Pairwise alignment

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Pairwise alignment
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Sequence to function
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Many biosequences are related
Of mice and men - myoglobin
GLSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLK
GLSDGEWQLVLNVWGKVEADLAGHGQEVLIGLFKTHPETLDKFDKFKNL
K SEDEMKASEDLKKHGATVLTALGGILKKKGHHEAEIKPLAQSHATKH
KIP SEEDMKGSEDLKKHGCTVLTALGTILKKKGQHAAEIQPLAQSHATK
HKIP VKYLEFISECIIQVLQSKHPGDFGADAQGAMNKALELFRKDMAS
NYKELGFQG VKYLEFISEIIIEVLKKRHSGDFGADAQGAMSKALELFRN
DIAAKYKELGFQG
GLSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLK
GLSDGEWQLVLNVWGKVEADLAGHGQEVLIGLFKTHPETLDKFDKFKNL
K SEDEMKASEDLKKHGATVLTALGGILKKKGHHEAEIKPLAQSHATKH
KIP SEEDMKGSEDLKKHGCTVLTALGTILKKKGQHAAEIQPLAQSHATK
HKIP VKYLEFISECIIQVLQSKHPGDFGADAQGAMNKALELFRKDMAS
NYKELGFQG VKYLEFISEIIIEVLKKRHSGDFGADAQGAMSKALELFRN
DIAAKYKELGFQG
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What is homology?

• In biological terms homology means evolutionary
  related
• In biosequence homologous sequences are
  similar
• We infer homology by comparing biosequences using
  sequence comparisons (alignments)

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Why look for homology?

• If two sequences are homologous on the sequence
  level what conclusions?
• Three-dimensional structure will be similar
• Biological functions are very likely to be
  related but not always
• Multiple alignments of homologous sequences can
  help identify structure/functional critical
  residues

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Evolution

• Evolution is driven by mutations in genes -
  changes in proteins.
• Changes in single bases
• Changes in multiple bases
• Addition/deletion of multiple bases
• Moving of part of a gene
• Copying part or all of a gene
• We are looking at chemical evolution

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Sequence differences
Deduction
Evolution
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Orthologs vs. paralogs
Paralogs
Orthologs
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Origins of genes with similar sequence
Paralogs
Orthologs
Analogous
Xenologous
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Modular proteins

• Most proteins larger than 20 kDa are built from
  modules

C-type lectin
Fibronectin
EGF domains
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Residue distribution in 3D

• Structures are hydrophilic on the outside and
  hydrophobic on the inside

Hydrophobic Small Acidamide Basic Active site
Chymotrypsin
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Homology search methods

• Dot-PlotSimple, graphical, visual
• Dynamic programmingNeedleman-WunchSmith-Waterman
  
• k-tuple programsFastABLAST

• Alignment
• Matrices
• PAM250
• BLOSUM62

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Dot-plot
A D K F H K E A C T E
X D X K X X A X
X A X X K
X E X A X X
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Dot-plot
X Human Factor X Y Human Factor IX (Christmas
factor)
Direct comparison No matrix
Direct comparison Compare 2
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DotLet
Distribution of scores of all residue pairs
Log of distribution
Protein C vs factor X
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DotLet - optimizing
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DotLet low e-score
MBL vs. Immunolectin Bombyx mori
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DotLet - optimizing
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Alignment

• An alignment is best fit between two or more
  biosequences.
• Proteins having close relationships can be
  identified by comparing identical
  residues.SEDEMKASEDLKKHGATVLTALGGILKKKGHHEA
  SEEDMKGSEDLKKHGCTVL
  TALGTILKKKGQHAAHuman and murine myoglobin

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Alignment - similarities

• When relationships are more distant you introduce
  similaritiesbovine GVTTSDVVVAGEFDQGSSSEKIQKLK
  IAKVFKNSKYNSL ... . ...
  .... Cod NVKNYHRVVLGEHDRSSNSEGVQVMT
  VGQVFKHPRYNGF
• When doing the comparison by hand you often use
  chemical similaritiesL/I/V F/Y/W R/K
  E/D/Q/N G/A

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Global/local alignment

• For global alignments you try to align complete
  sequences
• For local alignments you only align part of the
  sequence

Best suited for pattern matching
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Alignment

