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Lecture 1
Sequence Alignment
Sequence alignment: why?
• Early in the days of protein and gene sequence analysis, it was
discovered that the sequences from related proteins or genes were
similar, in the sense that one could align the sequences so that many
corresponding residues match.
• This discovery was very important: strong similarity between two genes
is a strong argument for their homology. Bioinformatics is based on it.
• Terminology:
– Homology means that two (or more) sequences have a common
ancestor. This is a statement about evolutionary history.
– Similarity simply means that two sequences are similar, by some
criterion. It does not refer to any historical process, just to a
comparison of the sequences by some method. It is a logically
weaker statement.
• However, in bioinformatics these two terms are often confused and
used interchangeably. The reason is probably that significant similarity
is such a strong argument for homology.
An example of a sequence alignment for two proteins (the
protein kinase KRAF_HUMAN and the uncharacterized
O22558 from Arabidopsis thaliana) using the BLAST program.
Note: protein is expresses as a sequence of amino acids, represented by
single letter alphabets.
Many genes have a common
ancestor
• The basis for comparison of proteins and genes using
the similarity of their sequences is that the the proteins
or genes are related by evolution; they have a
common ancestor.
• Random mutations in the sequences accumulate
over time, so that proteins or genes that have a
common ancestor far back in time are not as similar
as proteins or genes that diverged from each other
more recently.
• Analysis of evolutionary relationships between protein
or gene sequences depends critically on sequence
alignments.
A dotplot displays sequence similarity
100
150
50
100
150
50
1
Erythrocruorin from Chironomus (insect)
1
Hemoglobin A chain from human
Symbols in
matrix
indicates
degree of
matching
Some definitions
• Global alignment. Assumes that the two proteins are
basically similar over the entire length of one another.
• Local alignment. Searches for segments of the two
sequences that match well.
• Gaps and insertions. Match may be improved by
putting gaps or inserting extra residues into one of the
sequences.
---DFAHKAMM-PTWWEGCIL-----DXGHK-MMSPTW-ECAAL---
• Scoring.
Quantifies the goodness of alignment.
Exact match has highest score, substitution lower score
and insertion and gaps may have negative scores.
• Substitution matrix. A symmetrical 20*20 matrix
(20 amino acids to each side). Each element gives a
score that indicates the likelihood that the two residue
types would mutate to each other in evolutionary time.
• Gap penalty. Evolutionary events that makes gap
insertion necessary are relatively rare, so gaps have
negative scores. Three types:
– Single gap-open penalty. This will tend to stop gaps from
occuring, but once they have been introduced, they can grow
unhindered.
– Gap penalty proportional to the gap length. Works against
larger gaps.
– Gap penalty that combines a gap-open value with a gaplength value.
Substitution “log odds” matrix BLOSUM 62
sjk = 2 log2(qjk/ejk)
qjk – number of times
j-k pair of residues
seen together
ejk – number of times
j-k pair of residues
Henikoff and Henikoff
expected to be together(1992; PNAS 89:10915-10919)
sij = log10 Mij
Mij = probability of
replacement j to i
per occurrence
of residue j.
( M.O. Dayhoff, ed., 1978,
Atlas of Protein Sequence
and Structure, Vol 5).
• Let W(k) be the penalty for a gap of length k
and sub(A,B) the substitution score for replacing
A residue by B.
• The score for the alignment
C - K H V F C R V C I
C K K C - F C - K C V
Is:
3 sub(C,C) + sub(K,K) + sub(H,C) +
sub(F,F) + sub(V,K) + sub(I,V) + 3 W(1)
If we use PAM 250 and gap penealty of 10, then
score is
3x12 + 5 – 3 + 9 – 2 + 4 + 3x(-10) = 19
Question is how to find the alignment with the
highest scores.
There no single best alignment
• Optimal alignment. The alignment
that is the best, given the scoring
convention. There is no such thing as the
single best alignment. Good alignment is
given by a scoring systems based on solid
biology.
The Needleman-Wunsch-Sellers
Algorithm (NWS)
Needleman, S.B & Wunsch, C.D. (1970) J.Mol.Biol. 48:443-453;
Sellers (Sellers, P.H. (1974), SIAM J.Appl.Math. 26:787
• Dynamical-progamming algoritm for finding
the highest-scoring alignment of two
sequences (given a scoring scheme).
