Lecture 1 Sequence Alignment

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Lecture 1 Sequence Alignment
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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.

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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.
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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.

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A dotplot displays sequence similarity
1 50
100 150
Symbols in matrix indicates degree of matching
Erythrocruorin from Chironomus (insect)
1 50
100 150
Hemoglobin A chain from human
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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-
  --

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• 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 2020 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
  gap-length value.

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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 expected to be together
Henikoff and Henikoff (1992 PNAS 8910915-10919)
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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).
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• 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.

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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.

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The Needleman-Wunsch-Sellers Algorithm (NWS)
Needleman, S.B Wunsch, C.D. (1970) J.Mol.Biol.
48443-453 Sellers (Sellers, P.H. (1974), SIAM
J.Appl.Math. 26787

• 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

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i 1 to L
1. Line sequences to form a LxL matrix
L, L are length if the two sequences.
j 1 to L
2. Find the score at site (I,j) from scores at
previous sites and scoring scheme.
D i,j max D i1,j1 sub(A i , B j )
D i, jk W(k), k 1
to L-j D ik, j
W(k), k 1 to L-i
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3. Fill the empty sites in column iL and
row jL with zeroes. (Here use simple
diagonal 0 and 1 - substitution matrix
and ignore gap penalty.)
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4. Move in to the next column and row.
To go to site (i,j), choose from among
(i1,j), (i,j1) and (i1,j1) 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.
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5. Repeat the process
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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.
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7. Some of the paths are
CKHVFCRVCI CKKCFC-KCV CKHVFCRVCI CKKCFCK-CV C-KH
VFCRVCI 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.
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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 CKHVFCRVCI CKKCFCK-CV C-KH
VFCRVCI CKKC-FC-CKV CKH-VFCRVCI CKKC-FC-KCV
36 5 0 2 9 2 4 10 40
36 5 0 2 9 5 4 10 47
36 5 3 9 2 4 3x10 19
36 5 0 9 2 4 3x10 22
Gap penalty is important biology does not like
gaps
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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)

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• 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

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• 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

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Most important sequence databases

• Genbank maintained by USA National Center for
  Biology Information (NCBI)
• All biological sequences
• www.ncbi.nlm.nih.gov/Genbank/GenbankOverview.html
• Genomes
• www.ncbi.nlm.nih.gov80/entrez/query.fcgi?dbGenom
  e
• Swiss-Prot - maintained by EMBL- European
  Bioinformatics Institute (EBI )
• Protein sequences
• www.ebi.ac.uk/swissprot/

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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.

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An example cellulose-binding domain of
cellobiohydrolase I
Name of homologous domians
Position of residue
residues and position common to most homologs
consensus
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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
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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.
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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/s
  oftware/ClustalW.html
• Of genes, strains, organisms, species, life

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ClustalW A standard multiple alignment program

• Original paper
• Thompson JD, Higgins DG, Gibson TJ. Nucleic Acids
  Res 1994 11 224673-80
• 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

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Overview of ClustalW Procedure
CLUSTAL W
Hbb_Human 1 -
Hbb_Horse 2 .17 -
Hba_Human 3 .59 .60 -
Quick pairwise alignment calculate distance
matrix
Hba_Horse 4 .59 .59 .13 -
Myg_Whale 5 .77 .77 .75 .75 -
Hbb_Human
4
2
3
Hbb_Horse
Neighbor-joining tree (guide tree)
Hba_Human
1
Hba_Horse
Myg_Whale
alpha-helices
1 PEEKSAVTALWGKVN--VDEVGG
4
2
3
Progressive alignment following guide tree
2 GEEKAAVLALWDKVN--EEEVGG
3 PADKTNVKAAWGKVGAHAGEYGA
1
4 AADKTNVKAAWSKVGGHAGEYGA
5 EHEWQLVLHVWAKVEADVAGHGQ
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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

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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 ResourceNew York
  Universtiy School of Medicine
• www.med.nyu.edu/rcr/rcr/btr/complete.html

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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/BIOE
  DIT/bioedit.html