1. Alignment methods April 12, 2005 Return Homework (Ave. = 7.5)
Reminder: Quiz on Thurs. April 14
Learning objectives- Understand difference between identity, similarity and homology. PAM scoring matrices. Understand difference between global alignment and local alignment. Review of Dotter software program.
Workshop-Import sequences of interest from GenBank, place in FASTA format, align sequences using DOTTER program.
Homework #4 due on Tues, April 19 at the beginning of class.

3. Infer structural information Infer functional information
Purpose of finding differences and similarities of amino acids in two proteins.
Infer structural information
Infer functional information
Infer evolutionary relationships

4. Evolutionary Basis of Sequence Alignment
Similarity: Quantity that relates how much
two amino acid sequences are alike.
2. Identity: Quantity that describes how much
two sequences are alike in the strictest terms.
3. Homology: a conclusion drawn from data
suggesting that two genes share a common
evolutionary history.

5. Evolutionary Basis of Sequence Alignment (Cont. 1)
Why are there regions of identity?
1) Conserved function-residues participate in reaction.
2) Structural (For example, conserved cysteine residues that form a disulfide linkage)
3) Historical-Residues that are conserved solely due to a common ancestor gene.

6. One is mouse trypsin and the other is crayfish trypsin.
They are homologous proteins. The sequences share 41% identity.

7. Evolutionary Basis of Sequence Alignment (Cont. 2)
Note: it is possible that two proteins share a high degree of
similarity but have two different functions. For example,
human gamma-crystallin is a lens protein that has no known
enzymatic activity. It shares a high percentage of identity with
E. coli quinone oxidoreductase. These proteins likely had a
common ancestor but their functions diverged.
Analogous to railroad car and diner function.

9. Modular nature of proteins
The previous alignment was global. However, many proteins do not display global patterns of similarity. Instead, they possess local regions of similarity.
Proteins can be thought of as assemblies of modular domains. THINK OF MR. POTATOHEAD. It is thought that this may, in some cases, be due to a process known as exon shuffling.

10. Modular nature of proteins (cont. 1)
Gene A
Exon 1a
Exon 2a
Duplication of Exon 2a
Gene A
Exon 1a
Exon 2a
Exon 2a
Exchange with Gene B
Gene B
Exon 1b
Exon 2b
Exon 2b
Exon 3 (Exon 2b from Gene B)
Gene A
Exon 1a
Exon 2a
Gene B
Exon 1b
Exon 2b
Exon 3 (Exon 2a from Gene A)

11. Scoring Matrices Importance of scoring matrices
Scoring matrices appear in all analyses involving sequence comparisons.
The choice of matrix can strongly influence the outcome of the analysis.
Scoring matrices implicitly represent a particular theory of relationships.
Understanding theories underlying a given scoring matrix can aid in making proper choice.

12. Identity Matrix A 1 C 1 I 1 L 1 A C I L1 I 1 L 1 A C I L
Simplest type of scoring matrix

13. Similarity
It is easy to score if an amino acid is identical to another (the
score is 1 if identical and 0 if not). However, it is not easy to
give a score for amino acids that are somewhat similar.
+NH3
CO2-
+NH3
CO2-
Isoleucine
Leucine
Should they get a 0 (non-identical) or a 1 (identical) or
Something in between?

14. Scoring Matrices
When we consider scoring matrices, we encounter the convention that matrices have numeric indices corresponding to the rows and columns of the matrix.
For example, M11 refers to the entry at the first row and the first column. In general, Mij refers to the entry at the ith row and the jth column. To use this for sequence alignment, we simply associate a numeric value to each letter in the alphabet of the sequence.

15. Two major scoring matrices for amino acid sequence comparisons
PAM-derived from sequences known to be closely related (Eg. Proteins from chimpanzees and human). PAM1 was created from empirical data and other PAMs were mathematically derived.
BLOSUM-derived from sequences not closely related (Eg. E. coli and human) from data stored in the BLOCKS database.

16. The Point-Accepted-Mutation (PAM) model of evolution and the PAM scoring matrix
Started by Margaret Dayhoff, 1978
A series of matrices describing the extent to which two amino acids have been interchanged in evolution.
Proteins were aligned by eye and then the number of times an amino acid was substituted in different species was counted.

