Sequence Alignment
and Phylogeny
B I O I N F O R M A T I C S
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B I O L O G Y - M A T H - S
Dr Peter Smooker, peter.smooker@rmit.edu.au
Uses of alignments
1. To determine the relationship (ie: distance)
between two sequences (pair-wise alignment)
2. To search databanks for the presence of
homologues
3. To look for sequence conservation in families of
proteins
4. To use molecular approaches to phylogeny
Comments/Caveats
• When sequences are aligned, we assume they
share a common ancestor
• Protein fold is more conserved than protein
sequence
• DNA sequences are less informative than protein
sequences
• Two sequences can always be aligned- we need to
determine what is a meaningful result
Homology
• Proteins or genes are defined as
homologous if they can be said to have
shared an ancestor
• Genes or proteins are either homologs or
they are not- there is no such thing as
percent homology. There is percent identity
or similarity of the sequences
“Ologies”
• Homology - descent from a common
ancestor
• Orthology - descent from a speciation event
• Paralogy - descent from a duplication event
• Xenology - descent from a horizontal
transfer event
When Is Homology Real?
• As a general rule, in a pairwise alignment:
>25% identical aa’s, proteins will have similar
folding pattern- most likely homologous
18-25% identical- twilight zone- tantalizing
<18% identical- cannot determine from alignment
Measuring Sequence Similarity
•
Two measures of the distance between two
strings:
1. Hamming distance: strings equal length,
number of positions with mismatches
2. Levenshtein distance: not equal length,
number of edit operations to change one
string to the other
agtc
cgta
Hamming distance = 2
ag-tcc
cgctca
Levenshtein distance = 3
Protein AlignmentsSubstitution Matrices
• When sequences diverge over time, they
accumulate mutations- some are deleterious,
some are neutral, some are advantageous
• Some changes are more likely than others
• This can be examined and the relative
probability of a change occurring calculated
• Substitution matrices have been developed
Matrices.
• PAM = Percent Accepted Mutation
• Matrices are derived from families of
proteins with a set level of identity.
• PAM matrices proposed by Margaret
Dayhoff. Based on sequences with > 85%
identity. The PAM 1 matrix was computed.
Extrapolated for larger evolutionary
distances
PAM Matrices
PAM
% identity
0 30 80 110 200 250
100 75 50 60 25 20
• The PAM250 matrix is corresponds to proteins of
average 20% identity (lowest we can reasonably
be confident about). It was derived by the
extrapolation of observed substitution frequencies.
PAM250 refers to 250 substitutions per 100 amino
acids.
Definition of PAM from
BLAST literature
• http://www.ncbi.nlm.nih.gov/BLAST/tutorial/Alt
schul-1.html
• One "PAM" corresponds to an average change in 1%
of all amino acid positions. After 100 PAMs of
evolution, not every residue will have changed: some
will have mutated several times, perhaps returning to
their original state, and others not at all. Thus it is
possible to recognize as homologous proteins
separated by much more than 100 PAMs. Note that
there is no general correspondence between PAM
distance and evolutionary time, as different protein
families evolve at different rates.
BLOSUM Matrices
• Developed by S and JG Henikoff
• Made use of a much larger amount of data
• Based on the BLOCKS database of aligned
protein domains
• http://www.blocks.fhcrc.org/
• Used a weighted average of closely related
sequences with identities higher than a threshold.
For example, the common BLOSUM62 matrix is
based on proteins with greater than 62% identity
BLOCKS
• The substitutions in each aligned column
are identified and a score for each
substitution calculated and inserted into the
matrix.
Which Matrix to use?
•
•
•
•
•
•
•
In BLASTP, the following matrices are offered:
PAM 30
PAM 70
BLOSUM 80
BLOSUM 62 (default)
BLOSUM 42
In PAM, greater numbers = more evolutionary
distance. Reverse for BLOSUM
Which Matrix to use?
• Generally, BLOSUM perform better than PAM for
local alignment searches
• Use the matrix appropriate for the task- if you
expect a close match, use a low PAM or high
BLOSUM number
• Generally, if you use the default (generally
BLOSUM 62) and find nothing, go to a matrix
derived from a more evolutionarily distant dataset
Scoring
Score of mutation i > j
log observed i >j
expected i > j
Expected i > j is simply calculated by the
frequencies of the amino acids
Result is multiplied by 10. Scores are added.
