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Blast to Psi-Blast
• Blast makes use of Scoring Matrix
derived from large number of proteins.
• What if you want to find homologs
based upon a specific gene product?
• Develop a position specific scoring
matrix (PSSM).
PSSM
INDEL
M F W Y G A P V I L
C R K E N D Q S T H
M
5 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
G
1 0 0 0 1 0 0 0 0 1
0 0 0 1 0 0 1 0 0 0
A
1 0 0 0 0 4 0 0 0 0
0 0 0 0 0 0 0 0 0 0
S
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 3 2 0
F
0 4 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
Indel 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Can include a score for permitting insertions and deletions.
Perhaps this position is at a turn, where INDELs are common.
Utility of Y Blast
• Identify distantly related proteins based
upon the profile.
• These potential matches may suggest
functions.
• --Profile adds information only over
identified region of similarity.
Problem of approach:
• PSI-BLAST is iterative.
• Takes best hits and improves the
scoring matrix.
• Investigator must be certain that new
hits are correct.
• Investigator must be certain region of
interest is included in PSSM.
Multiple Sequence Alignment
Multiple Sequence Alignment
(MSA)
• Can define most similar regions in a set
of proteins
– functional domains
– structural domains
• If structure of one (or more) members is
known, may be possible to predict some
structure of other members
Multiple Sequence Alignment amino terminus of
Groucho
Poor alignment of N (and C) Terminus
Well conserved region, bordered by lower
similarity. What are the regions of lower
similarity?
MSA and Sequence Pair
Alignment
• Dynamic programming - (matrix
approach) provides an optimal
alignment between two sequences.
• Difficult for multiple alignment, because
the number of comparisons grows
exponentially with added sequences.
S
e
q
2
Optimal alignment
Seq 1
How to add a third sequence?
Complete all pair-wise comparisons.
Each added alignment imposes
boundaries on final MSA.
Optimal Multiple
Sequence Alignment
For more than three, problem
extends into N dimensional
space.
Scoring MSA
• Add scores derived from pair-wise
alignments.
• Sum of pairs (SP score).
• Gaps-constant penalty for any size of gap.
Progressive MSA
• Do pair-wise alignment
• Develop an evolutionary tree
• Most closely related sequences are then
aligned, then more distant are added.
• Genetic distance - number of mismatched
positions divided by the total number of
matched positions (gaps not considered).
Example
• Domain: a segment of a protein that can
fold to a 3D structure independent of
other segments of the protein.
• Card Domain
• Caspase recruitment domains (CARDs) are modules of
90 - 100 amino acids involved in apoptosis signaling
pathways.
•
http://www.mshri.on.ca/pawson/card.html
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Previous tree was Rooted
These are Unrooted trees
Gaps
• Clustalw attempts to place gaps
between conserved domains.
• In known sequences, gaps are
preferentially found between secondary
structure elements (alpha helices, beta
strands).
These are equivalent trees
A
B
B
A
C
C
C
C
A
B
B
A
Problem with Progressive
Alignment: Errors made in
early alignments are
propagated throughout the
MSA
Profiles & Gaps
• From an MSA, a conserved region
identified and a scoring matrix (profile)
constructed for that region.
• Each position has a score associated
with an amino acid substitution or gap.
• Blocks- also extracted from MSA, but no
gaps are permitted.
• Block Server
• http://blocks.fhcrc.org/blocks/blocks_search.html
• Results
Hidden Markov Models
• Probabilistic model of a Multiple
sequence alignment.
• No indel penalties are needed
• Experimentally derived information can
be incorporated
• Parameters are adjusted to represent
observed variation.
• Requires at least 20 sequences
The bottom line of states are the main states (M)
•These model the columns of the alignment
The second row of diamond shaped states are called the insert states (I)
•These are used to model the highly variable regions in the alignment.
The top row or circles are delete states (D)
•These are silent or null states because they do not match any residues, they simply
allow the skipping over of main states.
D1
D2
D3
D4
D5
D6
I4
I5
I6
I0
I1
I2
I3
B
M1
M2
M3
M4
M5
M6
E
The Evolution of a Sequence
• Over long periods of time a sequence will
acquire random mutations.
– These mutations may result in a new amino acid
at a given position, the deletion of an amino acid,
or the introduction of a new one.
– Over VERY long periods of time two sequences
may diverge so much that their relationship can
not see seen through the direct comparison of
their sequences.
Hidden Markov Models
• Pair-wise methods rely on direct comparisons
between two sequences.
• In order to over come the differences in the
sequences, a third sequence is introduced, which
serves as an intermediate.
• A high hit between the first and third sequences as
well as a high hit between the second and third
sequence, implies a relationship between the first
and second sequences. Transitive relationship
Introducing the HMM
• The intermediate sequence is kind of
like a missing link.
• The intermediate sequence does not
have to be a real sequence.
• The intermediate sequence becomes
the HMM.
Introducing the HMM
• The HMM is a mix of all the sequences
that went into its making.
• The score of a sequence against the
HMM shows how well the HMM serves
as an intermediate of the sequence.
– How likely it is to be related to all the other
sequences, which the HMM represents.
Match State with no Indels
MSGL
MTNL
B
M1
M2
M3
M4
Arrow indicates transition probability.
In this case 1 for each step
E
Match State with no Indels
MSGL
MTNL
B
M=1
S=0.5
T=0.5
M1
M2
M3
M4
E
Also have probability of Residue at each positon
Typically want to incorporate small probability
for all other amino acids.
MSGL
MTNL
B
M=1
S=0.5
T=0.5
M1
M2
M3
M4
E
Permit insertion states
MS.GL
MT.NL
MSANI
I0
I1
I2
I3
I4
B
M1
M2
M3
M4
Transition probabilities may not be 1
E
Permit insertion states
MS..GL
MT..NL
MSA.NI
MTARNL
I0
I1
I2
I3
I4
B
M1
M2
M3
M4
E
MS..GL-MT..NLAG
MSA.NIAG
MTARNLAG
DELETE PERMITS INCORPORATION OF
LAST TWO SITES OF SEQ1
D1
D2
D3
D4
D5
D6
I4
I5
I6
I0
I1
I2
I3
B
M1
M2
M3
M4
M5
M6
E
The bottom line of states are the main states (M)
•These model the columns of the alignment
The second row of diamond shaped states are called the insert states (I)
•These are used to model the highly variable regions in the alignment.
The top row or circles are delete states (D)
•These are silent or null states because they do not match any residues, they simply
allow the skipping over of main states.
D1
D2
D3
D4
D5
D6
I4
I5
I6
I0
I1
I2
I3
B
M1
M2
M3
M4
M5
M6
E
Dirichlet Mixtures
• Additional information to expand
potential amino acids in individual sites.
• Observed frequency of amino acids
seen in certain chemical environments
– aromatic
– acidic
– basic
– neutral
– polar