1. Aligning sequences and searching databases
Lesson 3
Aligning sequences and searching databases

2. Homology
Similarity between objects due to a common ancestry

3. Sequence homology
Similarity between sequences that results from a common ancestor
VLSPAVKWAKVGAHAAGHG
VLSEAVLWAKVEADVAGHG
Basic assumption:
Sequence homology → similar structure/function

4. Sequence alignment
Alignment: Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences.

5. Homology Ortholog – homolog with similar function (via speciation)
Paralog – homolog which arose from gene duplication
Orthologs –
2 homologs from different species
Paralogs – 2 homologs within the same species G G
G1,G2

6. How close? Rule of thumb:
Proteins are homologous if over 25% identical (length >100)
DNA sequences are homologous if over 70% identical

7. Twilight zone
< 20% identity in proteins – may be homologous and may not be….
(Note that 5% identity will be obtained completely by chance!)

8. Why sequence alignment?
Predict characteristics of a protein –
use the structure/function of known proteins for predicting the structure/function of an unknown proteins

9. Sequence modifications
Sequences change in the course of evolution due to random mutations
Three types of mutations:
Insertion - an insertion of a nucleotide or several nucleotides to the sequence. AAGA AAGTA
Deletion – a deletion of a nucleotide (or more) from the sequence.
AAGA AGA
Substitution – a replacement of a nucleotide by another.
AAGA AACA
Insertion or Deletion ? -> Indel

10. Local vs. Global
Global alignment: forces alignment in regions which differ
Global alignment – finds the best alignment across the entire two sequences.
Local alignment – finds regions of similarity in parts of the sequences.
ADLGAVFALCDRYFQ
|||| |||| |
ADLGRTQN-CDRYYQ
Local alignment will return only regions of good alignment
ADLG CDRYFQ
|||| |||| |
ADLG CDRYYQ

11. When global and when local?

12. Global alignment
PTK2 protein tyrosine kinase 2 of human and rhesus monkey

13. Protein tyrosine kinase domain

14. Protein tyrosine kinase domain
Human PTK2 and leukocyte tyrosine kinase
Both function as tyrosine kinases, in completely different contexts
Ancient duplication

15. Global alignment of PTK and LTKX

16. Local alignment of PTK and LTK

17. Pairwise alignment AAGCTGAATTCGAA AGGCTCATTTCTGA
One possible alignment:
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-

18. AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
This alignment includes:
2 mismatches
4 indels (gap)
10 perfect matches

19. Choosing an alignment:
Many different alignments are possible:
AAGCTGAATTCGAA
AGGCTCATTTCTGA
A-AGCTGAATTC--GAA
AG-GCTCA-TTTCTGA-
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
Which alignment is better?

20. Alignment scoring - scoring of sequence similarity:
Assumes independence between positions
Each position is considered separately
Scores each position
Positive if identical (match)
Negative if different (mismatch) or gap (indel)
Total score = sum of position scores
Can be positive or negative

21. Example - naïve scoring system:
Perfect match: +1
Mismatch: -2
Indel (gap): -1
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
A-AGCTGAATTC--GAA
AG-GCTCA-TTTCTGA-
Score: = (+1)x10 + (-2)x2 + (-1)x4 = 2
Score: = (+1)x9 + (-2)x2 + (-1)x6 = -1
Higher score  Better alignment

22. Scoring system: The choice of +1,-2, and -1 scores is quite arbitrary
Different scoring systems  different alignments
Scoring systems implicitly represent a particular theory of evolution
Some mismatches are more plausible
Transition vs. Transversion
LysArg ≠ LysCys
Gap extension ≠ Gap opening

23. Scoring matrix
Representing the scoring system as a table or matrix n  n (n is the number of letters the alphabet contains. n=4 for nucleotides, n=20 for amino acids)
symmetric T C G A 2 -6

24. DNA scoring matrices Uniform substitutions between all nucleotides: T
From To 2 -6
Match
Mismatch

25. DNA scoring matrices
Can take into account biological phenomena such as:
Transition-transversion

26. Amino-acid scoring matrices
Take into account physico-chemical properties

27. Amino-acid substitutions matrices
Actual substitutions:
Based on empirical data
Commonly used by many bioinformatics programs
PAM & BLOSUM

28. Protein matrices – actual substitutions
The idea: Given an alignment of a large number 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 the fourth column
E and D are found in 7 / 8

29. PAM Matrix - Point Accepted Mutations
Based on a database of 1,572 changes in 71 groups of closely related proteins (85% identity)
Alignment was easy
Counted the number of the substitutions per amino-acid pair (20 x 20)
Found that common substitutions occurred between chemically similar amino acids

30. PAM Matrices Family of matrices PAM 80, PAM 120, PAM 250
The number on the PAM matrix represents evolutionary distance
Larger numbers are for larger distances

31. Example: PAM 250
Similar amino acids have greater score

32. PAM - limitations Based only on a single, and limited dataset
Examines proteins with few differences (85% identity)
Based mainly on small globular proteins so the matrix is biased

33. BLOSUM
Henikoff and Henikoff (1992) derived a set of matrices based on a much larger dataset
BLOSUM observes significantly more replacements than PAM, even for infrequent pairs

34. BLOSUM: Blocks Substitution Matrix
Based on BLOCKS database
~2000 blocks from 500 families of related proteins
Families of proteins with identical function
Blocks are short conserved patterns of aa without gaps
AABCDA----BBCDA
DABCDA----BBCBB
BBBCDA-AA-BCCAA
AAACDA-A--CBCDB
CCBADA---DBBDCC
AAACAA----BBCCC

