BLAST and Multiple Sequence Alignment

1. BLAST and Multiple Sequence Alignment • Learning objectives-Learn the basics of BLAST, Psi-BLAST, and multiple sequence alignment

2. Which program should one use? • Most researchers use methods for determining local similarities: • Smith-Waterman (gold standard) • FASTA • BLAST } Do not find every possible alignment of query with database sequence. These are used because they run faster than S-W

3. BLAST • Basic Local Alignment Search Tool Three phases: 1) List of high scoring words 2) Scan the sequence database 3) Extend hits

4. The threshold and word size • The program declares a hit if the word taken from the query sequence has a score >= T when a scoring matrix is used. • This allows the word size (W) to be kept high (for speed) without sacrificing sensitivity. • If T is increased, the number of background hits is reduced and the program will run faster.

5. Phase 1: Compile a list of high-scoring words above threshold T. Query sequence: human p53: . . . RCPHHERCSD. . . Words derived from query sequence: RCP, CPH, PHH, HHE, … List of words above threshold T: . . . . . . Note: The line is located at the threshold. Word size is 3.

6. Phase 3: Extend the hits and terminate when the tabulated score drops below a cutoff score. Query EVVRRCPHHERCSD EVVRRCPHHER S+ Sbjct EVVRRCPHHERSSE (Ch. hamster p53 O09185) Phase 2: Scan the database for short segments that match the list of acceptable words/scores above or equal to threshold T. If the hit is extended far enough the query/subj segment is called a High Scoring Segment Pair (HSP).

7. What are the different BLAST programs? • blastp • compares an amino acid query sequence against a protein sequence database • blastn • compares a nucleotide query sequence against a nucleotide sequence database • blastx • compares a nucleotide query sequence translated in all reading frames against a protein sequence database • tblastn • compares a protein query sequence against a nucleotide sequence database dynamically translated in all reading frames • tblastx • compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database. Please note that tblastx program cannot be used with the nr database on the BLAST Web page.

8. What are the different BLAST programs? (continued) • psi-blast • Compares a protein sequence to a protein database. Performs the comparison in an iterative fashion in order to detect homologs that are evolutionarily distant. • blast2 • Compares two protein or two nucleotide sequences.

9. The E value (false positive expectation value) The Expect value (E) is a parameter that describes the number of “hits” one can "expect" to see just by chance when searching a database of a particular size. It decreases exponentially as the Similarity Score (S) increases (inverse relationship). The higher the Similarity Score, the lower the E value. Essentially, the E value describes the random background noise that exists for matches between two sequences. The E value is used as a convenient way to create a significance threshold for reporting results. When the E value is increased from the default value of 10 prior to a sequence search, a larger list with more low-similarity scoring hits can be reported. An E value of 1 assigned to a hit can be interpreted as meaning that in a database of the current size you might expect to see 1 match with a similar score simply by chance.

10. E value (Karlin-Altschul statistics) E = K•m•n•e-λS Where K is a scaling factor (constant), m is the length of the query sequence, n is the length of the database sequence, λ is the decay constant, S is the similarity score. If S increases, E decreases exponentially. If the decay constant increases, E decreases exponentially If m•n increases the “search space” increases and there is a greater chance for a random “hit”, E increases. Larger database will increase E. However, larger query sequence often decreases E. Why???

11. Thought problem A homolog to a query sequence resides in two databases. One is the UniProtKB/SwissProt database and the other is the PDB database. After performing BLAST search against the UniProtKB database you obtain an E value of 1. After performing the BLAST search against the PDB database you obtain an E value of 0.0625. What is the relative sizes of the two databases?

12. Using BLAST to get quick answers to bioinformatics problems

13. Using BLAST to get quick answers to bioinformatics problems (cont.)

14. Filtering Repetitive Sequences • Over 50% of genomic DNA is repetitive • This is due to: • retrotransposons • ALU region • microsatellites • centromeric sequences, telomeric sequences • 5’ Untranslated Region of ESTs Example of EST with simple low complexity region: T27311 GGGTGCAGGAATTCGGCACGAGTCTCTCTCTCTCTCTCTCTCTCTCTC TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC

15. Filtering Repetitive Sequences and Masking • Options available for user.

16. PSI-BLAST • PSI-position specific iterative • a position specific scoring matrix (PSSM) is constructed automatically from multiple HSPs of initial BLAST search. Normal E value threshold is used. • The PSSM is created as the new scoring matrix for a second BLAST search. A low E value threshold is used (E=.001). • Result-1) obtains distantly related sequences 2) finds the important residues that provide function or structure.

