1. ©CMBI 2005 Database Searching BLAST Database Searching Sequence Alignment Scoring Matrices Significance of an alignment BLAST, algorithm BLAST, parameters BLAST, output Alignment significance in BLAST

2. ©CMBI 2005 Database Searching Identify similarities between novel query sequences whose structures and functions are unknown and uncharacterized sequences in (public) databases whose structures and functions have been elucidated. N.B. The similarity might span the entire query sequence or just part of it!

3. ©CMBI 2005 Database searching (2) The query sequence is compared/aligned with every sequence in the database. High-scoring database sequences are assumed to be evolutionary related to the query sequence. If sequences are related by divergence from a common ancestor, there are said to be homologous.

4. J.Leunissen©CMBI 2005 Sequence Alignment The purpose of a sequence alignment is to line up all residues in the sequence that were derived from the same residue position in the ancestral gene or protein in any number of sequences gap = insertion or deletion A B B A

5. ©CMBI 2005 Scoring Matrix/Substitution Matrix To score quality of an alignment Contains scores for pairs of residues (amino acids or nucleic acids) in a sequence alignment For protein/protein comparisons: a 20 x 20 matrix of similarity scores where identical amino acids and those of similar character (e.g. Ile, Leu) give higher scores compared to those of different character (e.g. Ile, Asp). Symmetric

6. ©CMBI 2005 Substitution Matrices Not all amino acids are equal Residues mutate more easily to similar ones Residues at surface mutate more easily Aromatics mutate preferably into aromatics Mutations tend to favor some substitutions Core tends to be hydrophobic Selection tends to favor some substitutions Cysteines are dangerous at the surface Cysteines in bridges seldom mutate

7. ©CMBI 2005 PAM250 Matrix

8. ©CMBI 2005 Scoring example Score of an alignment is the sum of the scores of all pairs of residues in the alignment sequence 1: TCCPSIVARSN sequence 2: SCCPSISARNT 1 12 12 6 2 5 -1 2 6 1 0 => alignment score = 46

9. ©CMBI 2005 Dayhoff Matrix (1) The group of Dayhoff created a scoring matrix from a dataset of closely similar protein sequences that could be aligned unambiguously. Then they counted all mutations (and non-mutations) and calculated the mutation frequencies With a bit of math, they converted these frequencies into the famous Dayhoff matrix (also called PAM matrix).

10. ©CMBI 2005 Given the frequency of Leu and Val in my sequences, do I see more mutations of V  L than I would expect by chance? Score of mutation A  B = log ( observed a  b mutation rate / expected number of mutations ) This is called a log odd and can be negative, zero, or positive. When using a log odds matrix, the total score of the alignment is given by the sum of the scores for each aligned pair of residues. Dayhoff Matrix (2)

11. ©CMBI 2005 Dayhoff Matrix (3) This log odds matrix is called PAM 1. An evolutionary distance of 1 PAM (point accepted mutation) means there has been 1 point mutation per 100 residues PAM 1 may be used to generate matrices for greater evolutionary distances by multiplying it repeatedly by itself. PAM250: –2,5 mutations per residue. –equivalent to 20% matches remaining between two sequences, i.e. 80% of the amino acid positions are observed to have changed (one or more times). –is default in many analysis packages.

12. ©CMBI 2005 BLOSUM Matrix Limit of Dayhoff matrix: Matrices based on the Dayhoff model of evolutionary rates are derived from alignments of sequences that are at least 85% identical; that might not be optimal… An alternative approach has been developed by Henikoff and Henikoff using local multiple alignments of more distantly related sequences

13. ©CMBI 2005 BLOSUM Matrix (2) The BLOSUM matrices (BLOcks SUbstitution Matrix) are based on the BLOCKS database. The BLOCKS database utilizes the concept of blocks (un-gapped amino acid pattern), that act as signatures of a family of proteins. Substitution frequencies for all pairs of amino acids were then calculated and this used to calculate a log odds BLOSUM matrix. Different matrices are obtained by varying the identity threshold. For example, BLOSUM80 was derived using blocks of 80% identity.

14. Which Matrix to use? Close relationships (Low PAM, high Blosum) Distant relationships (High PAM, low Blosum) Often used defaults are: PAM250, BLOSUM62 BLOSUM 80BLOSUM 62BLOSUM 45 PAM 20PAM 120PAM 250 More conservedMore variable

15. ©CMBI 2005 Significance of alignment (1) When is an alignment statistically significant? In other words: How much different is the alignment score found from scores obtained by aligning random sequences to the query sequence? Or: What is the probability that an alignment with this score could have arisen by chance?

