Sequence Alignment and Phylogeny

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Sequence Alignment and Phylogeny
B I O I N F O R M A T I C S
B I O L O G Y - M A T H - S
Dr Peter Smooker, peter.smooker_at_rmit.edu.au
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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

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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

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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

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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

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When Is Homology Real?

• As a general rule, in a pairwise alignment
• gt25 identical aas, proteins will have similar
  folding pattern- most likely homologous
• 18-25 identical- twilight zone- tantalizing
• lt18 identical- cannot determine from alignment

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Measuring Sequence Similarity

• Two measures of the distance between two strings
• Hamming distance strings equal length, number of
  positions with mismatches
• Levenshtein distance not equal length, number of
  edit operations to change one string to the other

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• agtc Hamming distance 2
• cgta
• ag-tcc Levenshtein distance 3
• cgctca

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Protein Alignments-Substitution 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

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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 gt 85 identity. The PAM 1
  matrix was computed. Extrapolated for larger
  evolutionary distances

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PAM Matrices

• PAM 0 30 80 110 200 250
• identity 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.

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Definition of PAM from BLAST literature

• http//www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschu
  l-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.

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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

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BLOCKS

• The substitutions in each aligned column are
  identified and a score for each substitution
  calculated and inserted into the matrix.

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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

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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

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Scoring

• Score of mutation i gt j
• log observed i gtj
• expected i gt j
• Expected i gt j is simply calculated by the
  frequencies of the amino acids
• Result is multiplied by 10. Scores are added.

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PAM250
A R N D C Q E G H I L K M F
P S T W Y V
A 2
R -2 6
N 0 0 2
D 0 -1 2 4
C -2 -4 -4 -5 4
Q 0 1 1 2 -5 4
E 0 -1 1 3 -5 2 4
G 1 -3 0 1 -3 -1 0 5
H -1 2 2 1 -3 3 1 -2 6
I -1 -2 -2 -2 -2 -2 -2 -3 -2 5
L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6
K -1 3 1 0 -5 1 0 -2 0 -2 -3 5
M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6
F -4 -4 -4 -6 -4 -5 -5 -5 -2 1 2 -5 0
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P 1 0 -1 -1 -3 0 -1 -1 0 -2 -3 -1 -2
-5 6
S 1 0 1 0 0 -1 0 1 -1 -1 -3 0 -2
-3 1 3
T 1 -1 0 0 -2 -1 0 0 -1 0 -2 0 -1
-2 0 1 3
W -6 2 -4 -7 -8 -5 -7 -7 -3 -5 -2 -3 -4
0 -6 -2 -5 17
Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2
7 -5 -3 -3 0 10 V 0 -2 -2 -2 -2
-2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 -2 4
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• Scores below 0 indicate amino acids that are
  rarely substituted, and different aas that give
  a high ve score are usually functionally
  equivalent
• Scores below 0 indicates that those substitutions
  are rarely observed

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• Hydrophilic

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• These aas are hydrophobic (except glycine, often
  put in a class by itself).

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Interpreting scores- BLAST output
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Significance

• Two values are given- the Bit score and the
  E-value.
• 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

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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'
  (lambdaS - ln K)/(ln 2), where lambda and K are
  parameters dependent upon the scoring system
  (substitution matrix and gap costs) employed

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Multiple Sequence Alignment

• To quote Lesk
• One amino acid sequence plays coy a pair of
  homologous sequences whisper many aligned
  sequences shout out loud

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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.

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MSAs 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 n4, impossible for higher values.
• Use a heuristic approach. Most common is the
  CLUSTAL algorithm

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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

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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

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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

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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

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Fasciola- Liver Fluke
NEJ Adult
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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

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Cysteine proteases

• Digest proteins cleave between adjacent amino
  acids.
• Not random cleavage, different proteases show a
  preference for different targets.

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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

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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.

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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.

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Homology Modelling

• Three substitutions is residues lining the S2
  subsite were observed (L5-gt 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

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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.
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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

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What about the other enzymes- CLUSTAL W
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What amino acid is at 69?
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FgCatL1-a 61 GNMGCSGGLMENAYEYLKQFGLETESSYPYTAVE
GQCRYNRQLGVAKVTDYYTVHSGSEV 120 FgCatL1-b
GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTD
YYTVHSGSEV FgCatL1-c GNFGCNGGLMENACEYLKRFGLE
TESSYPYRAVEGPCRYNKQLGVAKVTGYYMVHSGDEV FgCatL1-d
GNHGCGGGYMENAYEYLKHSGLETDSYYPYQAVEGPCQYDGRLAYA
KVTDYYTVHSGDEV FgCatL1-e GNYGCMGGLMENAYEYLKQ
FGLETESSYPYTAVEDQCRYNRQLGVAKVTDYYTVHSGSEV FgCatL1-
f GNNGCRGGLMEIAYEYLRRFGLEIESTYPYRAVEGPCRYDRR
LGVAKVTGYYIVHSGDEV FgCatL2
GNMGCSGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTD
YYTVHSGSEV FgCatL3 GNINCMGGLMENAYEYLKQFGLE
TESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV FhCatL1
GNNGCGGGLMENAYQYLKQFGLETESSYPYTAVGGQCRYNKQLGVA
KVTGYYTVQSGSEV FhCatL2 GNYGCGGGYMENAYEYLKH
NGLETESYYPYQAVEGPCQYDGRLAYAKVTGYYTVHSGDEI FhCatL3
GNNGCSGGLMENAYQYLKQFGLETESSYPYTAVEGQCRYNKQ
LGVAKVTGYYTVHSGSEV FhCatL4
GNYGCNGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTG
YYTVHSGDEV FhCatL5 GNYGCNGGLMENAYEYLKRFGLE
TESSYPYRAVEGQCRYNEQLGVAKVTGYYTVHSGDEV FhCatL6
GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVA
KVTDYYTVHSGSEV FhCatL7 GNYGCGGGYMENAYEYLKH
NGLETESYYPYQAVEGPCQYDGRLAYAKVTGYYTVHSGDEI FhCatL8
GNHGCGGGWMENAYKYLKNSGLETASYYPYQAVEYQCQYRKE
LGVAKVTGAYTVHSGDEM FhCatL9
GNNGCSGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTG
YYTVHSGSEV FhCatL10 GNHGCGGGWMENAYKYLKNSGLE
TASDYPYQGWEYQCQYRKELGVAKVTGAYTVHSGDEM
. . . ..
. .  
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Fasciola CatLs 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)

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Relationship of Fasciola enzymes

• Tree constructed using 18 full-length sequences
• Resolved into 4 distinct clades

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AA69 Predicted Substrate
L69 Phe-Arg
L69 Phe-Arg
Y69 Pro-Arg
W69 ??-Arg
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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 neighbour-joining
  tree using Glutathione-S transferase sequences
  19 /- 5.2 MYA

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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

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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?

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• 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?

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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?