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Sequence Alignment Part 3

Introduction to bioinformatics. Sequence Alignment Part 3. WHATS TODAY?. MORE BLAST …. Similarity scores for protein sequences Gaps Statistical significance (e-value). Protein Sequence Alignment.

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Sequence Alignment Part 3

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  1. Introduction to bioinformatics Sequence Alignment Part 3

  2. WHATS TODAY? • MORE BLAST …. • Similarity scores for protein sequences • Gaps • Statistical significance (e-value)

  3. Protein Sequence Alignment • Rule of thumb:Proteins are homologous if 25% identical (length >100)DNA sequences are homologous if 70% identical

  4. Protein Pairwise Sequence Alignment • The alignment tools are similar to the DNA alignment tools • BLASTN for nucleotides • BLASTP for proteins • Main difference: instead of scoring match (+2) and mismatch (-1) we have similarity scores: • Score s(i,j) > 0 if amino acids i and j have similar properties • Score s(i,j) is  0 otherwise • How should we score s(i,j)?

  5. The 20 Amino Acids

  6. Chemical Similarities Between Amino Acids Acids & Amides DENQ (Asp, Glu, Asn, Gln) Basic HKR (His, Lys, Arg) Aromatic FYW (Phe, Tyr, Trp) Hydrophilic ACGPST (Ala, Cys, Gly, Pro, Ser, Thr) Hydrophobic ILMV (Ile, Leu, Met, Val)

  7. Sequence Alignment based on AA similarity TQSPSSLSASVGDTVTITCRASQSISTYLNWYQQKP----GKAPKLLIYAASSSQSGVPS || + |||| +|| ||| | +| | | | | TQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLTKGPSKLNDRADS RFSGSGSGTDFTLTINSLQPEDFATYYCQ---------------QSYSTPHFSQGTKLEI | | | +| | | +|+ || || |++ | | || | + RRSLWDQG-NFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLTL ---KRTVAAPSVFIFPPSDEQLKSGTASVVCLLN---------NFYPREAKVQWKVD ++||| | +++ | | | + ||++|+| TLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKID | = identity + = similarity

  8. Amino Acid Substitutions Matrices • When scoring protein sequence alignments it is common to use a matrix of 20  20, representing all pairwise comparisons : Substitution Matrix

  9. Given an alignment 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 D E M G Y E E M G Y D E M G Y Q E M G Y D E M G Y E E M G Y E E • In this column • E & D are found • 7/8

  10. Symmetric matrix of 20x20 entries: entry (i,j)=entry(j,i) Amino Acid Matrices • Entry (i,i) is greater than any entry (i,j), ji. • Entry (i,j): the score of aligning amino acid i against amino acid j.

  11. PAM - Point Accepted Mutations • Developed by Margaret Dayhoff, 1978. • Analyzed very similar protein sequences • Proteins are evolutionary close. • Alignment is easy. • Point mutations - mainly substitutions • Accepted mutations - by natural selection. • Used global alignment. • Counted the number of substitutions (i,j) per amino acid pair: Many i<->j substitutions => high score s(i,j) • Found that common substitutions occurred involving chemically similar amino acids.

  12. PAM 250 • Similar amino acids are close to each other. • Regions define conserved substitutions.

  13. COO- +H3N C H HCH C C O O- O O- Example: Asp & Glu COO- +H3N C H HCH HCH Score = 3 Aspartate (Asp, D) Glutamate (Glu, E)

  14. Selecting a PAM Matrix • Low PAM numbers: short sequences, strong local similarities. • High PAM numbers: long sequences, weak similarities. • PAM120 recommended for general use (40% identity) • PAM60 for close relations (60% identity) • PAM250 for distant relations (20% identity) • If uncertain, try several different matrices • PAM40, PAM120, PAM250 recommended

  15. BLOSUM • Blocks Substitution Matrix • Steven and Jorga G. Henikoff (1992) • Based on BLOCKS database (www.blocks.fhcrc.org) • Families of proteins with identical function • Highly conserved protein domains • Ungapped local alignment to identify motifs • Each motif is a block of local alignment • Counts amino acids observed in same column • Symmetrical model of substitution AABCDA… BBCDA DABCDA. A.BBCBB BBBCDABA.BCCAA AAACDAC.DCBCDB CCBADAB.DBBDCC AAACAA… BBCCC

  16. BLOSUM Matrices • Different BLOSUMn matrices are calculated independently from BLOCKS • BLOSUMn is based on sequences that are at most n percent identical.

  17. Selecting a BLOSUM Matrix • For BLOSUMn, higher n suitable for sequences which are more similar • BLOSUM62 recommended for general use • BLOSUM80 for close relations • BLOSUM45 for distant relations

  18. Summary: • BLOSUMmatrices are based on the replacement patterns found in more highly conserved regions of the sequences without gaps • PAMmatrices based on mutations observed throughout a global alignment, includes both highly conserved and highly mutable regions

  19. Gap Scores • Example showed -1 score per indel • So gap cost is proportional to its length • Biologically, indels occur in groups • We want our gap score to reflect this • Standard solution: affine gap model • Once-off cost for opening a gap • Lower cost for extending the gap • Changes required to algorithm

  20. Scoring system = Substitution Matrix + Gap Penalty

  21. Gap penalty • We expect to penalize gaps • Scoring for gap opening & for extension • Insertions and deletions are rare in evolution • But once they are created, they are easy to extend • Gap-extension penalty < gap-open penalty • Default gap parameters are given for each matrix: • PAM30: open=9, extension=1 • PAM250: open=14, extension=2

  22. Low Complexity Sequences • AAAAAAAAAAA • ATATATATATATA • CAGCAGCAGCAG Sequences of low complexity can cause getting significant hits which are not true homologues !!! How does BLAST deal with low complexity sequences? By default low complexity sequences are filtered out and replaced by XXXXX

  23. Statistical significance

  24. E-value • The number of hits (with the same similarity score) one can "expect" to see just by chance when searching the given string in a database of a particular size. • higher e-value lower similarity • “sequences with E-value of less than 0.01 are almost always found to be homologous” • The lower bound is normally 0 (we want to find the best)

  25. Expectation Values Increases linearly with length of query sequence Decreases exponentially with score of alignment Increases linearly with length of database

  26. Bit score (S) • Similar to alignment score • Normalized • Higher means more significant • E value:Number of hits of score ≥ S expected by chance • Based on random database of similar size • Lower means more significant • Used to assess the statistical significance of the alignment

  27. Remote homologues • Sometimes BLAST isn’t enough. • Large protein family, and BLAST only gives close members. We want more distant members PSI-BLAST

  28. PSI-BLAST • Position Specific Iterated BLAST Regular blast Construct profile from blast results Blast profile search Final results

  29. PSI-BLAST • Advantage: PSI-BLAST looks for seqs that are close to ours, and learns from them to extend the circle of friends • Disadvantage: if we found a WRONG sequence, we will get to unrelated sequences. This gets worse and worse each iteration

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