Bioinformatics 01 Part 3: Pairwise Alignments and Database Searches

1. Bioinformatics 01Part 3: Pairwise Alignments and Database Searches • Similarity and homology • Gap penalties and scoring matrices in pairwise alignments • Alignment algorithms • Database searching: BLAST and FASTA

2. Similarity and Homology • If proteins that are similar share a common ancestor, they are said to be homologous • Homology can be inferred, but not confirmed, from similarity • Biological data can be used to support the case that two or more similar proteins arose from a common ancestor and are therefore homologous • Proteins can be similar but not homologous, but homologous proteins always show similarity

3. Sequence 1 VLKAHLIDGGSKLTS ||||| ||| Sequence 2 VLKAHIDGGSRLTS ungapped alignment Score: 8 Identity: 53% Sequence 1 VLKAHLIDGGSKLTS ||||| ||||| ||| Sequence 2 VLKAH-IDGGSRLTS gapped alignment Score: 13 Identity: 86.7% Examples of Simple Pairwise Alignments

4. Scoring Penalties in Pairwise Alignments • Penalties are imposed to prevent the unrestricted insertion of gaps • Gap penalty: a penalty for introducing a gap • Extension penalty: a penalty for extending a gap • In protein evolution, it is more likely that an existing gap would be extended than a new gap introduced • Consequently, the score for a gap penalty is greater than the score for an extension penalty

5. Dot Matrix Analysis and Dot Plots • Compares two sequences in the form of a matrix, with each sequence lying along one axis • A match between residues is indicated by a dot • A sliding window is used to cut down “noise” and produce clearer results • Dot plot reveals diagonal lines where there is sufficient similarity between the sequences

6. Dot Plot: Human - and -Globin

7. Scoring Matrices in Pairwise Alignments • A scoring matrix takes into account the significance of matches and mismatches between aligned amino acids • In theory, a scoring matrix could be based on the different chemical and physical properties of amino acids • In practice, scoring matrices are based on observed differences between proteins (or parts of proteins)

8. PAM Scoring Matrices • Based on the analysis of 1,572 changes in 71 groups of closely related proteins (>85% identity) • Mutation probabilities were determined for each amino acid based on a substitution rate of 1% • These were used to construct the PAM 1 (point [or percent] accepted mutation) matrix • The PAM 250 matrix (often used as a default in pairwise alignments) provides scores equivalent to about 20% matches remaining between two sequences

9. BLOSUM Scoring Matrices • Based on amino acid substitutions in a large set of amino acid patterns called blocks, derived from several hundred groups of related proteins • BLOSUM matrices take distant but significant relationships between proteins into account, because only protein segments are considered • Over-representation of amino acid substitutions in closely related protein segments was reduced by combining those segments into one sequence • Example: proteins showing 62% or more identity were grouped to produce the BLOSUM62 matrix

10. Alignments and Dynamic Programming • Complete search of all possible alignments is computationally demanding and frequently impossible • Algorithms that use dynamic programming have been developed to obtain alignments between sequences • Algorithms may produce either global or local alignments

11. Global Alignment: Needleman-Wunsch • A matrix is constructed that shows matches between the two sequences • Moving from the top left of the matrix, a process of summation is carried out taking penalties into account • For any given cell in the matrix, the maximum score for that cell is entered • Needleman-Wunsch attempts to align all residues in the two sequences, and is therefore a global alignment algorithm

12. Local Alignment: Smith-Waterman • Takes into account that two relatively dissimilar sequences may exhibit short regions of local similarity • Smith-Waterman uses a local alignment algorithm to detect these similarities • Each cell in the matrix is considered as the end point of a potential alignment • A value for each cell is calculated using a similarity score, taking matches, mismatches and gaps into account • A backtracking procedure from the highest scoring cell is then used to trace the alignment through the matrix

13. Pairwise Database Searching • Use of the Needleman-Wunsch or Smith-Waterman algorithms in pairwise database searching requires enormous computational power • Heuristic approximations of these algorithms are therefore used in database searches • Examples of search tools are BLAST and FASTA • Both BLAST and FASTA aim to identify short identical matches, which are then extended to produce local alignments

14. BLAST • Search is made for regions of short length (words or k-tuples) obtained from the query sequence that match a database sequence = high scoring pairs (HSPs) • HSPs are extended in both directions to produce optimal alignments above a certain score • A scoring matrix (default is BLOSUM62), gap and gap extension penalties are taken into account in determining alignments • Optimal alignments are then reported in order of decreasing score

15. http://www.ncbi.nlm.nih.gov/BLAST/

16. FASTA • Regions of short length (words) in the query that match a target sequence are determined • High scoring regions (best initial regions) are used to rank matches for further analysis • Longer high scoring regions, including gaps, are generated by joining best initial regions • A full Smith-Waterman alignment is then performed between the high scoring regions • FASTA is slower than BLAST but may, in some cases, be more sensitive

17. http://www.ebi.ac.uk/fasta33/

18. A Final Few Words of Advice • Protein-protein searches are more informative than nucleotide-nucleotide searches (when the query is known to contain a protein-coding nucleotide sequence) • When performing a pairwise database search with a new, protein-coding nucleotide sequence, always use a translation of the nucleotide sequence in all six frames as the query • This can be done by using, for example, a translated BLAST search (such as tblastx, which translates both the query sequence and a nucleotide database)