35. BLOSUM
Each block represents a sequence alignment with different identity percentage
For each block the amino-acid substitution rates were calculated to create the BLOSUM matrix

36. BLOSUM Matrices
BLOSUMn is based on sequences that share at least n percent identity
BLOSUM62 represents closer sequences than BLOSUM45

37. Example : Blosum62 derived from block where the sequences
share at least 62% identity

38. More distant sequences
PAM vs. BLOSUM
PAM100 = BLOSUM90
PAM120 = BLOSUM80
PAM160 = BLOSUM60
PAM200 = BLOSUM52
PAM250 = BLOSUM45
More distant sequences

39. Scoring system =
substitution matrix +
gap penalty

40. Gap penalty We penalize gaps Scoring for gap opening & gap extension:
Gap-extension penalty < gap-open penalty

41. Optimal alignment algorithms
Needleman-Wunsch (global)
Smith-Waterman (local)

42. Alignment Search Space
The “search space” (number of possible gapped alignments) for optimally aligning two sequences is exponential in the length of the sequences (n).
If n=100, there are
= = 100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000
different alignments!
Average protein length is about n=250!

43. Searching databases

44. Searching a sequence database
Using a sequence as a query to find homologous sequences in a sequence database

45. Query sequence: DNA or protein?
For coding sequences, we can use the DNA sequence or the protein sequence to search for similar sequences.
Which is preferable?

46. Protein is better!
Selection (and hence conservation) works (mostly) on the protein level: CTTTCA = Leu-Ser TTGAGT = Leu-Ser

47. Query type Nucleotides: a four letter alphabet
Amino acids: a twenty letter alphabet
Two random DNA sequences will, on average, have 25% identity
Two random protein sequences will, on average, have 5% identity

48. Conclusions
Using the amino-acid sequence is preferable for homology search
Why use a nucleotide sequence after all?
No ORF found, e.g. newly sequenced genome
No similar protein sequences were found
Specific DNA databases are available (EST)

49. Some terminology
Query sequence - the sequence with which we are searching
Hit – a sequence found in the database, suspected as homologous

50. How do we search a database?
Assume we perform pairwise alignment of the query against all the sequences in the database
Exact pairwise alignment is O(mn) ≈ O(n2) (m – length of sequence 1, n – length of sequence 2)

51. How much time will it take?
O(n2) computations per search.
Assume n=200, so we have 40,000 computations per search
Size of database - ~60 million entries
2.4 x 1012 computations for each sequence search we perform!
Assume each computation takes 10-6 seconds  24,000 seconds ≈ 6.66 hours for each sequence search
150,000 searches (at least!!) are performed per day

52. Conclusion
Using the exact comparison pairwise alignment algorithm between query and all DB entries – too slow

53. Heuristic
Definition: a heuristic is a design to solve a problem that does not provide an exact solution (but is not too bad) but reduces the time complexity of the exact solution

54. BLAST BLAST - Basic Local Alignment and Search Tool
A heuristic for searching a database for similar sequences

55. DNA or Protein All types of searches are possible Query: DNA Protein
Database: DNA Protein
blastn – nuc vs. nuc blastp – prot vs. prot blastx – translated query vs. protein database tblastn – protein vs. translated nuc. DB tblastx – translated query vs. translated database
Translated databases:
trEMBL genPept

56. BLAST - underlying hypothesis
The underlying hypothesis: when two sequences are similar there are short ungapped regions of high similarity between them
The heuristic:
Discard irrelevant sequences
Perform exact local alignment with remaining sequences

57. How do we discard irrelevant sequences quickly?
Divide the database into words of length w (default: w = 3 for protein and w = 7 for DNA)
Save the words in a look-up table that can be searched quickly
WTD
TDF
DFG
FGY
GYP …
WTDFGYPAILKGGTAC

58. BLAST: discarding sequences
When the user gives a query sequence, divide it also into words
Search the database for consecutive neighbor words

59. Neighbour words
neighbor words are defined according to a scoring matrix (e.g., BLOSUM62 for proteins) with a certain cutoff level
GFC (20)
GFB
GPC (11)
WAC (5)

60. Search for consecutive words
Neighbor word
Look for a seed: hits on the same diagonal which can be connected A
At least 2 hits on the same diagonal with distance which is smaller than a predetermined cutoff
Database record
This is the filtering stage – many unrelated hits are filtered, saving lots of time!
Query

61. Perform local alignment
Database record
Query

62. Try to extend the alignment
Stop extending when the score of the alignment drops X beneath the maximal score obtained so far
Discard segments with score < S
ASKIOPLLWLAASFLHNEQAPALSDAN
JWQEOPLWPLAASOIHLFACNSIFYAS
Score=15
Score=17
Score=14
X=4

63. The result – local alignment
The result of BLAST will be a series of local alignments between the query and the different hits found

64. Theoretically, we could trust any result with an E-value ≤ 1
The number of times we will theoretically find an alignment with a score ≥ Y of a random sequence vs. a random database
Theoretically, we could trust any result with an E-value ≤ 1
In practice – BLAST uses estimations. E-values of 10-4 and lower indicate a significant homology. E-values between 10-4 and 10-2 should be checked (similar domains, maybe non-homologous). E-values between 10-2 and 1 do not indicate a good homology

65. Filtering low complexity
Low complexity regions : e.g., Proline rich areas (in proteins), Alu repeats (in DNA)
Regions of low complexity generate high score of alignment, BUT – this does not indicate homology

66. Solution
In BLAST there is an option to mask low-complexity regions in the query sequence (such regions are represented as XXXXX in query)