1. Sequence similarity

2. Sequence Comparison Much of bioinformatics involves sequences
DNA sequences
RNA sequences
Protein sequences
We can think of these sequences as strings of letters
DNA & RNA: alphabet of 4 letters
Protein: alphabet of 20 letters

3. Sequence Comparison - Motivation
Nucleotide
Learn about evolutionary relationships
Finding genes, domains, signals …
Protein
Classify protein families (function, structure)
Identify common domains (function, structure)

4. Calculation of an alignment score

5. How do we align two sequences?
ATTGCAGTGATCG
ATTGCGTCGATCG
Solution Solution 2
ATTGCAGTGATCG ATTGCAGT-GATCG
||||| ||||| ||||| || |||||
ATTGCGTCGATCG ATTGC-GTCGATCG
10 matches | , 3 mismatches
12 matches |, 2 gaps -

6. Which alignment is better?
Solution Solution 2
ATTGCAGTGATCG ATTGCAGT-GATCG
||||| ||||| ||||| || |||||
ATTGCGTCGATCG ATTGC-GTCGATCG
10X1+3X(-1) = 7
12X1+2X(-2) = 8
10 matches, 3 mismatches
12 matches, 2 gaps
We will use a scoring scheme
Match
Mismatch –1 0
Indel(gap)
10X1+3X(0) = 10
12X1+2X(-2) = 8

7. Scoring Alignments - intuition
Similar sequences evolved from a common ancestor
Evolution changed the sequences from this ancestral sequence by mutations:
Replacements: one letter replaced by another
Deletion: deletion of a letter
Insertion: insertion of a letter
Scoring of sequence similarity should examine how many operations took place

8. Causes for sequence (dis)similarity
mutation: a nucleotide at a certain location is replaced by
another nucleotide (e.g.: ATA → AGA)
insertion: at a certain location one new nucleotide is inserted inbetween two existing nucleotides (e.g.: AA → AGA)
deletion: at a certain location one existing nucleotide is deleted (e.g.: ACTG → AC-G)
indel: an insertion or a deletion

9. Gaps • Positions at which a letter is paired with a null
are called gaps.
• Gap scores are typically negative.
• Since a single mutational event may cause the insertion
or deletion of more than one residue, the presence of
a gap is ascribed more significance than the length
of the gap.

10. Gap Opening
The gap-opening penalty defines the cost for opening a gap in one of the sequences.
If you raise the gap-opening penalty above default, local alignments that contain gaps may be split into several shorter alignments.

11. Affine Gap Penalties ATA__GC ATATTGC ATAG_GC AT_GTGC
In nature, a series of indels often come as a single event rather than a series of single nucleotide events:
ATA__GC
ATATTGC
ATAG_GC
AT_GTGC
This is more likely.
This is less likely.
Normal scoring would give the same score for both alignments
Gap = Gapopen + Len * Gapextend

12. Gap penalties lead to:
Increasing penalties for gaps opening and extension
The alignment will contain fewer gaps and more mismatches
Decreasing penalties for gaps opening and extension
The alignment will contain more gaps (of varied lengths) and fewer mismatches
Holding same score of penalty for gap opening and increasing penalty for gap extension
Very long gaps will not be tolerated – they will be replaced with additional gaps of medium length and with mismatches.

13. Sequence similarity

14. Global alignment
A global alignment between two sequences is an alignment in which all the characters in both sequences participate in the alignment.
As these sequences are also easily identified by local alignment methods global alignment is now somewhat deprecated as a technique.
Global Local _____ _______ __ ____ __ ____ ____ __ ____

15. Local alignment
Local alignment methods find related regions within sequences - they can consist of a subset of the characters within each sequence.
For example, positions of sequence A might be aligned with positions
50-70 of sequence B.
This is a more flexible technique than global alignment and has the advantage that related regions which appear in a different order in the two proteins can be identified as being related.
Global Local _____ _______ __ ____ __ ____ ____ __ ____

16. Global vs. Local:
Global
Local
Jack Leunissen

17. Global vs. Local: Use global alignment if Use local alignment if
You expect, based on some biological information, that your sequences will match over the entire length.
Your sequences are of similar length.
Use local alignment if
You expect that only certain parts of two sequences will match (as in the case of conserved segment that can be found in many different proteins).
Your sequences are very different in length.
You want to search a sequence database (we will talk about it in details later).

