Pairwise sequence alignments Volker Flegel Vassilios Ioannidis VI - 2004 Page 1 Outline • Introduction • Definitions • Biological context of pairwise alignments • Computing of pairwise alignments • Some programs VI - 2004 Page 2
Importance of pairwise alignments Sequence analysis tools depending on pairwise comparison • Multiple alignments • Profile and HMM making (used to search for protein families and domains) • 3D protein structure prediction • Phylogenetic analysis • Construction of certain substitution matrices • Similarity searches in a database VI - 2004 Page 3 Goal Sequence comparison through pairwise alignments • Goal of pairwise comparison is to find conserved regions (if any) between two sequences • Extrapolate information about our sequence using the known characteristics of the other sequence THIO_EMENI GFVVVDCFATWCGPCKAIAPTVEKFAQTY THIO_EMENI GFVVVDCFATWCGPCKAIAPTVEKFAQTY G ++VD +A WCGPCK IAP +++ A Y G ++VD +A WCGPCK IAP +++ A Y ??? GAILVDFWAEWCGPCKMIAPILDEIADEY ??? GAILVDFWAEWCGPCKMIAPILDEIADEY Extrapolate Extrapolate THIO_EMENI ??? SwissProt VI - 2004 Page 4
Do alignments make sense ? Evolution of sequences • Sequences evolve through mutation and selection � Selective pressure is different for each residue position in a protein (i.e. conservation of active site, structure, charge, etc.) • Modular nature of proteins � Nature keeps re-using domains • Alignments try to tell the evolutionnary story of the proteins Relationships Same Sequence Same Origin Same Function Same 3D Fold VI - 2004 Page 5 Example: An alignment - textual view • Two similar regions of the Drosophila melanogaster Slit and Notch proteins 970 980 990 1000 1010 1020 970 980 990 1000 1010 1020 SLIT_DROME FSCQCAPGYTGARCETNIDDCLGEIKCQNNATCIDGVESYKCECQPGFSGEFCDTKIQFC SLIT_DROME FSCQCAPGYTGARCETNIDDCLGEIKCQNNATCIDGVESYKCECQPGFSGEFCDTKIQFC ..:.: :. :.: ...:.: .. : :.. : ::.. . :.: ::..:. :. :. : ..:.: :. :.: ...:.: .. : :.. : ::.. . :.: ::..:. :. :. : NOTC_DROME YKCECPRGFYDAHCLSDVDECASN-PCVNEGRCEDGINEFICHCPPGYTGKRCELDIDEC NOTC_DROME YKCECPRGFYDAHCLSDVDECASN-PCVNEGRCEDGINEFICHCPPGYTGKRCELDIDEC 740 750 760 770 780 790 740 750 760 770 780 790 VI - 2004 Page 6
Example: An alignment - graphical view • Comparing the tissue-type and urokinase type plasminogen activators. Displayed using a diagonal plot or Dotplot. Tissue-Type plasminogen Activator Urokinase-Type plasminogen Activator URL: www.isrec.isb-sib.ch/java/dotlet/Dotlet.html VI - 2004 Page 7 Some definitions Identity Proportion of pairs of identical residues between two aligned sequences. Generally expressed as a percentage. This value strongly depends on how the two sequences are aligned. Similarity Proportion of pairs of similar residues between two aligned sequences. If two residues are similar is determined by a substitution matrix. This value also depends strongly on how the two sequences are aligned, as well as on the substitution matrix used. Homology Two sequences are homologous if and only if they have a common ancestor. There is no such thing as a level of homology ! (It's either yes or no) • Homologous sequences do not necessarily serve the same function... • ... Nor are they always highly similar: structure may be conserved while sequence is not. VI - 2004 Page 8
More definitions Consider a set S (say, globins) and a test t that tries to detect members of S (for example, through a pairwise comparison with another globin). True positive A protein is a true positive if it belongs to S and is detected by t . True negative A protein is a true negative if it does not belong to S and is not detected by t . False positive A protein is a false positive if it does not belong to S and is (incorrectly) detected by t . False negative A protein is a false negative if it belongs to S and is not detected by t (but should be). VI - 2004 Page 9 Definition example The set of all globins and a test to identify them Consider: • a set S (say, globins: G ) • a test t that tries to detect members of S (for example, through a pairwise comparison with another globin). Globins G G G G True positives G G G True negatives G X False positives X X X False negatives X Matches VI - 2004 Page 10