• In order to maximise an alignment, gaps have to
  be introducedChymo QDKTGFHFCGGSLINENWVVTAAHCGVT
  TS-DVVVAGEFDQGSSSEKIQKLKIAKVFKNS .
  . . . ...
  . Fac X INEENEGFCGGTILSEFYILTAAHCLYQAKRFKVRVG
  DRNTEQEEGGEAVHEVEVVIKHNChymo KYNSLTINNDITLLKLSTA
  ASFSQTVSAVCLPS---ASDDFAAGTTCVTTGWGLTRYTNA
  .. .. .... .. .
  . .. Fac X RFTKETYDFDIAVLRLKTPITFRMNVAPAC
  LPERDWAESTLMTQKTGIVSGFGRTHEKGRChymo
  NTPDRLQQASLPLLSNTNCKKYWGTKIKDAMICAGASG--VSSCMGDSGG
  PLVCKKNGAW . .... . .
  . . . Fac X
  QS-TRLKMLEVPYVDRNSCKLSSSFIITQNMFCAGYDTKQEDACQGDSGG
  PHVTRFKDTY Chymo TLVGIVSWGSSTCSTSTPGVYARVTA
  LVNWVQQTLAAN-------------------
  .. . ... . Fac X
  FVTGIVSWGESCARKGKYGIYTKVTAFLKWIDRSMKTRGLPKAKSHAPEV
  ITSSPLK

ws gap penalty g gap opening r gap
extension x extension length
Gap penalties are usually implemented as ws g
rx
Bovine chymotrypsin / human factor X
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Alignment

• In the three-dimensional structure, gaps are
  usually located to peripherial loop structures.

Bovine chymotrypsin Insertion and deletion
positions (compared to Factor X) are in green.
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The twilight zone
After insertion of gaps, two random sequences can
be expected to be 10-20 identical! Two sequences
of length 100 residues that are 25 identical are
likely to be genuinely related. If in the 15-25
region, it is worthwhile to do additional tests.
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Matrices

• Instead of just counting amino acid substitutions
  (e.g. conserved vs. non-conserved residues) we
  can use biological information
• The PAM series of substitution matrices are based
  on global alignment of a limited set of closely
  related protein sequences (replacements are
  counted on the brances of a phylogenetic tree).
  The PAM series constitute Point Accepted
  Mutations
• The BLOSUM series of substitution matrices are
  based highly conserved regions in a series of
  alignments forbidden to contains gaps. The
  BLOSUMxx series contains blocks with at most xx
  identity.

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PAM250
C 12 G -3 5 P -3 -1 6 S 0 1 1 1 A -2 1
1 1 2 T -2 0 0 1 1 3 D -5 1 -1 0 0
0 4 E -5 0 -1 0 0 0 3 4 N -4 0 -1 1 0
0 2 1 2 Q -5 -1 0 -1 0 -1 2 2 1 4 H
-3 -2 0 -1 -1 -1 1 1 2 3 6 K -5 -2 -1 0
-1 0 0 0 1 1 0 5 R -4 -3 0 0 -2 -1 -1
-1 0 1 2 3 6 V -2 -1 -1 -1 0 0 -2 -2 -2
-2 -2 -2 -2 4 M -5 -3 -2 -2 -1 -1 -3 -2 0 -1
-2 0 0 2 6 I -2 -3 -2 -1 -1 0 -2 -2 -2 -2
-2 -2 -2 4 2 5 L -6 -4 -3 -3 -2 -2 -4 -3 -3
-2 -2 -3 -3 2 4 2 6 F -4 -5 -5 -3 -4 -3 -6
-5 -4 -5 -2 -5 -4 -1 0 1 2 9 Y 0 -5 -5 -3 -3
-3 -4 -4 -2 -4 0 -4 -5 -2 -2 -1 -1 7 10 W -8 -7
-6 -2 -6 -5 -7 -7 -4 -5 -3 -3 2 -6 -4 -5 -2 0 0
17 C G P S A T D E N Q H K R V M
I L F Y W
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BLOSUM62
G 7P -2 9 D -1 -1 7 E -2 0 2 6 N 0 -2 2
0 6 H -2 -2 0 0 1 10 Q -2 -1 0 2 0 1 6 K
-2 -1 0 1 0 -1 1 5 R -2 -2 -1 0 0 0 1 3
7 S 0 -1 0 0 1 -1 0 -1 -1 4 T -2 -1 -1 -1
0 -2 -1 -1 -1 2 5 A 0 -1 -2 -1 -1 -2 -1 -1 -2
1 0 5 M -2 -2 -3 -2 -2 0 0 -1 -1 -2 -1 -1
6 V -3 -3 -3 -3 -3 -3 -3 -2 -2 -1 0 0 1 5 I
-4 -2 -4 -3 -2 -3 -2 -3 -3 -2 -1 -1 2 3 5 L -3
-3 -3 -2 -3 -2 -2 -3 -2 -3 -1 -1 2 1 2 5 F -3
-3 -4 -3 -2 -2 -4 -3 -2 -2 -1 -2 0 0 0 1 8 Y
-3 -3 -2 -2 -2 2 -1 -1 -1 -2 -1 -2 0 -1 0 0
3 8 W -2 -3 -4 -3 -4 -3 -2 -2 -2 -4 -3 -2 -2 -3
-2 -2 1 3 15 C -3 -4 -3 -3 -2 -3 -3 -3 -3 -1
-1 -1 -2 -1 -3 -2 -2 -3 -5 12 G P D E N H
Q K R S T A M V I L F Y W C
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The genetic code
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Needleman-Wunch