16. ©CMBI 2005 Significance of alignment (2) Database size= 20 x 10 6 letters peptide#hits A1 x 10 6 AP50000 IAP2500 LIAP125 WLIAP6 KWLIAP0,3 KWLIAPY0,015

17. ©CMBI 2005 BLAST Question: What database sequences are most similar to (or contain the most similar regions to) my previously uncharacterised sequence? BLAST finds the highest scoring locally optimal alignments between a query sequence and a database. Very fast algorithm Can be used to search extremely large databases Sufficiently sensitive and selective for most purposes Robust – the default parameters can usually be used

18. ©CMBI 2005 BLAST – Algorithme Step 1: Read/understand user query sequence. Step 2: Use hashing technology to select several thousand likely candidates. Step 3: Do a real alignment between the query sequence and those likely candidate. ‘Real alignment’ is a main topic of this course. Step 4: Present output to user.

19. ©CMBI 2005 BLAST Algorithm, Step 2 For a given word length w and a given score matrix: Create a list of all words (w-mers) that can score >T when compared to w-mers from the query. P D G 13 P Q A 12 P Q N 12 etc. Below Threshold (T=13) Query SequenceL N K C K T P Q G Q R L V N Q P Q G 18 P E G 15 P R G 14 P K G 14 P N G 13 Neighborhood Words Word P M G 13

20. ©CMBI 2005 BLAST Algorithm, Step 2 Each neighbourhood word gives all positions in the database where it is found (hit list). P D G 13 P Q G 18 P E G 15 P R G 14 P K G 14 P N G 13 P M G 13 PMG Database

21. ©CMBI 2005 BLAST Algorithm, Step 2 The program extends matching segments (seeds) in both directions by adding residues. Residues will be added until the incremental score drops below a threshold.

22. ©CMBI 2005 Basic BLAST Algorithms ProgramQueryDatabase BLASTPProtein BLASTNDNA BLASTXtranslatedDNAprotein TBLASTNproteintranslatedDNA TBLASTXtranslatedDNA

23. ©CMBI 2005 PSI-BLAST Position-Specific Iterated BLAST Distant relationships are often best detected by motif or profile searches rather than pair-wise comparisons PSI-BLAST first performs a BLAST search. PSI-BLAST uses the information from significant BLAST alignments returned to construct a position specific score matrix, which replaces the query sequence for the next round of database searching. PSI-BLAST may be iterated until no new significant alignments are found.

24. ©CMBI 2005 BLAST Input Steps in running BLAST: Entering your query sequence (cut-and-paste) Select the database(s) you want to search Choose output parameters Choose alignment parameters (scoring matrix, filters,….) Example query= MAFIWLLSCYALLGTTFGCGVNAIHPVLTGLSKIVNGEEAVPGTWPWQVTLQDRSGFHFC GGSLISEDWVVTAAHCGVRTSEILIAGEFDQGSDEDNIQVLRIAKVFKQPKYSILTVNND ITLLKLASPARYSQTISAVCLPSVDDDAGSLCATTGWGRTKYNANKSPDKLERAALPLLT NAECKRSWGRRLTDVMICGAASGVSSCMGDSGGPLVCQKDGAYTLVAIVSWASDTCSASS GGVYAKVTKIIPWVQKILSSN

25. ©CMBI 2005 BLAST Output (1)

26. ©CMBI 2005 BLAST Output (2) A high score, or preferably, clusters of high scores, indicates a likely relationship A low probability indicates that a match is unlikely to ave arisen by chance

27. ©CMBI 2005 BLAST Output (3) Low scores with high probabilities suggest that matches have arisen by chance

28. ©CMBI 2005 Alignment Significance in BLAST P-value (probability) Relates the score for an alignment to the likelihood that it arose by chance. The closer to zero, the greater the confidence that the hit is real. E-value (expect value) The number of alignments with E that would be expected by chance in that database (e.g. if E=10, 10 matches with scores this high are expected to be found by chance). A match will be reported if its E is below the threshold. Lower E thresholds are more stringent, and report fewer matches.

29. ©CMBI 2005 BLAST Output (4)

30. ©CMBI 2005 BLAST Output (5)

31. ©CMBI 2005 BLAST Output (6)

32. ©CMBI 2005 Low complexity filter

33. ©CMBI 2005 Low complexity filter

34. ©CMBI 2005 Low complexity filter

35. ©CMBI 2005 Local implementation - Blast in MRS MRS also contains a BLAST. This BLAST is simpler, has fewer options, knows fewer databases, but is faster.

36. ©CMBI 2005 Blast in MRS MRS Blast remembers all your queries from one session, and stores them in a table. The one you are running is in that table too. Multiple BLASTs can run at one time. Still runningReady

37. ©CMBI 2005 Blast hitlist in MRS

38. ©CMBI 2005 Blast hitlist expansion in MRS

39. ©CMBI 2005 Blast hitlist expansion in MRS

40. ©CMBI 2005 Low complexity motifs visible

41. ©CMBI 2005 Routing

42. ©CMBI 2005 Routing to Clustal

43. ©CMBI 2005 Routing MRS to Blast