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Multiple Alignments & Molecular
Evolution
Prof:Rui Alves
[email protected]
973702406
Dept Ciencies Mediques Basiques,
1st Floor, Room 1.08
Website of the
Course:http://web.udl.es/usuaris/pg193845/Courses/Bioinformatics_2007/
Course: http://10.100.14.36/Student_Server/
Part I: Multiple Alignments
Pairwise Alignment
• We have seen how pairwise alignments are
made.
• Dynamic programming is an efficient algorithm
for finding the optimal alignment.
– Break problem into smaller subproblems
– Solve subproblems optimally, recursively
– Use optimal solutions to construct an optimal solution
for the original problem
• Alignments require a substitution (scoring)
matrix that accounts for gap penalties.
Sub. Matrix: Basic idea
• Probability of substitution (mutation)
PAM Matrices
• A family of matrices (PAM-N)
• Based upon an evolutionary model
• The score for a substitution of nucleotides/amino
acids is based on how much we expect that
substitution to be observed after a certain length of
evolutionary time
• The scores are derived by a Markov model – i.e.,
the probability that one amino acid will change to
another is not affected by changes that occurred at
an earlier stage of evolutionary history
Nucleic acid PAM matrices
• PAM = point accepted mutation
• 1 PAM = 1% probability of mutation at each sequence
position.
• A uniform PAM1 matrix for a familiy of closely related
proteins:
A
G
T
C
A
0.99
0.00333
0.00333
0.00333
G
0.00333
0.99
0.00333
0.00333
T
0.00333
0.00333
0.99
0.00333
C
0.00333
0.00333
0.00333
0.99
How did they get the values for
PAM-1?
• Look at 71 groups of protein sequences
where the proteins in each group are at least
85% similar (Why these groups?)
• Compute relative mutability of each amino
acid – probability of change
• From relative mutability, compute mutability
probability for each amino acid pair X,Y–
probability that X will change to Y over a
certain evolutionary time
• Normalize the mutability probability for each
pair to a value between 0 and 1
Transitions and transversions
• Transitions (A  G or C  T) are more likely
than transversions (A  T or G  C)
• Assume that transitions are three times as likely:
A
G
T
C
A
0.99
0.006
0.002
0.002
G
0.006
0.99
0.002
0.002
T
0.002
0.002
0.99
0.006
C
0.002
0.002
0.006
0.99
PAM-N Matrices
• N is a measure of evolutionary distance
• PAM-1 is modeled on an estimate of how long in
evolutionary time it would take one amino acid
out of 100 to change. That length of time is
called 1 PAM unit, roughly 10 million years
(abbreviated my).
• Values in a PAM-1 matrix show the probability
that an amino acid will change over 10 my.
• To get the PAM-N matrix for any N, multiply
PAM-(N-1) by PAM-1.
Distant relatives
• If a family of proteins is say, 80% homologous
use a PAM 2.
A
G
T
C
A
0.98014
0.011888
0.003984
0.003984
G
0.011888
0.98014
0.003984
0.003984
T
0.003984
0.003984
0.98014
0.011888
C
0.003984
0.003984
0.011888
0.98014
Computing Relative Mutability – A
Measure of the Likelihood that an
Amino Acid Will Mutate
For each amino acid
• changes = number of times the amino acid
changed into something else
• exposure to mutation =
(percentage occurrence of the amino acid in
the group of sequences being analyzed) *
(frequency of amino acids changes in the
group)
• relative mutability =
(changes/exposure to mutation) / 100
Computing Relative Mutability of A:
changes = # times A changes into something else = 4
% occurrence of A in group = 10 / 63 = 0.159
frequency of all amino acid changes in group = 6 * 2 = 12
(Note: Count changes backwards and forwards.)
exposure to mutation = (% occurrence of A in group)
* (frequency of all amino acid changes in group)
= 12 * 0.159
relative mutability = (changes / exposure to mutation) / 100
= (4 / (12 * 0.159)) = 2.09 / 100 = 0.0209
Example from Fundamental Concepts of Bioinformatics by Krane and Raymer.
How can we understand relative mutability intuitively?
relative mutability = changes / exposure to mutation =
the number of times A changed in proportion to the
the probability that it COULD have changed
exposure to mutation – that were 6 times when something
changed in the tree. Each time, that change could have been
A changing to something else, or something else
changing to A – 12 chances for a change involving A. But A
appears in a sequence only .159 of the time.