• The best global alignment can be found as the
  best path through a substitution matrix. The
  optiomal path can be found by incremental
  extension of a subpath.
• The Smith-Waterman algorithm extends this to
  optimal local alignment.
• The main disadvantage is that finding the optimal
  alignment is very computing intensive

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Needleman-Wunch alignment scheme
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Needleman-Wunch
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Smith-Waterman
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FastA - BLAST

• Present day homology search methods are all
  word-based. The idea is that a true relationship
  will have at least one word (two or more
  residues) in common.
• FastA use the parameter ktup for number of
  identical residues that form the basis of a local
  alignment.
• BLAST has extended this to neighborhood words.
  For a given word size W the score has to be at
  least T using a substitution matrix. W is usually
  kept constant while varying T.The original
  implementation did not allow for gaps, but has
  been implemented from v. 2.0.

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Alignments - seeding
Smith-Waterman
Word seeding
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Clustering and extension
Word clusters Words on the same diagonal
Extending words
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Multiple alignments
Rat_CALRETICULIN ----MLLSVPLLLGLLGLAAAD-------
---------------------------PAIYFKEQFLDGDAWTNR-----
----WVESKHKSD--FGKFVL Human_CALRETICULIN
----MLLSVPLLLGLLGLAVAE----------------------------
------PAVYFKEQFLDGDGWTSR---------WIESKHKSD--FGKFVL
RAT_CALNEXIN MEGKWLLCLLLVLGTAAIQAHDGHDDD
MIDIEDDLDDVIEEVEDSKSKSDTSTPPSPKVTYKAPVPTGEVYFADSFD
RGSLSGWILSKAKKDDTDDEIAK Human_CALNEXIN
MEGKWLLCMLLVLGTAIVEAHDGHDDDVIDIEDDLDDVIEEVEDSKPDT-
TAPPSSPKVTYKAPVPTGEVYFADSFDRGTLSGWILSKAKKDDTDDEIAK
. .
.
. .. Prim.cons.
MEGK2LL2V2L2LG22GLAA2DGHDDD2IDIEDDLDDVIEEVEDSK222D
T22P2SP2V22K22222G2V22A2SFDRG2LSGWI2SK2K2DDT222222
Rat_CALRETICULIN SSGKFYGDQEK------DKGLQTSQD
ARFYALSARF-EPFSNKGQTLVVQFTVKHEQNIDCGGGYVKLFPGG--LD
QKDMHGDSEYNIMFGPDICGPGTK Human_CALRETICULIN
SSGKFYGDEEK------DKGLQTSQDARFYALSASF-EPFSNKGQTLVVQ
FTVKHEQNIDCGGGYVKLFPNS--LDQTDMHGDSEYNIMFGPDICGPGTK
RAT_CALNEXIN YDGKWEVDEMKETKLPGDKGLVLMSRA
KHHAISAKLNKPFLFDTKPLIVQYEVNFQNGIECGGAYVKLLSKTSELNL
DQFHDKTPYTIMFGPDKCG-EDY Human_CALNEXIN
YDGKWEVEEMKESKLPGDKGLVLMSRAKHHAISAKLNKPFLFDTKPLIVQ
YEVNFQNGIECGGAYVKLLSKTPELNLDQFHDKTPYTIMFGPDKCG-EDY
. .
. . . ....
.. . Prim.cons.
22GK222DE2KE2KLPGDKGL22222A222A2SAK2N2PF222222L2VQ
22V22222I2CGG2YVKL22KT2EL22D22H2222Y2IMFGPD2CGP222
Rat_CALRETICULIN KVHVIFNYKGKNVLINKDIRCK----
------DDEFTHLYTLIVRPDNTYEVKIDNSQVESGSLEDDWD--FLPPK
KIKDPDAAKPEDWDERAKIDDPTD Human_CALRETICULIN
KVHVIFNYKGKNVLINKDIRCK----------DDEFTHLYTLIVRPDNTY
EVKIDNSQVESGSLEDDWD--FLPPKKIKDPDASKPEDWDERAKIDDPTD
RAT_CALNEXIN KLHFIFRHKNPKTGVYEEKHAKRPDAD
LKTYFTDKKTHLYTLILNPDNSFEILVDQSVVNSGNLLNDMTPPVNPSRE
IEDPEDRKPEDWDERPKIADPDA Human_CALNEXIN
KLHFIFRHKNPKTGIYEEKHAKRPDADLKTYFTDKKTHLYTLILNPDNSF
EILVDQSVVNSGNLLNDMTPPVNPSREIEDPEDRKPEDWDERPKIPDPEA
... . .
. . .
. . Prim.cons.
K2H2IF22K22222I222222KRPDADLKTYF2D22THLYTLI22PDN22
E222D2S2V2SG2L22D22PP22P222I2DP22RKPEDWDER2KIDDPT2
Rat_CALRETICULIN SKPEDWDK------------------
---PEHIPDPDAKKPEDWDEEMDGEWEP-------------------PVI
QNPEYKGEWKPRQIDNPDYKGTWI Human_CALRETICULIN
SKPEDWDK---------------------PEHIPDPDAKKPEDWDEEMDG
EWEP-------------------PVIQNPEYKGEWKPRQIDNPDYKGTWI
RAT_CALNEXIN VKPDDWDEDAPSKIPDEEATKPEGWLDD
EPEYIPDPDAEKPEDWDEDMDGEWEAPQIANPKCESAPGCGVWQRPMIDN
PNYKGKWKPPMIDNPNYQGIWK Human_CALNEXIN
VKPDDWDEDAPAKIPDEEATKPEGWLDDEPEYVPDPDAEKPEDWDEDMDG
EWEAPQIANPRCESAPGCGVWQRPVIDNPNYKGKWKPPMIDNPSYQGIWK


