Pairwise sequence alignments

1
Pairwise sequence alignments

• Dynamic programming (Needleman-Wunsch), finds
  optimal alignment
• Heuristics Blast (Altschul et al) does not
  guarantee finding optimal alignment, but fast

2
Pairwise sequence alignments

• APLFVA----ITRSDD
• APVFIAGDTRITRSEE
• Assumptions
• evolution of sequences through mutations and
  deletions/insertions
• the closer similarity between sequences, the more
  chances they are evolutionarily related.

3
Similarity measures Percent Identity
Identity score Exact matches receive score of
1 and non-exact matches score of
0 AVLILKQW AVLI I LQ T -------------------------
----- 1 1 1 1 0 0 1 0 5 (Score of
the alignment under identity) Percent
identity identity_score/length_of_the_shorter_pro
tein
Disadvantage of id does not take into account
the similarity between their properties.
4
Substitution Matrices measure of similarity
score of amino-acids

• M(i,j) probability of substituting i into j
  over some time period
• Percent Accepted Mutation (PAM) unit
  evolutionary time corresponding to average of 1
  mutation per 100 res.
• Two most popular classes of matrices
• PAMn relates to mutation probabilities in
  evolutionary interval of n PAM units (PAM 120 is
  often used in practice)
• BLOSUMx relates to mutation probabilities
  observed between pairs of related proteins that
  diverged so above x identity.
• BLOSUM62 PAM250

5
Scoring the gaps
The two alignments below have the same score.
The second alignment is better.
ATTTTAGTAC ATT- - AGTAC
ATTTTAGTAC A-T-T -AGTAC

• Solution Have additional penalty for opening a
  gap

Affine gap penalty
w(k) h gk h,g constants
Interpretation const of starting a gap hg,
extending gap g
6
Dot plot illustration
Adapted from T. Przytycka
The alignment corresponds to path from upper
left corner to lower right corner going trough
max. nr of dots
Deletions
TTACTCAAT - - - - - ACTCA- TTAC
7
Gap penalties
Consider two pairs of alignments
ATCG ATTG
AT C G AT T - G
They have the same score but the right
alignment is more likely from evolutionary
perspective (simpler explanation better
explanation)
and
and
AT - C - T A AT T T T TA
ATC - - T A ATT T T TA

• First problem is corrected by introducing gap
  penalty for each gap subtract gap penalty from
  the score
• Second problem is corrected by introducing
  additional penalty for opening a gap

Affine gap penalty
w(k) h gk h,g constants
Interpretation const of starting a gap hg,
extending gap g
8
Organizing the computation dynamic programming
table
Align
j
Align(i,j)
Align(Si,Sj) max
i

Align(Si-1,Sj-1) s(ai, aj) Align(Si-1,Sj) -
g Align(Si,Sj-1) - g
s(ai,aj)
max
9
Recovering the path
A T T G
A T G C

• A T T G -
• A T - G C

10
Ignoring initial and final gaps semiglobal
comparison
CAGCA - CTTGGATTCTCGG - - - CAGCGTGG - - - - -
- - -
No penalties for these gaps
Recall the initialization step for the dynamic
programming table A0,i, Aj,0 these are
responsible for initial gaps.
set them to zero! How to ignore
final gaps?
Take the largest value in the last row /column
and trace-back form there
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Comparing similar sequences
Similar sequences optimal alignment has small
number of gaps.
The alignment path stays close to the diagonal
From book Setubal MeidanisIntroduction Comp.
Mol. Biol
12
Local and global alignments
Global Local
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Local alignment (Smith - Waterman)
So far we have been dealing with global
alignment. Local alignment alignment between
substrings. Main idea If alignment becomes too
bad drop it.
ai-1,j-1 s(ai, aj) ai-1,j g ai,j-1 g 0

ai,j max
14
Example
15
BLAST

• Local heuristics
• Fast
• Good statistics
• Precalculated lookup table of all high score word
  matches of three residue long
• Extend the hit until score drops below some
  threshold

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Sequence-profile alignments sequence profiles
describe conserved features with respect to
position in multiple alignment
1 2 3 4 5 6 7
IDVVVVC --------------------------------------
- LDLV--C A 2 -2 -2 -1 -1
-1 -2 LDLVFVC -------------------------
-------------- ADIIFLI R -3 -2
-3 -3 -2 -2 -4 ---------------------------
------------ N -3 1 -4 -4 -2 -2
-4 --------------------------------------- D
-3 7 -4 -4 -3 -3 -4 -----------------
---------------------- C -2 -4 -2 -1
-2 -1 6 -------------------------------------
--.
Gribskov et al, PNAS, 1987 Schaffer et al,
Nucleic Acids Res., 2001
17
Computational aspects of protein structure
18
Examples of protein architecture
ß-sheet with all pairs of strands parallel
Architecture refers to the arrangement and
orientation of SSEs, but not to the connectivity.
ß-sheet with all pairs of strands anti-parallel
19
Examples of protein topology
Topology refers to the manner in which the SSEs
are connected.
Two ß-sheets (all parallel) with
different topologies.
20
Secondary structures are connected to form
motifs.
G.M. Salem et al. J. Mol. Biol. (1999) 287 969-981
21
Supersecondary structure Greek key motifs
G.M. Salem et al. J. Mol. Biol. (1999) 287 969-981
22
Some supersecondary structure motifs are
associated with specific functionDNA binding
motifs.
Helix-turn-helix motif recognizes specific
palindromic DNA sequence Zn-finger motif Zn
binds to two Cys and two His binds in tandems
along major groove
23
P-loop motif.
Sequence pattern G/AxxxxGK(x)S/T Function
mononucleotide binding
24
Calcium-binding motif.
Calcium-binding sequence pattern
DxD/NxDxxxE/DxxE Function binding of Ca(2)
calmodulin Ca-dependent signaling pathways
A.Lewit-Bentley S. Rety, 2000
25
Protein domains can be defined based on