• Let W(k) be the penalty for a gap of length k
and sub(A,B) the substitution score for
replacing A residue by B.
• Suppose we want align two short sequences:
CKHVFCRVCI and CKKCFCKCV
i = 1 to L
j = 1 to L’
1. Line sequences to
form a LxL’ matrix;
L, L’ are length if
the two sequences.
2. Find the score at site (I,j) from scores at previous sites
and scoring scheme.
D i,j = max {D i+1,j+1 + sub(A i , B j );
D i, j+k + W(k), k = 1 to L’-j;
D i+k, j + W(k), k = 1 to L-i }
3. Fill the empty sites
in column i=L and
row j=L’ with zeroes.
(Here use simple
diagonal – 0 and 1
- substitution matrix
and ignore gap
penalty.)
4. Move in to the next
column and row.
To go to site (i,j),
choose from
among (i+1,j), (i,j+1)
and (i+1,j+1) that
has the the largest
value, then add
that value to the
value of (i,j).
E.g. Value of (6,8)
was 1. Go to that
site from (7,8).
Value of (7,8) is 1.
So value of (6,8)
is updated to 2.
5. Repeat the process
6. Backtrace our path ending
at the cell with value 5 at
(1,1) to identify all paths
(value must increase along
the path) leading to that
(1,1). These paths are
highlighted as shown.
7. Some of the paths are
CKHVFCRVCI
CKKCFC-KCV
CKHVFCRVCI
CKKCFCK-CV
C-KHVFCRVCI
CKKC-FC-CKV
CKH-VFCRVCI
CKKC-FC-KCV
8. And these give alignments
such as those on the left;
all have a score of 5.
However if we used the more realistic PAM 250
substitution matrix then these alignments would have
different scores (and the NWS algorithm would have
picked the alignment with the highest one).
Score with PAM 250 and gap penalty -10
CKHVFCRVCI
CKKCFC-KCV
36 + 5 + 0 – 2 + 9 – 2 + 4 – 10 = 40
CKHVFCRVCI
CKKCFCK-CV
36 + 5 + 0 – 2 + 9 + 5 + 4 – 10 = 47
C-KHVFCRVCI
CKKC-FC-CKV
36 + 5 – 3 + 9 – 2 + 4 – 3x10 = 19
CKH-VFCRVCI
CKKC-FC-KCV
36 + 5 + 0 + 9 – 2 + 4 – 3x10 = 22
Gap penalty is important; biology does not like gaps
Database searching
• Probe sequence
– When we have a sequence (the probe
sequence), often we want to find other
sequences similar to it in a database
• Match sequence
– The sequence(s) found by database search
that is (are) similar to the probe sequence;
also called a hit.
• Homologs
– Sequences having the same ancestor (who
diverged and evolved differently)
• Score
– Used to determine quality of match and
basis for the selection of matches. Scores
are relative.
• Expectation value
– An estimate of the likelihood that a given hit
is due to pure chance, given the size of the
database; should be as low as possible.
E.V.’s are absolute. A high score and a low
E.V. indicate a true hit.
• Sequence identity (%) (or Similarity)
– Number of matched residues divided by
total length of probe
• Rule-of-thumb for true hit
– A database hit having a sequence identity
of 25% or more (protein lengths 200 residues
or more) is almost certainly a true hit
• Popular and powerful sequence search
software
– BLAST
• www.ncbi.nlm.nih.gov/blast/
• Or do a Google on “BLAST”
– FASTA
• www.ebi.ac.uk/fasta33/
• Or do a Google on “FASTA”
Most important sequence
databases
• Genbank– maintained by USA National Center
for Biology Information (NCBI)
– All biological sequences
• www.ncbi.nlm.nih.gov/Genbank/GenbankOvervie
w.html
– Genomes
• www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?db=G
enome
• Swiss-Prot - maintained by EMBL- European
Bioinformatics Institute (EBI )
– Protein sequences
• www.ebi.ac.uk/swissprot/
Multiple sequence alignment
• Often a probe sequence will yield many hits in
a search. Then we want to know which are the
residues and positions that are common to all
or most of the probe and match sequences
• In multiple sequence alignment, all similar
sequences can be compared in one single
figure or table. The basic idea is that the
sequences are aligned on top of each other,
so that a coordinate system is set up, where
each row is the sequence for one protein, and
each column is the 'same' position in each
sequence.
