1. Introduction to bioinformatics
Sequence Alignment
Part 3

2. WHATS TODAY? MORE BLAST …. Similarity scores for protein sequences
Gaps
Statistical significance (e-value)

3. Protein Sequence Alignment
Rule of thumb: Proteins are homologous if 25% identical (length >100) DNA sequences are homologous if 70% identical

4. Protein Pairwise Sequence Alignment
The alignment tools are similar to the DNA alignment tools
BLASTN for nucleotides
BLASTP for proteins
Main difference: instead of scoring match (+2) and mismatch (-1) we have similarity scores:
Score s(i,j) > 0 if amino acids i and j have similar properties
Score s(i,j) is  0 otherwise
How should we score s(i,j)?

5. The 20 Amino Acids

6. Chemical Similarities Between Amino Acids
Acids & Amides DENQ (Asp, Glu, Asn, Gln)
Basic HKR (His, Lys, Arg)
Aromatic FYW (Phe, Tyr, Trp)
Hydrophilic ACGPST (Ala, Cys, Gly, Pro, Ser, Thr)
Hydrophobic ILMV (Ile, Leu, Met, Val)

7. Sequence Alignment based on AA similarity
TQSPSSLSASVGDTVTITCRASQSISTYLNWYQQKP----GKAPKLLIYAASSSQSGVPS
|| |||| +|| ||| | +| | | | |
TQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLTKGPSKLNDRADS
RFSGSGSGTDFTLTINSLQPEDFATYYCQ QSYSTPHFSQGTKLEI
| | | +| | | +|+ || || | | | || | +
RRSLWDQG-NFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLTL
---KRTVAAPSVFIFPPSDEQLKSGTASVVCLLN NFYPREAKVQWKVD
++||| | | | | ||++|+|
TLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKID
| = identity
+ = similarity

8. Amino Acid Substitutions Matrices
When scoring protein sequence alignments it is common to use a matrix of 20  20, representing all pairwise comparisons :
Substitution Matrix

9. Given an alignment of closely related sequences
we can score the relation between amino acids
based on how frequently they substitute each other
M G Y D E
M G Y E E
M G Y Q E
In this column
E & D are found
7/8

10. Amino Acid Matrices
Symmetric matrix of 20x20 entries: entry (i,j)=entry(j,i)
Entry (i,i) is greater than any entry (i,j), ji.
Entry (i,j): the score of aligning amino acid i against amino acid j.

11. PAM - Point Accepted Mutations
Developed by Margaret Dayhoff, 1978.
Analyzed very similar protein sequences
Proteins are evolutionary close.
Alignment is easy.
Point mutations - mainly substitutions
Accepted mutations - by natural selection.
Used global alignment.
Counted the number of substitutions (i,j) per amino acid pair: Many i<->j substitutions => high score s(i,j)
Found that common substitutions occurred involving chemically similar amino acids.

12. PAM 250 Similar amino acids are close to each other.
Regions define conserved substitutions.

13. Example: Asp & Glu Score = 3 C H +H3N COO- HCH O- O COO- +H3N C H HCH
Aspartate
(Asp, D)
Glutamate
(Glu, E)

14. Selecting a PAM Matrix
Low PAM numbers: short sequences, strong local similarities.
High PAM numbers: long sequences, weak similarities.
PAM120 recommended for general use (40% identity)
PAM60 for close relations (60% identity)
PAM250 for distant relations (20% identity)
If uncertain, try several different matrices
PAM40, PAM120, PAM250 recommended

15. BLOSUM Blocks Substitution Matrix
Steven and Jorga G. Henikoff (1992)
Based on BLOCKS database (
Families of proteins with identical function
Highly conserved protein domains
Ungapped local alignment to identify motifs
Each motif is a block of local alignment
Counts amino acids observed in same column
Symmetrical model of substitution
AABCDA… BBCDA
DABCDA. A.BBCBB
BBBCDABA.BCCAA
AAACDAC.DCBCDB
CCBADAB.DBBDCC
AAACAA… BBCCC

16. BLOSUM Matrices
Different BLOSUMn matrices are calculated independently from BLOCKS
BLOSUMn is based on sequences that are at most n percent identical.

17. Selecting a BLOSUM Matrix
For BLOSUMn, higher n suitable for sequences which are more similar
BLOSUM62 recommended for general use
BLOSUM80 for close relations
BLOSUM45 for distant relations

18. Summary:
BLOSUM matrices are based on the replacement patterns found in more highly conserved regions of the sequences without gaps
PAM matrices based on mutations observed throughout a global alignment, includes both highly conserved and highly mutable regions

19. Gap Scores Example showed -1 score per indel
So gap cost is proportional to its length
Biologically, indels occur in groups
We want our gap score to reflect this
Standard solution: affine gap model
Once-off cost for opening a gap
Lower cost for extending the gap
Changes required to algorithm

20. Scoring system =
Substitution Matrix +
Gap Penalty

21. Gap penalty We expect to penalize gaps
Scoring for gap opening & for extension
Insertions and deletions are rare in evolution
But once they are created, they are easy to extend
Gap-extension penalty < gap-open penalty
Default gap parameters are given for each matrix:
PAM30: open=9, extension=1
PAM250: open=14, extension=2

22. Low Complexity Sequences
AAAAAAAAAAA
ATATATATATATA
CAGCAGCAGCAG
Sequences of low complexity can cause getting significant hits
which are not true homologues !!!
How does BLAST deal with low complexity sequences?
By default low complexity sequences are filtered out
and replaced by XXXXX

23. Statistical significance

24. E-value The lower bound is normally 0 (we want to find the best)
The number of hits (with the same similarity score) one can "expect" to see just by chance when searching the given string in a database of a particular size.
higher e-value lower similarity
“sequences with E-value of less than 0.01 are almost always found to be homologous”
The lower bound is normally 0 (we want to find the best)

25. Expectation Values Increases linearly with length of query sequence
Decreases exponentially with score of alignment
Increases linearly with length of database

26. E value: Number of hits of score ≥ S expected by chance
Bit score (S)
Similar to alignment score
Normalized
Higher means more significant
E value: Number of hits of score ≥ S expected by chance
Based on random database of similar size
Lower means more significant
Used to assess the statistical significance of the alignment

27. Remote homologues PSI-BLAST Sometimes BLAST isn’t enough.
Large protein family, and BLAST only gives close members. We want more distant members
PSI-BLAST

28. Construct profile from blast results
PSI-BLAST
Position Specific Iterated BLAST
Regular blast
Construct profile from blast results
Blast profile search
Final results

29. PSI-BLAST
Advantage: PSI-BLAST looks for seqs that are close to ours, and learns from them to extend the circle of friends
Disadvantage: if we found a WRONG sequence, we will get to unrelated sequences. This gets worse and worse each iteration