18. Emboss [best solution] vs. Lalign (Embnet) [several solutions]
If two proteins share more than one common region, for example one has a single copy of a particular domain while the other has two copies, it may be possible to "miss" one of the two copies if using local alignment, which presents only the best scoring alignment.
Emboss [best solution] vs. Lalign (Embnet) [several solutions]

19. Comparing nucleotides
Every match got the same score
Every mismatch got the same score
Gaps- we decided but default usually good.
However

20. In the case of aa Not all matches are the same
Different mismatches get different scores

21. Serine (S) and Threonine (T) have similar physicochemical properties
Amino acid properties
Serine (S) and Threonine (T) have similar physicochemical properties
Aspartic acid (D) and Glutamic acid (E) have similar properties
=>
Substitution of S/T or E/D occurs relatively often during evolution
=>
Substitution of S/T or E/D should result in scores that are only moderately lower than identities

22. So how can we score matches and mismatches?
Each aa is characterized by a combination of features (size, charge, etc.).
The relative importance of each feature may vary according to the aa role in the 3-D structure and function of the protein.
So how can we score matches and mismatches?

23. Amino Acids Substitution Matrices
The PAM and BLOSUM substitution matrices describe the likelihood that two residue types would mutate to each other.
These matrices are based on biological sequence information: the substitutions observed in structural (BLOSUM) or evolutionary (PAM) alignments of well studied protein families
These scoring systems have a probabilistic foundation.

24. PAM series - Percent Accepted Mutation (Accepted by natural selection)
All the PAM data come from alignments of closely
related proteins (>85% amino acid identity) from 71 protein families (total of protein sequences).
PAM matrices are based on global sequence alignments - these include both highly conserved and highly mutable regions.
Some of the protein families are:
Ig kappa chain
Kappa casein
Lactalbumin
Hemoglobin a
Myoglobin
Insulin
Histone H4
Ubiquitin

25. Various degrees of conservation
The PAM1 is the matrix calculated from comparisons
of sequences with no more than 1% divergence. At an evolutionary interval of PAM1, one change has occurred over a length of 100 amino acids.
Other PAM matrices are extrapolated from PAM1. For PAM250, 250 changes have occurred for two proteins over a length of 100 amino acids.
All the PAM data come from closely related proteins
(>85% amino acid identity).

26. PAM series - Percent Accepted Mutation (Accepted by natural selection)
Varying degrees of conservation *

27. Blocks Substitution Matrices-
THE BLOSUM Family of Matrices
Blocks Substitution Matrices-
Henikoff and Henikoff, 1992
Blocks are short conserved patterns of 3-60 aa long.
Proteins can be divided into families by common blocks.
Different BLOSUM matrices emerge by looking
at sequences with different identity percentage.
Example: BLOSUM62 is derived from an alignment
of sequences that share no less than 62% identity.
Block A B C D

28. The Blocks Database
Gapless alignment blocks

29. Blosum62 scoring matrix

30. 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

31. PAM versus BLOSUM Based on an explicit evolutionary model
Derived from small, closely related proteins with ~15% divergence
Higher PAM numbers to detect more remote sequence similarities
Errors in PAM 1 are scaled 250X in PAM 250
Based on empirical frequencies
Uses much larger, more diverse set of protein sequences (30-90% ID)
Lower BLOSUM numbers to detect more remote sequence similarities
Errors in BLOSUM arise from errors in alignment

32. Guidelines
Lower PAMs and higher Blosums find short local alignment of highly similar sequences
Higher PAMs and lower Blosums find longer weaker local alignment
No single matrix answers all questions

33. Guidelines BLOSUM is generally better than PAM for local alignments.
The default matrix is often identity matrix for DNA and BLOSUM 62 for proteins
When using BLOSUM80 instead of BLOSUM45, local alignments tend to be shorter.
Low PAMs have same effects as high Blosums. BLOSUM indicates percent identity while PAM is proportional to the percent of accepted mutations.