Computing
Mutability Probability Between
Amino Acid Pairs
For each pair of amino acids X and Y:
r = relative mutability of X
c = num times X becomes Y or vice versa
p = num changes involving X
mutability probability of X to Y =
(r * c) / p
Computing Mutability Probability that
A will change to G:
r = relative mutability of A = .0209
c = num times A becomes G or vice versa = 3
p = num changes involving A = 4
mutability probability of A to G =
(r * c) / p = (0.0209 * 3) / 4 = 0.0156
Normalizing
Mutability Probability, X to Y
• For each Y among all amino acids, compute
mutability probability of X to Y as described
above
• Get a total of these 20 probabilities. Divide
them by a normalizing factor such that the
probability that X will NOT change is 99% and
the sum of probabilities that it will change to
any other amino acid is 1%
• These are the numbers that go in the PAM-1
matrix!
Converting
Mutability Probabilities to
Log Odds Score for X to Y
• Compute the relative frequency of change for X to Y
as follows:
– Get the X to Y mutability probability
– Divide by the % frequency of X in the sequence data
– Convert to log base 10, multiply by 10
• In our example, we get log10(0.0156/0.1587) =
log10(.098)
• To compute log10(.098) solve for x:
– 10x = 0.098
x = -1.01
10-1.01 = 1/101.01 = 0.098
• Compute log odds score for Y to X
• Take the average of these two values
Usefulness of
Log Odds Scores
• A score of 0 indicates that the change from one
amino acid to another is what is expected by
chance
• A negative score means that the change is
probably due to chance
• A positive score means that the change is more
than expected by chance
• Because the scores are in log form, they can be
added (i.e., the chance that X will change to Y
and then Y to Z)
Disadvantages of
PAM Matrices
• An alignment tree must be constructed first,
implying some circularity in the analysis
• The original PAM-1 matrix was based on a
limited number of families, not necessarily
representative of all protein families
• The Markov model does not take into account
that multi-step mutations should be treated
differently from single-step ones
Most Commonly-Used Amino Acid
Subtitution Matrices
• PAM (Percent Accepted Mutation, also
called Dayhoff Amino Acid Substitution
Matrix)
• BLOSUM (BLOcks amino acid
SUbstitution Matrix)
BLOSUM Scoring Matrices
• Based on a larger set of protein families than PAM
(about 500 families). The proteins in the families are
known to be biochemically related.
• Focuses on blocks of conserved amino acid patterns in
these families
• Designed to find conserved domains in protein families
• BLOSUM matrices with lower numbers are more useful
for scoring matches in pairs that are expected to be
less closely related through evolution – e.g.,
BLOSUM50 is used for more distantly-related proteins
than BLOSUM62. (This is the opposite of the PAM
matrices.)
BLOSUM Matrices
• Target frequencies are identified
directly and not by extrapolation
• Sequences more than x% identical
are collapsed into a single sequence
–BLOSUM 50: >=50% Identity
–BLOSUM 62: >=62% Identity
Building a BLOSUM Matrix
• BLOSUM 62:
– Collapse Sequences that have more than
62% identity into one
– Calculate probability of a given pair of AAs
being in same column (qij)
– Calculate the frequency of a given AA (fi)
– Calculate log odds ratio sij=log2(qij/fi). This is
the value that goes into the BLOSUM matrix
BLOSUM50
What matrix to choose?
• BLOSUM Matrices perform better in local
similarity searches
• BLOSUM 62 is the default matrix used
for database searching
Gap Penalty
(Gap Scoring)
Gap Penalties
• Gaps in the alignment are necessary to
increase score.
• They must be penalized; however if
penalty is to high no gaps will appear
• On the other hand if they are too low,
gaps everywhere!!!
• The default settings of programs are
usually ok for their default scoring
matrices
Once a gap, can we widen it?
