Sequence comparisons

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Sequence comparisons. April 9, 2002 Review homework Learning objectives-Review amino acids. Understand difference between identity, similarity and homology. Understand difference between global alignment and local alignment. Workshop-Perform sliding window to compare two sequences - PowerPoint PPT Presentation

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  • Sequence comparisonsApril 9, 2002Review homeworkLearning objectives-Review amino acids. Understand difference between identity, similarity and homology. Understand difference between global alignment and local alignment.Workshop-Perform sliding window to compare two sequences Homework #3 due on Thurs.

  • Amino acid characteristics

  • Review of amino acid characteristicshttp://info.bio.cmu.edu/Courses/BiochemMols/AAViewer/AAVFrameset.htmhttp://info.bio.cmu.edu/Courses/BiochemMols/BCMolecules.html

  • Purpose of finding differences and similarities of amino acids.Infer structural information

    Infer functional information

    Infer evolutionary relationships

  • Evolutionary Basis of Sequence AlignmentSimilarity: Quantity that relates how much two amino acid sequences are alike.2. Identity: Quantity that describes how muchtwo sequences are alike in the strictest terms.3. Homology: a conclusion drawn from datasuggesting that two genes share a commonevolutionary history.

  • Evolutionary Basis of Sequence Alignment (Cont. 1)1. Example: Shown on the next page is a pairwise alignment of two proteins. One is mouse trypsin and the other is crayfish trypsin. They are homologous proteins. The sequences share 41% identity.2. Underlined residues are identical. Asterisks and diamond represent those residues that participate in catalysis. Five gaps are placed to optimize the alignment.

  • Evolutionary Basis of Sequence Alignment (Cont. 2)Why are there regions of identity?1) Conserved function-residues participate in reaction.2) Structural (For example, conserved cysteine residues that form a disulfide linkage) 3) Historical-Residues that are conserved solely due to a common ancestor gene.

  • Evolutionary Basis of Sequence Alignment (Cont. 3)Note: it is possible that two proteins share a high degree of similarity but have two different functions. For example, human gamma-crystallin is a lens protein that has no knownenzymatic activity. It shares a high percentage of identity withE. coli quinone oxidoreductase. These proteins likely had acommon ancestor but their functions diverged.Analogous to railroad car and diner function.

  • Modular nature of proteinsThe previous alignment was global. However, many proteins do not display global patterns of similarity. Instead, they possess local regions of similarity. Proteins can be thought of as assemblies of modular domains. It is thought that this may, in some cases, be due to a process known as exon shuffling.

  • Modular nature of proteins (cont. 1)Exon 1aExon 2aDuplication of Exon 2aExon 1aExon 2aExon 2aExchange with Gene BGene AGene AGene AGene BExon 1aExon 2aExon 3 (Exon 2b from Gene B)Exon 1bExon 2bExon 3 (Exon 2a from Gene A)Exon 1bExon 2bExon 2bGene B

  • Dot PlotsWindow = 1

    Note that 25% ofthe table will befilled due to randomchance. 1 in 4 chanceat each position

  • Dot Plots with window = 2A T G C C T A GA T G C C T A G*******Window = 2The larger the windowthe more noise canbe filtered

    What is thepercent chance thatyou will receive a match randomly?1/16 * 100 = 6.25%

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  • SimilarityIt is easy to score if an amino acid is identical to another (thescore is 1 if identical and 0 if not). However, it is not easy togive a score for amino acids that are somewhat similar.+NH3CO2-+NH3CO2-LeucineIsoleucineShould they get a 0 (non-identical) or a 1 (identical) orSomething in between?

  • Identity MatrixSimplest type of scoring matrixLICA1000L100I10C1A

  • The Point-Accepted-Mutation (PAM) model of evolution and the PAM scoring matrixIt implies that each amino acid (AA) mutates independently ofeach other with a probability which depends only on the AA. Since there are 20 AA, the transition probabilities aredescribed by a 20X20-mutation matrix, denoted by M. A standard M, which defines a 1-PAM change.

    Point Accepted Mutation (PAM) Distance: A 1-PAM unit changes 1% of the amino acids on average:

    where fi is the frequency of AA i. One PAM is a unit of evolutionarydivergence in which 1% of the amino acids have been changed.

  • The Point-Accepted-Mutation (PAM) model of evolution and the PAM scoring matrix (cont. 1)A 2-PAM unit is equivalent to two 1-PAM unit evolution (or M2). A k-PAM unit is equivalent to k 1-PAM unit evolution (or Mk). Example 1: CNGTTDQVDKIVKILNEGQIASTDVVEVVVSPPYVFLPVVKSQLRPEIQV|||||||||||||| |||||||||||||||||||||||||||||||||||CNGTTDQVDKIVKIRNEGQIASTDVVEVVVSPPYVFLPVVKSQLRPEIQVlengths = 50 1 Mismatch PAM distance = 2

  • Two proteins that are similar in certain regionsTissue plasminogen activator (PLAT)Coagulation factor 12 (F12).

  • The Dotter Program Program consists of three components:

    Sliding window

    A table that gives a score for each amino acid match

    A graph that converts the score to a dot of certain density. The higher the density the higher the score.

  • Region ofsimilarity