1. 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,

2. Uses of alignments
To determine the relationship (ie: distance) between two sequences (pair-wise alignment)
To search databanks for the presence of homologues
To look for sequence conservation in families of proteins
To use molecular approaches to phylogeny

3. 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

4. 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

5. “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

6. 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

7. 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

8. agtc Hamming distance = 2
cgta
ag-tcc Levenshtein distance = 3
cgctca

9. 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

10. 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

11. 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.

12. Definition of PAM from BLAST literature
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.

13. 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
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

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

15. 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

16. 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

17. 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.

18. 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

19. 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

20. Hydrophilic

21. These aa’s are hydrophobic (except glycine, often put in a class by itself).

22. Interpreting scores- BLAST output

24. 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

25. 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

26. Multiple Sequence Alignment
To quote Lesk
“One amino acid sequence plays coy; a pair of homologous sequences whisper; many aligned sequences shout out loud”

27. 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.

28. 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

29. 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

30. 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

31. 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

32. 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

33. Fasciola- Liver Fluke
NEJ Adult

34. 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

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

36. 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

37. Presumed to be due to changes affecting the S2 subsite of the enzyme.
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.

38. 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.

39. 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

40. 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.

41. 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

42. What about the other enzymes- CLUSTAL W

43. What amino acid is at #69?

44. FgCatL1-a 61 GNMGCSGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV 120
FgCatL1-b GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FgCatL1-c GNFGCNGGLMENACEYLKRFGLETESSYPYRAVEGPCRYNKQLGVAKVTGYYMVHSGDEV
FgCatL1-d GNHGCGGGYMENAYEYLKHSGLETDSYYPYQAVEGPCQYDGRLAYAKVTDYYTVHSGDEV
FgCatL1-e GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEDQCRYNRQLGVAKVTDYYTVHSGSEV
FgCatL1-f GNNGCRGGLMEIAYEYLRRFGLEIESTYPYRAVEGPCRYDRRLGVAKVTGYYIVHSGDEV
FgCatL GNMGCSGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FgCatL GNINCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FhCatL GNNGCGGGLMENAYQYLKQFGLETESSYPYTAVGGQCRYNKQLGVAKVTGYYTVQSGSEV
FhCatL GNYGCGGGYMENAYEYLKHNGLETESYYPYQAVEGPCQYDGRLAYAKVTGYYTVHSGDEI
FhCatL GNNGCSGGLMENAYQYLKQFGLETESSYPYTAVEGQCRYNKQLGVAKVTGYYTVHSGSEV
FhCatL GNYGCNGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTGYYTVHSGDEV
FhCatL GNYGCNGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTGYYTVHSGDEV
FhCatL GNYGCMGGLMENAYEYLKQFGLETESSYPYTAVEGQCRYNRQLGVAKVTDYYTVHSGSEV
FhCatL GNYGCGGGYMENAYEYLKHNGLETESYYPYQAVEGPCQYDGRLAYAKVTGYYTVHSGDEI
FhCatL GNHGCGGGWMENAYKYLKNSGLETASYYPYQAVEYQCQYRKELGVAKVTGAYTVHSGDEM
FhCatL GNNGCSGGLMENAYEYLKRFGLETESSYPYRAVEGQCRYNEQLGVAKVTGYYTVHSGSEV
FhCatL GNHGCGGGWMENAYKYLKNSGLETASDYPYQGWEYQCQYRKELGVAKVTGAYTVHSGDEM
** .* ** ** * :**:. *** * *** . *:* .*. ****. * *:**.*:

45. 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)

47. Relationship of Fasciola enzymes
Tree constructed using 18 full-length sequences
Resolved into 4 distinct clades

48. AA69 Predicted
Substrate
L69 Phe-Arg
L69 Phe-Arg
Y69 Pro-Arg
W69 ??-Arg

50. 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

51. Practice runs- 1. Blast Go to the BLAST server at NCBI
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

52. 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”?

53. For interest, use sequence 2 to run a BLAST
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?

54. Practice runs- 2. CLUSTAL W
Go to
Upload (or paste) Seq3.txt, run the tool
Does the dendogram resemble that previously demonstrated?