PAM250
A
R
N
D
C
Q
E
G
H
I
L
K
M
F
P
S
T
W
Y
V
A
2
-2
0
0
-2
0
0
1
-1
-1
-2
-1
-1
-4
1
1
1
-6
-3
0
R
N
D
C
Q
E
G
H
I
L
K
M
6
0
-1
-4
1
-1
-3
2
-2
-3
3
0
-4
0
0
-1
2
-4
-2
2
2
-4
1
1
0
2
-2
-3
1
-2
-4
-1
1
0
-4
-2
-2
4
-5
2
3
1
1
-2
-4
0
-3
-6
-1
0
0
-7
-4
-2
4
-5
-5
-3
-3
-2
-6
-5
-5
-4
-3
0
-2
-8
0
-2
4
2
-1
3
-2
-2
1
-1
-5
0
-1
-1
-5
-4
-2
4
0
1
-2
-3
0
-2
-5
-1
0
0
-7
-4
-2
5
-2
-3
-4
-2
-3
-5
-1
1
0
-7
-5
-1
6
-2
-2
0
-2
-2
0
-1
-1
-3
0
-2
5
2
-2
2
1
-2
-1
0
-5
-1
4
6
-3
4
2
-3
-3
-2
-2
-1
2
5
0
-5
-1
0
0
-3
-4
-2
6
0
-2
-2
-1
-4
-2
2
F
P
S
T
W
Y
V
9
-5 6
-3 1 3
-2 0 1 3
0 -6 -2 -5 17
7 -5 -3 -3 0 10
-1 -1 -1 0 -6 -2
4
• Scores below 0 indicate amino acids that are
rarely substituted, and different aa’s that
give a high +ve score are usually
functionally equivalent
• Scores below 0 indicates that those
substitutions are rarely observed
• Hydrophilic
• These aa’s are hydrophobic (except glycine, often
put in a class by itself).
Interpreting scores- BLAST output
Significance
• Two values are given- the Bit score and the Evalue.
• The E-value is a statistical calculation of the
probability that the match is real, ie: that in a
query database of that size, the sequence would
give that score by chance
• The bit score is related to both the raw score
(calculated from the BLOSUM or PAM lookup
matrix) but is normalised
Bit Score
• Bit scores are normalised with respect to the
scoring system. Hence they can be compared
across different searches (using different matrices)
• In particular:
To convert a raw score S into a normalized score
S' expressed in bits, one uses the formula S' =
(lambda*S - ln K)/(ln 2), where lambda and K are
parameters dependent upon the scoring system
(substitution matrix and gap costs) employed
Multiple Sequence Alignment
• To quote Lesk
• “One amino acid sequence plays coy; a pair
of homologous sequences whisper; many
aligned sequences shout out loud”
Multiple Sequence Alignment
• Multiple sequence alignments can offer a
considerable amount of information over a
pairwise alignment.
– Regions of similarity (especially distant
similarity) can be detected
– Regions of functional significance can often be
detected
– Evolutionary relationships can be examined,
and trees drawn.
MSA’s are computationally
expensive
• If we use dynamic programming, rather
than a 2D array as for pairwise comparison,
have an n-dimensional array. Computational
time grows as Mn, where n is the number of
sequences. Difficult for n=4, impossible for
higher values.
• Use a heuristic approach. Most common is
the CLUSTAL algorithm
Progressive Alignment
• Iterative pairwise alignment
• Two most similar sequences aligned first,
then next most similar to that pair, etc.
• A very popular progressive alignment
algorithm is CLUSTAL W
CLUSTAL W- Steps
• A matrix of pairwise distances between all
sequences is constructed. This determines the
similarity between all sequences to be aligned.
• A guide tree (dendogram), or inferred phylogeny,
is built
• The alignment is constructed based on the guide
tree.
• Generally results in a near-optimal alignment
CLUSTAL W
• A major problem in MSA is the selection of an
appropriate matrix for alignments consisting of
divergent and closely related sequences
• CLUSTAL W (weighted) assigns weights to a
sequence dependent on how divergent it is from
the two most closely related sequences
• Adapts gap penalties and scoring matrix to suit
An example (from our
research)
• Some definitions:
• Phylogeny: Evolutionary history (“tree of life”)
• Molecular phylogeny: Determined using sequence
data
• Bootstrapping: A statistical process to evaluate
phylogenetic trees. The data is resampled 1000
times (generally) and the support for each branch
determined
• Homology modelling. Predicting the structure of a
protein based on the experimentally derived
structure of a homologue
Fasciola- Liver Fluke
NEJ
Adult
Liver fluke (Fasciola spp.)
•
•
•
•
Trematode (flatworm) parasite
Infects ruminants, humans
Has a complex life-cycle
Secretes proteins (excretory/secretory
material)
• Major secreted protein is cathepsin L in
adults
Cysteine proteases
• Digest proteins: cleave between adjacent
amino acids.
• Not random cleavage, different proteases
show a preference for different targets.
There are a number of Fasciola
cathepsin L sequences known.
• At least 30 full sequences now known
• Only one contains an indel
• Protein sequences 46-99% identical
What are the differences
between the two classes of CatL
that account for the substrate
specificity?
Presumed to be due to changes
affecting the S2 subsite of the
enzyme.
