence Alignment Techniques

Sequence Alignment
Techniques
In this presentation……
Part 1 – Searching for Sequence
Similarity
Part 2 – Multiple Sequence Alignment
Part
1
Searching for
Sequence Similarity
Sequence similarity searches
• Sequence similarity searches of database enable us
to extract sequences that are similar to a query
sequence
• Information about these extracted sequences can
be used to predict the structure or function of the
query sequence
• Prediction using similarity is a powerful and
ubiquitous idea in bioinformatics. The underlying
reason for this is molecular evolution
Sequence alignment
• Any pair of DNA sequence will show some degree
of similarity
• Sequence alignment is the first step in quantifying
this in order to distinguish between chance
similarity and real biological relationships
• Alignments show the differences between
sequences and changes (mutations), insertions or
deletions (indels or gaps) and can be interpreted in
evolutionary terms
Alignment algorithms
• Dynamic programming algorithms can calculate the
best alignment of two sequences
• Well-known variants are
– the Smith-Waterman algorithm (local alignments)
– the Needleman-Wunsch algorithm (global alignments)
• Local alignments are useful when sequences are not
related over their full lengths, e.g., proteins sharing
only certain domains or DNA sequences related only
in exons
Alignment scores and gap penalties
• A simple alignment score measures the number or
proportion of identically matching residues
• Gap penalties are subtracted from such scores to
ensure that alignment algorithms produce
biologically sensible alignments without many gaps
• Gap penalties may be constant (independent of the
length of the gap), proportional (proportional to the
length of the gap) or affine (containing gap opening
and gap extension contributions)
• Gap penalties can be varied according to the desired
application
Similarity and homology
• Similarity may exist between any sequences
• Sequences are homologous only if they
have evolved from a common ancestor
• Homologous sequences often have similar
biological functions (orthologs), but the
mechanism of gene duplication allows
homologous sequences to evolve different
functions (paralogs)
Similarity search in databases
• Sequences similar to a query can be found
in a database by aligning it to each database
sequence in turn and returning the highest
scoring (most similar) sequences
• This can be achieved by dynamic
programming algorithms but in practice
faster approximate methods are often used
Statistical scores
• The p value of a similarity score is the probability of
obtaining a score at least as high in a chance
similarity between two unrelated sequences of
similar composition
• Low p values indicate significance matches that are
likely to have real biological significance
• The related E value is the expected frequency of
chance occurrences scoring at least as high as the
identified similarity
• A low p value for a similarity between two
sequences can translate into a high E value for a
search of a large database
Sensitivity and specificity
• These measures quantify the success of a database
search strategy
• Sensitivity measures the proportion of real
biological sequence relationships in the database
that were detected as hits in the search
• Specificity is the proportion of the hits
corresponding to real biological relationships
• Changing E and p value thresholds results in a
trade-off between these complementary measures
of success
Maximizing amino acid identities
• Protein sequences can be aligned to
maximize amino acid identities, but this
will not reveal distant evolutionary
relationships
Evolution
• Protein-coding sequences evolve slowly
compared with most other parts of the
genome, because of the need to maintain
protein structure and function
• An exception to this is the fast evolution
that might occur in the redundant copy of a
recently duplicated gene
Allowed changes
• Changes in protein sequences during
evolution tend to involve substitutions
between amino acids with similar properties
because these tend to maintain the structural
stability of the protein
Substitution score matrices
• These matrices give scores for all possible amino
acid substitutions during evolution
• Higher scores indicate more likely substitutions
• Example matrices are BLOSUM62 and PAM250
• PAM stands for Accepted Point Mutations, and in
this case, the evolutionary distance of the matrix is
250 amino acid changes per 100 residues
• Dynamic programming algorithms for sequence
alignment can operate using scores from these
matrices
Significance of score matrices
• Substitution score matrices allow detection
of distant evolutionary relationships
between protein sequences
• It is possible to detect much more distant
relationships by comparing protein
sequences than by comparing nucleic acid
sequences
MATLEKLMKA
PPPPPPPPPP
AVAEEPLHRP
PEFQKLLGIA
MSDNLPRLQL
PQKCRPYLVN
FESLKSFQQQ
PQLPQPPPQA
KKELSATKKD
MELFLLCSDD
ELYKEIKKNG
LLPCLTRTSK
QQQQQQQQQQ
QPLLPQPQPP
RVNHCLTICE
AESDVRMVAD
APRSLRAALW
RPEESVQETL
QQQQQQQQQQ
PPPPPPPPGP
NIVAQSVRNS
ECLNKVIKAL
RFAELAHLVR
AAAVPKIMAS
Part of the sequence of human Huntington’s disease
protein (Huntingtin) showing low complexity
regions (underlined) associated with compositional
bias towards glutamine (Q) and proline (P)
0
PLEK_HUMAN
(horizontal) vs.
PLEK_HUMAN
(vertical)
100
200
300
400
50
100
150
200
250
300
350
400
A dot plot of human pleckstrin sequence against itself produced
with Erik Sonnhammer’s ‘dotter’ program. The sequence is plotted
from N- to C- terminus along horizontal and vertical axes between
residues 1 and approximately 350.