17. Protein families used to construct Dayhoff’s scoring matrix
Protein PAMs per 100 mil yrs
IgG kappa C region 37
Kappa casein 33
Serum Albumin 26
Cytochrome C
Histone H
Histone H

18. Numbers of accepted point mutations, multiplied by 10
A R N D C Q E G H I L K M F P S T W Y V A
R 30 N D C Q E G H I L K M F P S T W Y V
Original amino acid
Replacement amino acid

19. Calculation of relative mutability of amino acid
Find frequency of amino acid change to another amino acid at a certain position in protein.
Divide the frequency of aa change by the frequency that the “j” (original) aa occurs in all proteins studied. This is called the “mutability”.
Determine the factor to multiply the alanine mutability to get 100.
Multiply the 19 other a.a. mutabilities by the same factor. This is called the relative mutability

20. Relative mutabilities of amino acids
Asn 134
Ser 120
Asp 106
Glu 102
Ala 100
Thr 97
Ile 96
Met 94
Gln 93
Val 74
His 66
Arg 65
Lys 56
Pro 56
Gly 49
Tyr 41
Phe 41
Leu 40
Cys 20
Trp 18

21. Why are the mutabilities different?
High mutabilities because a similar amino acid can replace it. (Asp for Glu)
Conversely, the low mutabilities are unique, can’t be replaced.

22. Creation of a mutation probability matrix
Used accepted mutation data from earlier slide and the mutability of each amino acid in nature to create a mutation probability matrix. Mij shows the probability that an original amino acid j (in columns) will be replaced by amino acid i (in rows) over a defined evolutionary interval. For PAM1, 1% of aa’s have been changed.

23. PAM1 mutational probability matrix
Values of each column will sum to 10,000

24. The Point-Accepted-Mutation (PAM) model of evolution and the PAM scoring matrix
A 1-PAM unit is equivalent to 1 mutation found in a stretch of 2 sequences each containing 100 amino acids that are aligned
Example 1:
..CNGTTDQVDKIVKILNEGQIASTDVVEVVVSPPYVFLPVVKSQLRPEIQV..
|||||||||||||| |||||||||||||||||||||||||||||||||||
..CNGTTDQVDKIVKIRNEGQIASTDVVEVVVSPPYVFLPVVKSQLRPEIQV..
length = 100, 1 Mismatch, PAM distance = 1
A k-PAM unit is equivalent to k 1-PAM units (or Mk).

25. The Point-Accepted-Mutation (PAM) model of evolution and the PAM scoring matrix
Observed %
Difference
Evolutionary Distance
In PAMs 1 5 10 20 40 50 60 70 80 1 5 11 23 56 80
112
159
246

26. Final Scoring Matrix is the Log-Odds Scoring Matrix
S (a,b) = 10 log10(Mab/Pb)
Replacement amino acid
Original amino acid
Frequency of amino acid b
Mutational probability matrix number

28. Summary of PAM Scoring Matrix
PAM = a unit of evolution (1 PAM = 1 point mutation/100 amino acids)
Accepted Mutation means fixed point mutation
Comparison of 71 groups of closely related proteins yielding 1,572 changes. (>85% identity)
Different PAM matrices are derived from the PAM 1 matrix by matrix multiplication.
The matrices are converted to log odds scoring matrices. (Frequency of change divided by probability of chance alignment converted to log base 10.)
A PAM 250 matrix is roughly equivalent to 20% identity in two sequences.

29. The Dotter Program Program consists of three components:
Sliding window
A table that gives a score for each amino acid match
A graph that converts the score to a dot of certain density.
The higher the density the higher the score.

30. Two proteins that are similar in certain regions
Tissue plasminogen activator (PLAT)
Coagulation factor 12 (F12).

31. Single region on F12
is similar to two regions
on PLAT
Region of
similarity

33. FASTA format
>gi| |gb|AAA | p53 tumor suppressor homolog
MSQGTSPNSQETFNLLWDSLEQVTANEYTQIHERGVGYEYHEAEPDQTSLEISAYRIAQPDPYGRSESYD
LLNPIINQIPAPMPIADTQNNPLVNHCPYEDMPVSSTPYSPHDHVQSPQPSVPSNIKYPGEYVFEMSFAQ
PSKETKSTTWTYSEKLDKLYVRMATTCPVRFKTARPPPSGCQIRAMPIYMKPEHVQEVVKRCPNHATAKE
HNEKHPAPLHIVRCEHKLAKYHEDKYSGRQSVLIPHEMPQAGSEWVVNLYQFMCLGSCVGGPNRRPIQLV
FTLEKDNQVLGRRAVEVRICACPGRDRKADEKASLVSKPPSPKKNGFPQRSLVLTNDITKITPKKRKIDD
ECFTLKVRGRENYEILCKLRDIMELAARIPEAERLLYKQERQAPIGRLTSLPSSSSNGSQDGSRSSTAFS
TSDSSQVNSSQNNTQMVNGQVPHEEETPVTKCEPTENTIAQWLTKLGLQAYIDNFQQKGLHNMFQLDEFT
LEDLQSMRIGTGHRNKIWKSLLDYRRLLSSGTESQALQHAASNASTLSVGSQNSYCPGFYEVTRYTYKHT
ISYL

34. Workshop 3