>gi|729942|sp|P40601|LIP1_PHOLU
Length = 645
Lipase 1 precursor (Triacylglycerol lipase)
Score = 33.5 bits (75), Expect = 5.9
Identities = 32/180 (17%), Positives = 70/180 (38%), Gaps = 9/180 (5%)
Query: 2038 IYSLYGLYNVPYENLFVEAIASYSDNKIRSKSRRVIATTLETVGYQTANGKYKSESYTGQ 2097
+++ YGL+
Y+ ++
Y D K
+R ++
+ N
+ G+
Sbjct: 441 VFTAYGLWRY-YDKGWISGDLHYLDMKYEDITRGIVLNDW----LRKENASTSGHQWGGR 495
Query: 2098 LMAGYTYMMPENINLTPLAGLRYSTIKDKGYKETGTTYQNLTVKGKNYNTFDGLLGAKVS 2157
+ AG+
+
+ +P+
+
KGY+E+G
+
+ Y++ G LG ++
Sbjct: 496 ITAGWDIPLTSAVTTSPIIQYAWDKSYVKGYRESGNNSTAMHFGEQRYDSQVGTLGWRLD 555
Query: 2158 SNINVNEIVLTPELYAMVDYAFKNKVSAIDARLQGMTAPLPTNSFKQSKTSFDVGVGVTA 2217
+N
P
++ F +K
I + +
+ S KQ
+ +G+ A
Sbjct: 556 TNFG----YFNPYAEVRFNHQFGDKRYQIRSAINSTQTSFVSESQKQDTHWREYTIGMNA 611
Real gaps are often more than one letter long.
Affine gap penalty
LETVGY
W----L
-5 -1 -1 -1
• Separate penalties for gap opening and gap
extension.
• This requires modifying the DP algorithm to
store three values in each box.
Scoring Gap Penalties
• Linear Gap Penalty Score
  gap    gap  (opening penalty score)
• Affine Penalty Score
  gap    (opening penalty score)
 (extension penalty )  ( gap  1)
• Opening a gap is costly; extending it not
so much (open=12; extension=1)
Multiple Sequence Alignment
MSA Introduction
• Goal of protein sequence alignment:
– To discover “biological” (structural / functional)
similarities
• If sequence similarity is weak, pairwise
alignment can fail to identify important
features (eg interaction residues)
• Simultaneous comparison of many
sequences often find similarities that are
invisible in PA.
Why do we care about sequence
alignment?
• Identify regions of a gene (or protein) susceptible to
mutation and regions where residue replacement does
not change function.
• Information about the evolution of organisms.
• Orthologs are genes that are evolutionarily related, have
a similar function, but now appear in different species.
• Homologous genes (genes with share evolutionary origin)
have similar sequences.
• Paralogs are evolutionarily related (share an origin) but
no longer have the same function.
• You can uncover either orthologs or paralogs through
sequence alignment.
Multiple Sequence Alignment
• Often applied to proteins (not very good
with DNA)
• Proteins that are similar in sequence are
often similar in structure and function
• Sequence changes more rapidly in
evolution than does structure and function.
Work with proteins!
If at all possible —
• Twenty match symbols versus four, plus
similarity! Way better signal to noise.
• Also guarantees no indels are placed within
codons. So translate, then align.
• Nucleotide sequences will only reliably align if
they are very similar to each other. And they will
require extensive hand editing and careful
consideration.
Overview of Methods
• Dynamic programming – too computationally
expensive to do a complete search; uses
heuristics
• Progressive – starts with pair-wise alignment of
most similar sequences; adds to that (LOCAL
OPTIMIZATION)
• Iterative – make an initial alignment of groups of
sequences, adds to these (e.g. genetic
algorithms) (GLOBAL OPTIMIZATION)
• Locally conserved patterns
• Statistical and probabilistic methods
Dynamic Programming
• Computational complexity – even worse
than for pair-wise alignment because
we’re finding all the paths through an ndimensional hyperspace (Remember
matrix, now add many dimensions)
• Can align less than 20 relatively short
(200-300) protein sequences in a
reasonable amount of time; not much
beyond that
A Heuristic for Reducing the
Search Space in
Dynamic Programming
• Consider the pair-wise alignments of each pair
of sequences.
• Create alignments from these scores.
• Consider a multiple sequence alignment built
from the individual pairwise alignments.
• These alignments circumscribe a space in which
to search for a good (but not necessarily optimal)
alignment of all n sequences.
The details
• Create an “alignment of alignments” (AOA) based on
pair-wise alignments (Pairs of sequences that have the
best scores are paired first in the tree.)
• Do a “first-cut” msa by incrementally doing pair-wise
alignments in the order of “alikeness” of sequences as
indicated by the AOA. Most alike sequences aligned first.
• Use the pair-wise alignments and the “first-cut” msa to
circumscribe a space within which to do a full msa that
searches through this solution space.
• The score for a given alignment of all the sequences is
the sum of the scores for each pair, where each of the
pair-wise scores is multiplied by a weight є indicating
how far the pair-wise score differs from the first-cut msa
alignment score.
Heuristic Dynamic Programming
Method for MSA
• Does not guarantee an optimal alignment
of all the sequences in the group.
• Does get an optimal alignment within the
space chosen.