• Geometry group of residues with the high contact
  density, number of contacts within domains is
  higher than the number of contacts between
  domains.
• - chain continuous domains
• - chain discontinous domains
• Kinetics domain as an independently folding
  unit.
• Physics domain as a rigid body linked to other
  domains by flexible linkers.
• Genetics minimal fragment of gene that is
  capable of performing a specific function.

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Domains as recurrent units of proteins.

• The same or similar domains are found in
  different proteins.
• Each domain has a well determined compact
  structure and performs a specific function.
• Proteins evolve through the duplication and
  domain shuffling.
• Protein domain classification based on comparing
  their recurrent sequence, structure and
  functional features Conserved Domain Database

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Conserved Domain Database (CDD).

• Protein domain classification based on comparing
  their recurrent sequence, structure and
  functional features Conserved Domain Database
• CDD represents a collection of multiple sequence
  alignments corresponding to different protein
  domains

28
CDD icludes a set of multiple sequence alignments.

• Accurate alignments since structure-structure
  alignments are reconciled with sequence
  alignments.
• Block-based alignments.
• Annotated alignments.
• Annotated functionally important sites.

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PSSMs for each CDD are calculated using observed
residue frequencies and relationships between
different residue types.

• 1 2 3 4 5 6 7
  IDVVVVC
• ---------------------------------------
  LDLV--I
• A 2 -2 -2 -1 -1 -1 -2
  LDLVFVI
• ---------------------------------------
  ADIIFLI
• R -3 -2 -3 -3 -2 -2 -4
• ---------------------------------------
  W(D,3) log( Q(D,3) / P(D) )
• N -3 1 -4 -4 -2 -2 -4
• ---------------------------------------
  P(D) background probability
• D -3 7 -4 -4 -3 -3 -4
• ---------------------------------------
  Q(D,3) estimated probability
• C -2 -4 -2 -1 -2 -1 6
  for residue D to be found in
• ---------------------------------------
  column 3.
• .
• .
• .

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How to annotate domains in a protein using CDD?

• To annotate domains in a protein
• - to find domain boundaries
• - to assign function(structure) for each
  domain
• For each query sequence perform CD-search.
• CD-search query sequence is compared with
  sequence profiles derived from CDD multiple
  sequence alignments.

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Classwork

• Retrieve 1WQ1 from MMDB, look at structural
  domains and domains annotated by CDD. How
  different are they?
• Pretend you do not know the structure of 1WQ1,
  perform the CD-search, annotate domain boundaries.

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Protein folds.

• Fold definition two folds are similar if they
  have a similar arrangement of SSEs (architecture)
  and connectivity (topology). Sometimes a few
  SSEs may be missing.
• Fold classification structural similarity
  between folds is searched using
  structure-structure comparison algorithms.
• There is a limited number of folds 1000 3000.

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Superfolds are the most populated protein folds.

• There are about 10 types of folds, the
  superfolds, to which about 30 of the other folds
  are similar.
• Superfolds are characterized by a wide range of
  sequence diversity and spanning a range of
  non-similar functions.

C.Orengo et al, 1994
34
Why do some folds are more populated than others?

• Thermodynamic stability?
• Fast folding?
• By chance, through the duplication processes?
• Perform essential functions?
• Symmetrical folds, emerged through the gene
  duplication?
• High supersecondary structure content, higher
  fraction of local interactions?

35
Distinguishing structural similarity due to
common origin versus convergent evolution.
Divergent evolution, homologs
Convergent evolution, analogs
36
TIM barrels

• Classified into 21 families in the CATH database.
• Mostly enzymes, but participate in a diverse
  collection of different biochemical reactions.
• There are intriguing common features across the
  families, e.g. the active site is always located
  at the C-terminal end of the barrel.

Catalytic and metal-binding residues aligned in
structure-structure alignments
Nagano, C. Orengo and J. Thornton, 2002
37
Functional diversity of TIM-barrels.
38
TIM barrel evolutionary relationships

• Sequence analyses with advanced programs such as
  PSI-BLAST have identified further relationships
  among the families.
• Further interesting similarities observed from
  careful comparison of structures, e.g. a
  phosphate binding site commonly formed by loops
  7, 8 and a small helix.
• In summary, there is evidence for evolutionary
  relationships between 17 of the 21 families.

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SCOP (Structural Classification of Proteins)

• http//scop.mrc-lmb.cam.ac.uk/scop/
• Levels of the SCOP hierarchy
• Family clear evolutionary relationship
• Superfamily probable common evolutionary origin
• Fold major structural similarity
• Class secondary structure content

40
CATH (Class, Architecture, Topology, Homologous
superfamily)

• http//www.biochem.ucl.ac.uk/bsm/cath/

41
Classwork

• Using SCOP and CATH classify four protein
  structures (1b5t, 1n8i, 1tph and 1hti).
• How different are the classifications produced by
  SCOP and CATH?
• Can these proteins be considered homologous?