.
. Prim.cons.
2KP2DWD2DAP2KIPDEEATKPEGWLDDEPE2IPDPDA2KPEDWDE2MDG
EWE2PQIANP2CESAPGCGVWQRPVI2NP2YKG2WKP22IDNPDY2G2W2

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Implementing a search algorithm

• SensitivityYou dont want to miss anything.
• SelectivityYou dont want any false positives.
• SpeedYou dont want to wait.

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BLAST

• BLAST Basic Local Alignment Sequence Tool
• Although the program handles both nucleotide and
  protein sequences you should translate to protein
  sequence if you have a coding region.
• Always use the longest sequence possible.
• Concentrate on regions having conserved
  residues Trp, Cys, Phe, Tyr, Pro.
• You cannot expect to locate a sequence in the
  database if your search sequence is very short
  (residues.
• If not successful, try varying the parameters.

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BLAST

• You start out by selecting the program to run
  (old style)
• BLASTP protein against protein
• BLASTN nucleotide against nucleotide
• BLASTX nucleotide translated in three frames
  against protein
• TBLASTN protein sequence against nucleotide
  translated in all three frames.
• TBLASTX six-frame translation of query sequence
  against six-frame translation of nucleotide
  database

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BLAST
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BLAST
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BLAST
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BLAST parameters

• The Expect threshold is the cut-off value for
  expected scores. Select a high value (10000) when
  searching for low similarity (e.g. short
  sequences) a low value (10) when searching for
  high similarity (long sequences).
• Word size (2 or 3) is the size of the sequence
  chosen for initial comparison (word size W). For
  short sequences choose 2.

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BLAST parameters

• The Scoring matrix can be changed to search for
  closely matching or diverging sequences. BLOSUM90
  has been calculated for analysing very similar
  sequences while BLOSUM30 for highly diverging
  sequences. Low PAM values are for higher degrees
  of similarity.
• The Gap Costs finetunes the search for divergent
  sequences when having a long search
  sequence.High values give a more stringent
  search.Use high values for short sequences and
  low values for long sequences.
• Compositional adjustments changes the scoring
  matrix to reflect the composition of the
  sequences to be compared.

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BLAST parameters

• Filtering will remove low complexity regions
  (regions that have residues that are
  overrepresented) and repetitive elements (like
  Alu repeats in nucleotide sequences). Should be
  turned off for short sequences.
• Mask for lookup Extensions of the initial hit
  are not removed by filtering.
• Mask lower case A sequence in upper case can be
  marked for filtering by changing part to lower
  case.

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BLAST
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Output options
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Waiting for results
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Results

• The graphical viewer displays hit sequences in
  color lines corresponding to score and position
  in sequence. Mouse-over displays target sequence
  name, and mouse click displays alignment.

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Results

• The results are listed with the highest scores at
  the top.
• Each line contains the accession number followed
  by name of protein, the score (higher is better)
  and the E-value (Expected the number of
  expected occurrences of a sequence with the given
  composition in the requested database).
• The E-value should be as low as possible (

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Results

• One or more HSPs (High Scoring Sequence Pair) is
  shown at after the scoring list for each
  protein.Query input sequence Sbjct database
  sequence Similar residues are marked by
  and are counted as part of positives

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Result distance tree
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BLAST distance tree