Progressive Methods
• Similar to dynamic programming method in that
it uses the first step (i.e., it creates an AOA,
aligns the most-alike pair, and incrementally
adds sequences to the alignment.)
• Differs from dynamic programming method for
MSA in that it doesn’t refine the “first-cut” MSA
by doing a full search through the reduced
search space. (This is the computationally
expensive part of DP MSA.)
Progressive Method: the details
• Generally proceeds as follows:
– Choose a starting pair of sequences and align them
– Align each next sequence to those already aligned,
one at a time
• Heuristic method – doesn’t guarantee an optimal
alignment
• Details vary in implementation:
– How to choose the first sequence to align?
– Align all subsequence sequences cumulatively or in
subfamilies?
– How to score?
ClustalW
• Based on phylogenetic analysis
• A AOA is created using a pairwise distance matrix and
nearest-neighbor algorithm
• The most closely-related pairs of sequences are aligned
using dynamic programming
• Each of the alignments is analyzed and a profile of it is
created
• Alignment profiles are aligned progressively for a total
alignment
• W in ClustalW refers to a weighting of scores depending
on how far a sequence is from the root on the AOA
ClustalW Procedure
AOA
“Once a gap, always a gap”
Basic Steps in Progressive Alignment
“Once a gap, always a gap”
Problems with Progressive Method
• Highly sensitive to the choice of initial pair
to align. If they aren’t very similar, it
throws everything off.
• It’s not trivial to come up with a suitable
scoring matrix or gap penalties.
Part II: Molecular Evolution
Theory of Evolution
• Evolution is the theory that allows us to
understand how organisms came to be how they
are
•In probabilistic terms, it is likely that all living
beings today have originated from a single type of
cells
•These cells divided and occupied ecological
niches, where they adapted to the new
environments through natural selection
How did the first cell create
different cells?
Neutral Mutation (e.g.
by error in genome
replication)
How did the first cell create
different cells?
Neutral Mutation
Mutation (e.g. by error
in genome replication)
How did the first cell create
different cells?
Neutral Mutation Mutation (e.g. by
error in genome replication)
How did the first cell create
different cells?
Deleterious Mutation (e.g. by error in
genome replication)
How did the first cell create
different cells?
Deleterious Mutation Mutation (e.g. by
error in genome replication)
How did the first cell create
different cells?
Deleterious Mutation Mutation (e.g. by error in genome replication)
How did the first cell create
different cells?
Advantageous Mutation (e.g. by error in genome
replication)
How did the first cell create
different cells?
Neutral Mutation Mutation (e.g. by
error in genome replication)
And then there was sex…
Why Sex???
• Asexual reproduction is quicker, easier,  more
offspring/individual.
• Sex may limit harmful mutations
– Asexual: all offspring get all mutations
– Sexual: Random distribution of mutations. Those
with the most harmful ones tend not to reproduce.
• Generate beneficial gene combinations
–
–
–
–
Adaptation to changing environment
Adaptation to all aspects of constant environment
Can separate beneficial mutations from harmful ones
Sample a larger space of gene combinations
What drives cells to adapt?
New Niche/
New conditions in old niche
What drives cells to adapt?
New (better addapted)
mutation
How do New Genes and Proteins
appear?
• Genes (Proteins) are build by combining domains
• New proteins may appear either by intradomain
mutation of by combining existing domains of
other proteins
Cell
Cell Division
Division …
…
The Coalescent
•This model of cellular evolution has implications for
molecular evolution
•Coalescent Theory:
•a retrospective model of population genetics that traces all
alleles of a gene in a sample from a population to a single
ancestral copy shared by all members of the population, known
as the most recent common ancestor
Why is the coalescent the de
facto standard today?
Alternatives?
Current sequences have evolved from the same
original sequence (Coalescent)
Current sequences have converged to a similar
sequence from multiple origins of life
Back of the envelop support for
divergence
?
ACDEFGHIKLMNPQRSTVWY
A EDYAHIKLMNPQRGTVWY
AAi
AAk
AAi
AAk
AAk
AAk
   Log[ p1]  0
  Log[ p 2]  0
  Log[ p1]  0
  Log[ p 2]  0
AAi
ptot  p1 p 2
p1 p 2  p1  p 2
20
20
Convergence
ptot14 p16  p214 p120
Divergence
p 214 p16
Which is more likely?