An example: cellulose-binding domain of
cellobiohydrolase I
Name of homologous domians
consensus
Position of residue
residues and position common to most homologs
A schematic image of the 3D structure of the domian.
Arrows indicate beta sheets. Other parts are loops.
Kraulis J, et al.,
Biochemistry 1989,
28(18):7241-57
A sequence logo. This shows the conserved residues
as larger characters, where the total height of a
column is proportional to howconserved that
position is. Technically, the height is proportional to
the information content of the position.
Applications of
multiple sequence alignment
• Identify consensus segments
– Hence the most conserved sites and residues
• Use for construction of phylogenesis
– Convert similarity to distance
www.ch.embnet.org/software/ClustalW.html
– Of genes, strains, organisms, species, life
基因親緣樹
Simplified cladogram
(relationship tree)
of the 'many-to-many'
relationships of
classical nuclear
receptors. Triangles
indicate expansion
within one lineage;
bars represent single
members. Numbers in
parentheses indicate
the number of
paralogues in each
group.
Constructing
Bacteria
Tree of Life
with k-mers
(16S rRNA
35 organisms)
A. aeolicus
T. maritima
LF Luo
FM Ji
LC Hsieh
HC Lee
(2001)
Eukarya
Archaea
Black tree: dist’n of 8-mers. Red tree: sequence aligment.
ClustalW: A standard multiple
alignment program
• Original paper
– Thompson JD, Higgins DG, Gibson TJ. Nucleic Acids
Res 1994 11; 22:4673-80
• Where to find on web
–
–
–
–
–
www.ebi.ac.uk/clustalw/
www.ch.embnet.org/software/ClustalW.html
www.clustalw.genome.ad.jp/
bioweb.pasteur.fr/seqanal/interfaces/clustalw.html
Do a Google on “ClustalW”
Overview of ClustalW Procedure
Hbb_Human
Hbb_Horse
Hba_Human
Hba_Horse
Myg_Whale
1
2
3
4
5
.17
.59
.59
.77
CLUSTAL W
.60
.59
.77
.13
.75
Hbb_Human
.75
-
2
3
Quick pairwise alignment:
calculate distance matrix
4
Hbb_Horse
Hba_Human
Neighbor-joining tree
(guide tree)
1
Hba_Horse
Myg_Whale
alpha-helices
1
2
3
4
5
PEEKSAVTALWGKVN--VDEVGG
GEEKAAVLALWDKVN--EEEVGG
PADKTNVKAAWGKVGAHAGEYGA
AADKTNVKAAWSKVGGHAGEYGA
EHEWQLVLHVWAKVEADVAGHGQ
2
1
3
4
Progressive alignment
following guide tree
Databases of multiple alignments
• Pfam: Protein families database of aligments
and HMMs
• www.cgr.ki.se
• PRINTS, multiple motifs consisting of ungapped,
aligned segments of sequences, which serve as
fingerprints for a protein family
• www.bioinf.man.ac.uk
• BLOCKS, multiple motifs of ungapped, locally
aligned segments created automatically
• fhcrc.org
This lecture is mostly based on
• Lecture on Sequence alignment
by Per Kraulis, SBC, Uppsala University
– www.sbc.su.se/~per/molbioinfo2001/multali.html
• Elementary Sequence Analysis
by Brian Golding, Computational Biology,
McMaster U.
– helix.biology.mcmaster.ca/courses.html
• A rich resource of lectures is given at
Research Computing Resource
New York Universtiy School of Medicine
– www.med.nyu.edu/rcr/rcr/btr/complete.html
Manual Alignment- software
GDE- The Genetic Data Environment (UNIX)
CINEMA- Java applet available from:
– http://www.biochem.ucl.ac.uk
Seqapp/Seqpup- Mac/PC/UNIX available from:
– http://iubio.bio.indiana.edu
SeAl for Macintosh, available from:
– http://evolve.zoo.ox.ac.uk/Se-Al/Se-Al.html
BioEdit for PC, available from:
– http://www.mbio.ncsu.edu/RNaseP/info/programs/BIOEDIT/bi
oedit.html