Homology Modelling
• FhCatL modelled on the known crystal
structure of human CatL.
• Models of CatL2 and CatL5 (functional
equivalent of CatL1) compared, especially
around the S2 subsite of the enzyme.
Homology Modelling
• Three substitutions is residues lining the S2
subsite were observed (L5-> L2)
• L69Y: Makes substantial contacts with the
P2 Phe
• N161T: Side chain points away from pocket
• G163A: Bottom of pocket, no substantial
contact with P2 Phe
L2
L5
GRASP electrostatic surface potential
The architecture around the S2 pocket is substantially influenced
by a Y or L at position 69.
Made mutant, expressed in yeast, performed kinetic analysis.
Conclusions
• The L69Y change does affect the substrate
specificity
• 69Y allows increased catalysis of substrates
with a P2 proline
• There are other, more subtle changes
between L5 and L2
What about the other enzymesCLUSTAL W
What amino acid is at #69?
FgCatL1-a
61 GNMGCSGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV 120
FgCatL1-b
GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FgCatL1-c
GNFGCNGGLMENACEYLKRFGLETESSYPYRAVEGPCRYNKQLGVAKVTGYYMVHSGDEV
FgCatL1-d
GNHGCGGGYMENAYEYLKHSGLETDSYYPYQAVEGPCQYDGRLAYAKVTDYYTVHSGDEV
FgCatL1-e
GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEDQCRYNRQLGVAKVTDYYTVHSGSEV
FgCatL1-f
GNNGCRGGLMEIAYEYLRRFGLEIESTYPYRAVEGPCRYDRRLGVAKVTGYYIVHSGDEV
FgCatL2
GNMGCSGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FgCatL3
GNINCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FhCatL1
GNNGCGGGLMENAYQYLKQFGLETESSYPYTAVGGQCRYNKQLGVAKVTGYYTVQSGSEV
FhCatL2
GNYGCGGGYMENAYEYLKHNGLETESYYPYQAVEGPCQYDGRLAYAKVTGYYTVHSGDEI
FhCatL3
GNNGCSGGLMENAYQYLKQFGLETESSYPYTAVEGQCRYNKQLGVAKVTGYYTVHSGSEV
FhCatL4
GNYGCNGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTGYYTVHSGDEV
FhCatL5
GNYGCNGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTGYYTVHSGDEV
FhCatL6
GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FhCatL7
GNYGCGGGYMENAYEYLKHNGLETESYYPYQAVEGPCQYDGRLAYAKVTGYYTVHSGDEI
FhCatL8
GNHGCGGGWMENAYKYLKNSGLETASYYPYQAVEYQCQYRKELGVAKVTGAYTVHSGDEM
FhCatL9
GNNGCSGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTGYYTVHSGSEV
FhCatL10
GNHGCGGGWMENAYKYLKNSGLETASDYPYQGWEYQCQYRKELGVAKVTGAYTVHSGDEM
** .* ** ** * :**:. *** * *** .
*:* .*. ****. * *:**.*:
Fasciola CatL’s form a
monophyletic clade
• Fasciola sequences aligned to the family of
papain-like cysteine proteases
• 100% bootstrap support for clade
• All Fasciola sequences arose after
divergence from Schistosoma
• Probably all parasitic catLs have diverged
after speciation (Sajid and McKerrow)
Relationship of Fasciola
enzymes
• Tree constructed using 18 full-length
sequences
• Resolved into 4 distinct clades
AA69
L69
Predicted
Substrate
Phe-Arg
L69
Phe-Arg
Y69
Pro-Arg
W69
??-Arg
Evolutionary Timeframe
• First observed divergence (clade A) 135
MYA
• F. hepatica and F. gigantica predicted to
diverge approx. 19 –25 MYA
• Confirmed by constructing a neighbourjoining tree using Glutathione-S transferase
sequences: 19 +/- 5.2 MYA
Practice runs- 1. Blast
• Go to the BLAST server at NCBI
• http://www.ncbi.nlm.nih.gov/BLAST/
• Note the different “flavours” of BLAST that
can be performed.
• Go to Protein-Protein BLAST. Look at the
format and the searching parameters.
• Paste in sequence 1 and run the BLAST
Sequence 1
• What is it? (note that a conserved domain is
detected)
• From what organism (should be 100%
match)?
• What is the organism that has the closest
relative?
• What is meant by “positives”?
• For interest, use sequence 2 to run a
BLAST. This is the mRNA sequence from
which the protein sequence is translated.
(note- choose your BLAST flavour
carefully!)
• Is the same result obtained?
Practice runs- 2. CLUSTAL W
• Go to http://www.ebi.ac.uk/clustalw/
• Upload (or paste) Seq3.txt, run the tool
• Does the dendogram resemble that
previously demonstrated?