Convergence
 p114  ()1
Divergence
About the mutational process
Point mutations:
• Transitions (A↔G, C↔T) are more frequent than transversions (all other
substitutions)
• In mammals, the CpG dinucleotide is frequently mutated to TG or CA (possibly
related to the fact that most CpG dinucleotides are methylated at the C-residues)
• Microsatellites frequently increase or decrease in size (possibly due to polymerase
slippage during replication)
Gene and genome duplications (complete or partial), may lead to:
• pseudogenes: function-less copies of genes which rapidly accumulate (mostly
deleterious) mutations, useful for estimating mutation rates!
• new genes after functional diversification
Chromosomal rearrangements (inversions and translocation), may lead to
• meiotic incompatibilities, speciation
Estimated mutation rates:
• Human nuclear DNA: 3-5×10-9 per year
• Human mitochondrial DNA: 3-5×10-8 per year
• RNA and retroviruses: ~10-2 per year
Consequences of the coalescent
model?
So what if we accept the coalescent model?
A1-6
TSRISEIRR
A7
PSRISEIRR
A8-9
PKRISEVRR
A10-11
PQRISAIQR
A12-13
PQRISTIQR
A14
ASHLHNLQR
A15-17
TKHLQELQR
A18
SKHLHELQR
A19
PKNLHELQK
A20
SKRLHEVQS
A1
TSRISEIRR
A2
TSRISEIRR
A3
TSRISEIRR
A4
TSRISEIRR
A5
TSRISEIRR
A6
TSRISEIRR
A7
PSRISEIRR
A8
PKRISEVRR
A9
PKRISEVRR
A10
PQRISAIQR
A11
PQRISAIQR
A12
PQRISTIQR
A13
PQRISTIQR
A14
ASHLHNLQR
A15
TKHLQELQRE
A16
TKHLQELQRE
A17
TKHLQELQRE
A18
SKHLHELQRD
A19
PKNLHELQKD
A20
SKRLHEVQSE
So what if we accept the coalescent model?
A1-6
TSRI SEI RR
A7
PSRI SEI RR
A8-9
PKRI SEVRR
A10-11
PQRI SAI QR
A12-13
PQRI STI QR
A14
ASHLHNLQR
A15-17
TKHLQELQR
A18
SKHLHELQR
A19
PKNLHELQK
A20
SKRLHEVQS
A’1-7
A’10-13
A1-6
A7
A10-11
A12-A13
So what if we accept the coalescent model?
A’1-7
(p-t) SRI S E I RR
A8-9
P KRI S E VRR
A’10-13
P QRI S(a-t)I QR
A14
A SHLH N LQR
A15-17
T KHLQ E LQR
A18
S KHLH E LQR
A19
P KNLH E LQK
A20
S
KRLH E VQS
4
3324
5 323
Phylogenetic trees
Rooted tree satisfying “molecular clock” hypothesis:
all leaves at same distance from the root.
Rooted tree
root
6
8
root
7
time
7
6
3
1
8
5
2
4
1
2
3
4
5
Unrooted tree:
3
4
8
2
6
1
7
5
Note: 1-5 are called leaves, or
leave nodes. 6-8 are inferred
nodes corresponding to ancestral
species or molecules. Branches
are also called edges. The edge
lengths reflect evolutionary
distances.
Phylogenetic trees
A tree is a graph reflecting the approximate distances between a set of objects.
A tree is also called a dendrogram.
There are different types of trees:
Unrooted versus rooted trees: A rooted tree has an additional node representing the
origin, in molecular phylogeny the last common ancestor of the sequences analyzed. In
general, the root cannot be directly inferred from the data. It may be inferred from the
paleontological record, from a trusted outlier, or on the basis of the molecular clock
hypothesis.
Scaled and unscaled trees: In an unscaled tree, the length of the branches are not
important. Only the topology counts. In phylogeny, trees are usually scaled.
Binary trees: each node branches into two daughter nodes. Other trees are usually not
considered in phylogeny as they can easily be approximated by binary trees with very
short edges between nodes.
Note: A rooted (or unrooted) tree connecting n objects (leaves) has
2n–1 (or 2n–2) nodes altogether and
2n–2 (or 2n–3) edges
Phylogenetic tree reconstruction,
overview
Computational challenge: There is an enormous number of different
topologies even for a relatively small number of sequences:
3 sequences: 1
4 sequences: 3
5 sequences: 15
10 sequences: 2,027,025
20 sequences: 221,643,095,476,699,771,875
Consequence: Most tree construction algorithm are heuristic methods not
guaranteed to find the optimal topology.