C
S
T
P
A
G
N
D
E
Q
B
R
K
M
I
L
V
F
Y
W
12
0
–2
–1
–2
–3
–4
–5
–5
–5
–3
–4
–5
–5
–3
–6
–2
–4
0
–8
C
2
1
1
1
1
1
0
0
–1
–1
0
0
–2
–1
–3
–3
–3
–3
–2
S
3
0
1
0
0
0
0
–1
–1
–1
0
–1
0
–2
0
–3
–3
–5
T
6
1
–1
–1
–1
–1
0
0
0
–1
–2
–2
–3
–1
–5
–5
–6
P
The PAM250 matrix and alignment of sequences. Total
alignment scores for two matrices should not be compared, but
note that the PAM matrix is able to detect a much better
alignment in second halves of these sequences rather than
identity matrix. With the introduction of a single gap, sensible
alignments of hydrophobic amino acids, and alignment of K with
R (both basic), D with E (both acidic) and F with Y (both
aromatic) can be seen
2
1
0
0
0
0
–1
–2
–1
–1
–1
–2
0
–4
–3
–6
A
5
0
1
0
–1
–2
–3
–2
–3
–3
–4
–1
–5
–5
–7
G
2
2
1
1
2
0
1
–2
–2
–3
–2
–4
–2
4
N
4
3
2
1
–1
0
–3
–2
–4
–2
–6
–4
7
D
4
2
4
–1
0
–2
–2
–3
–4
–5
–4
7
E
4
3
1
1
–1
-2
-2
-2
-5
–4
5
Q
Sequence 1:
MIIVKP –VVLKGDFG
Sequence 2:
MILLKP AIIIRAEYPosition score: 656256 044231370
6
2
0
–2
-2
-2
-2
–2
0
3
H
5
3
0
-2
–3
–2
–4
–4
2
R
5
0 6
–2 2 5
–3 4 2 6
–2 2 4 2 4
–5 0 1 2 –1
–4 –2 –1 –1 –2
–3 –4 –5 –5 –6
K M I L V
9
7 10
0 0 17
F Y W
Figure 3. Display of the DNA
unit. DNA can be described at
several levels of detail. At the
most detailed level, DNA can
be characterized by the 5' and 3'
termini at both external and
internal positions; at the most
abstract level, the substrate
DNA can be one of 16 common
structures. The goal is to
provide methods for specifying
the properties of DNA in as
many ways as is natural for a
scientist.
Figure 7. An initial
experimental environment.
The temperature is 37
degrees Celsius and the pH
value is 7.4. No DNA
polymerase I activity is
possible
Part
2
Multiple Sequence
Alignment
Non specific sequence similarity
• Certain types of sequence similarity are less
likely to be indicative of an evolutionary
relationship than others are
• Examples of this are similarity between
regions of low compositional complexity,
short period repeats and protein sequences
coding for generic structures like coiled
coils
Similarity search filters
• Regions of the non specific sequence types can
degrade the results of similarity searches and are
often filtered out of query sequences prior to
searching
• The programs SEG and DUST can be used to
detect and filter low complexity sequences, XNU
can filter short period repeats and COILS can
detect the presence of potential coiled coil
structures
Database types for searches
• Database and query sequences can be protein
or nucleic acid sequences and different query
strategies are required for different types and
combinations
• In general, searches are more sensitive using
strategies where protein-coding nucleic acid
database and/or query sequences are first
translated to protein sequences
Iterative database searches
• PSI-BLAST is an iterative search method that
improves on the detection rate of BLAST and FASTA
• Each iteration discovers intermediate sequences that
are used in a sequence profile to discover more distant
relatives of the query sequence in subsequent iterations
• Potential problems with PSI-BLAST are associated
with the potential for unrelated sequences to pollute
the iterative search, and difficulties associated with the
domain structure of proteins
• PSI-BLAST often detects up to twice as many
evolutionary relationships as BLAST
Multiple sequence alignment
• Multiple alignment illustrates relationships
between two or more sequences
• When the sequences involved are diverse,
the conserved residues are often key
residues associated with maintenance of
structural stability or biological function
• Multiple alignments can reveal many clues
about protein structure and functions
Multiple alignment
Part of a (artificial) multiple alignment of a family consisting of 7 sequences,
which subdivide into 3 subfamilies. The bars on the left indicate subfamilies; the
dotted boxes highlight conservation patterns.
Progressive sequence alignment
• Most commonly used software uses the
method of progressive alignment
• This is a fast method, but frozen-in errors
mean that it does not always work perfectly
• Biological knowledge can provide
information about likely alignments, and
where automatically produced alignments
turn out to be imperfect, software for
manual alignment editing is required
Protein families
• Assigning sequences to protein families is a
very valuable way of predicting protein
family (consensus sequences, conserved
residues, residue patterns, sequence profiles,
etc.)
• Many ways have been developed to represent
protein family information and these have
been stored in secondary protein family
databases
Consensus sequences
• These condenses the information from a multiple
alignment into single sequence
• Their main shortcoming is the inability to
represent any probabilistic information apart from
the most common residue at a particular position
• Derivation of consensus sequence illustrates that
any protein family representation is subject to bias
if the set of sequences from which it was derived
is biased
PRINTS and BLOCKS
• These represent protein families of multiply aligned
ungapped segments (motifs) derived from the most
highly conserved regions of sequences
• By representing more of the sequence, they have the
potential to be more sensitive than short PROSITE
patterns
• The ability to match in only a subset of the motifs
associated with a particular family means that they
have the ability to detect splice variants and sequence
fragments and to represent subfamilies
• WWW-based search engines for the databases are
available
Protein domain families
• Many proteins are built up from domains in
a modular architecture
• The study of protein families is best pursued
as a study of protein domain families
• Prodom is a database of protein domain
sequences created by automatic means from
the protein sequence databases
Resources for domain families
• Pfam and SMART can be used for protein
domain family analysis
• The integrated resource Interpro unites
PROSITE, PRINTS, Pfam, Prodom and
SMART
Visualization of similarities
• Dot plots are a very good way to
visualize sequence similarity and
find repeats