17. A PSSM

18. Steps to multiple alignment Create Alignment Edit the alignment to ensure that regions of functional or structural similarity are preserved Structure Analysis Find conserved motifs to deduce function Design of PCR primers Phylogenetic Analysis

19. Multiple Sequence Alignment • Collection of three or more protein (or nucleic acid) sequences partially or completely aligned. • Aligned residues tend to occupy corresponding positions in the 3-D structure of each aligned protein.

20. Practical use of MSA • Helps to place protein into a group of related proteins. It will provide insight into function, structure and evolution. • Helps to detect homologs • Identifies sequencing errors • Identifies important regulatory regions in the promoters of genes.

21. Clustal W (Thompson et al., 1994) • CLUSTAL=Cluster alignment • The underlying concept is that groups of sequences are phylogenetically related. If they can be aligned then one can construct a phylogenetic tree.

22. Flowchart of computation steps in Clustal W (Thompson et al., 1994) Pairwise alignment: calculation of distance matrix Creation of unrooted neighbor-joining tree Rooted nJ tree (guide tree) and calculation of sequence weights Progressive alignment following the guide tree

23. Step 1-Pairwise alignments Compare each sequence with each other and calculate a distance matrix. A - B .87 - C .59 .60 - Different sequences Each number represents the number of exact matches divided by the sequence length (ignoring gaps). Thus, the higher the number the more closely related the two sequences are. A B C In this matrix, sequence A is 87% identical to sequence B

24. Step 1-Pairwise alignments Compare each sequence with each other and pairwise alignment scores human EYSGSSEKIDLLASDPHEALICKSERVHSKSVESNIEDKIFGKTYRKKASLPNLSHVTEN 480 dog EYSGSSEKIDLMASDPQDAFICESERVHTKPVGGNIEDKIFGKTYRRKASLPKVSHTTEV 477 mouse GGFSSSRKTDLVTPDPHHTLMCKSGRDFSKPVEDNISDKIFGKSYQRKGSRPHLNHVTE 476

25. Step 1-Calculation of Distance Matrix Use the Distance Matrix to create a Guide Tree to determine the “order” of the sequences. Hbb-Hu 1 - Hbb-Ho 2 .17 - Hba-Hu 3 .59 .60 - Hba-Ho 4 .59 .59 .13 - Myg-Ph 5 .77 .77 .75 .75 - Gib-Pe 6 .81 .82 .73 .74 .80 - Lgb-Lu 7 .87 .86 .86 .88 .93 .90 - 1 2 3 4 5 6 7 D = 1 – (I) D = Difference score # of identical aa’s in pairwise global alignment I = total number of aa’s in shortest sequence

26. Myg-Ph Hba-Ho Hba-Hu Hbb-Ho Gib-Pe Hbb-Hu Lgb-Lu Step 2-Create unrooted NJ tree

27. Step 3-Create Rooted NJ Tree Weight Alignment Order of alignment: 1 Hba-Hu vs Hba-Ho 2 Hbb-Hu vs Hbb-Ho 3 A vs B 4 Myg-Ph vs C 5 Gib-Pe vs D 6 Lgh-Lu vs E

28. Step 4-Progressive alignment

29. Step 4-Progressive alignment Scoring during progressive alignment

30. Rules for alignment • Short stretches of 5 hydrophilic residues often indicate loop or random coil regions (not essential for structure) and therefore gap penalties are reduced reduced for such stretches. • Gap penalties for closely related sequences are lowered compared to more distantly related sequences (“once a gap always a gap” rule). It is thought that those gaps occur in regions that do not disrupt the structure or function. • Alignments of proteins of known structure show that proteins gaps do not occur more frequently than every eight residues. Therefore penalties for gaps increase when required at 8 residues or less for alignment. This gives a lower alignment score in that region. • A gap weight is assigned after each aa according the frequency that such a gap naturally occurs after that aa in nature

31. Amino acid weight matrices • As we know, there are many scoring matrices that one can use depending on the relatedness of the aligned proteins. • As the alignment proceeds to longer branches the aa scoring matrices are changed to more divergent scoring matrices. The length of the branch is used to determine which matrix to use and contributes to the alignment score.

32. Example of Sequence Alignment using Clustal W Asterisk represents identity : represents high similarity . represents low similarity

33. Multiple Alignment Considerations • Quality of guide tree. It would be good to have a set of closely related sequences in the alignment to set the pattern for more divergent sequences. • If the initial alignments have a problem, the problem is magnified in subsequent steps. • CLUSTAL W is best when aligning sequences that are related to each other over their entire lengths • Do not use when there are variable N- and C- terminal regions • If protein is enriched for G,P,S,N,Q,E,K,R then these residues should be removed from gap penalty list. (what types of residues are these?) Reference: http://www-igbmc.u-strasbg.fr/BioInfo/ClustalW/