Input data for two major classes of algorithms:
1. Input data distance matrix, examples UPGMA, neighbor-joining
2. Input data multiple alignment: parsimony, maximum likelihood
Distance matrix methods use distances computed from pairwise or multiple
alignments as input.
Building phylogenetic trees of
proteins
Genome 1
Protein A
Genome 2
Protein C
Genome 3
Genome …
Protein D
Protein B
Protein A
Protein B
…
Protein C
Protein D
Protein B
Protein D
Protein A
Protein C
Distance based phylogenetic trees
A1
A2
A3
…
A2
A1
5 substitutions
ACTDEEGGGGSRGHI…
A-TEEDGGAASRGHI…
ACFDDEGGGGSRGHL…
…
A1
A3
A3
3 substitutions
A2
8 substitutions
5
A1
A3
3
A2
Maximum likelihood phylogenetic trees
Alignment
Probability of aa substitution
A
-
E
D
…
ACTDEEGGGGSRGHI…
0.01 0.2
0.09 …
A-TEEDGGAASRGHI… A 1
ACFDDEGGGGSRGHL… - 0.01 1 0.0001 0.0001 …
…
E 0.2 0.0001 1
0.5
D
…
0.09 0.0001 0.5
1
Maximum likelihood phylogenetic trees
A2
Alignment
p(1,2)
ACTDEEGGGGSRGHI…
A-TEEDGGAASRGHI…
ACFDDEGGGGSRGHL…
…
p(1,3)
A1
5 substitutions
A1
A3
3 substitutions
p(2,3)>p(1,2)>p(1,3)
A3
A2
A1 p(2,3)
A3
A3
A1
A2
A2
8 substitutions
Maximum Likelyhood:Parsimony
•
•
•
•
Goal: To explain the MSA with a minimal number of mutational events –
to find the tree with the minimal cost
Input: a multiple sequence alignment (MSA):
Major components:
• A cost function for a tree given an MSA which simultaneously
defines the branch lengths
• An algorithm which finds a tree with the minimal cost
Output:
• an un-rooted tree (topology plus branch-lengths)
• A total cost
• an ancestral sequences for each non-terminal node
Statistical evaluation of trees:
bootstrapping
5
1
2
4
6
7
8
3
Motivation: Some branching patterns in a tree may be uncertain for statistical
reasons (short sequences, small number of mutational events)
Goal of bootstrapping: To assess the statistical robustness for each edge of the tree.
Note that each edge divides the leave nodes into two subsets. For instance, edge 7–8
divides the leaves into subsets {1,2,3} and {4,5}.However, is this short edge
statistically robust ?
Method: Try to generate tree from subsets of input data as follows:
• Randomly modify input MSA by eliminating some columns and replacing
them by existing ones, This results in duplication of columns.
• Compute tree for each modified input MSA.
• For each edge of the tree derived from the real MSA, determine the fraction
of trees derived from modified MSAs which contain an edge that divides the
leaves into the same subsets. This fraction is called the bootstrap value.
Edges with low bootstrap values (e.g. <0.9) are considered unreliable.
Statistical evaluation of trees:
bootstrapping
Other Trees
• Use genomes
• Use Enzymomes
• Use whatever group of molecules are
important for a given function
To Do
• Take the HKs and RRs you have
annotated.
• Go to
• http://bioinf.cs.ucl.ac.uk/dompred/DomPre
dform.html
• Identify the different domains in each
individual protein.
To Do
• Take the HKs and RRs you have
annotated.
• Create phylogenies for the complete HKs
and RR sequences
• Create phylogenies for the individual
homologous domains of the different
proteins.
• Use either clustal or STRAP for protein
alignment and tree building.
Distance measures for phylogenetic
tree construction
Distance measures respect the following constraints: d = 0 if the sequences are
identical, d > 0 if the sequences are different
Distances between molecular sequences are computed from pair-wise alignment scores.
For closely related DNA sequences, one could simply use f , the fraction of nonidentical residues (readily computed from the % identity value returned by an
alignment program).
For more distantly related sequences, the Jukes-Cantor distance, d = –¾log(1–4f/3) is
preferred. This measure is supposed to be proportional to evolutionary time. It takes
into account that the percent identity value saturates at 25% over time.
For protein sequences aligned with the aid of a substitution matrix, an approximate
distance is often computed as follows:
D   log
S obs  S rand
S max  S rand
Sobs
Smax
Srand
observed pairwise alignment score
maximum score (average of alignment scores
of each sequence against itself)
expected score for random sequences of
same length and composition