Even more definitions Sensitivity Ability of a method to detect positives, irrespective of how many false positives are reported. Selectivity Ability of a method to reject negatives, irrespective of how many false negatives are rejected. Greater sensitivity Less selectivity True positives True negatives Less sensitivity False positives Greater selectivity False negatives VI - 2004 Page 11 Pairwise sequence alignment Concept of a sequence alignment • Pairwise Alignment: � Explicit mapping between the residues of 2 sequences deletion Seq AGARFIELDTHELASTFA-TCAT Seq AGARFIELDTHELASTFA-TCAT ||||||||||| || |||| ||||||||||| || |||| Seq BGARFIELDTHEVERYFASTCAT Seq BGARFIELDTHEVERYFASTCAT errors / mismatches insertion – Tolerant to errors (mismatches, insertion / deletions or indels) – Evaluation of the alignment in a biological concept (significance) VI - 2004 Page 12
Pairwise sequence alignement Number of alignments • There are many ways to align two sequences • Consider the sequence fragments below: a simple alignment shows some conserved portions CGATGCAGACGTCA CGATGCAGACGTCA |||||||| |||||||| CGATGCAAGACGTCA CGATGCAAGACGTCA but also: CGATGCAGACGTCA CGATGCAGACGTCA |||||||| |||||||| CGATGCAAGACGTCA CGATGCAAGACGTCA • Number of possible alignments for 2 sequences of length 1000 residues: � more than 10 600 gapped alignments (Avogadro 10 24 , estimated number of atoms in the universe 10 80 ) VI - 2004 Page 13 Alignement evaluation What is a good alignment ? • We need a way to evaluate the biological meaning of a given alignment • Intuitively we "know" that the following alignment: CGAGGCACAACGTCA CGAGGCACAACGTCA ||| ||| |||||| ||| ||| |||||| CGATGCAAGACGTCA CGATGCAAGACGTCA is better than: ATTGGACAGCAATCAGG ATTGGACAGCAATCAGG | || | | | || | | ACGATGCAAGACGTCAG ACGATGCAAGACGTCAG • We can express this notion more rigorously, by using a scoring system VI - 2004 Page 14
Scoring system Simple alignment scores • A simple way (but not the best) to score an alignment is to count 1 for each match and 0 for each mismatch. CGAGGCACAACGTCA CGAGGCACAACGTCA ||| ||| |||||| ||| ||| |||||| CGATGCAAGACGTCA CGATGCAAGACGTCA � Score: 12 ATTGGACAGCAATCAGG ATTGGACAGCAATCAGG | || | | | || | | ACGATGCAAGACGTCAG ACGATGCAAGACGTCAG � Score: 5 VI - 2004 Page 15 Introducing biological information Importance of the scoring system � discrimination of significant biological alignments • Based on physico-chemical properties of amino-acids � Hydrophobicity, acid / base, sterical properties, ... � Scoring system scales are arbitrary • Based on biological sequence information � Substitutions observed in structural or evolutionary alignments of well studied protein families � Scoring systems have a probabilistic foundation Substitution matrices • In proteins some mismatches are more acceptable than others • Substitution matrices give a score for each substitution of one amino- acid by another VI - 2004 Page 16
Substitution matrices (log-odds matrices) Example matrix • For a set of well known proteins: • Align the sequences • Count the mutations at each position • For each substitution set the score to the (Leu, Ile): 2 log-odd ratio (Leu, Cys): -6 observed � � ... log � � � � expected by chance � � • Positive score: the amino acids are similar, mutations from one into the other occur more often then expected by chance during evolution • Negative score: the amino acids are dissimilar, the mutation from one into the other occurs less often then expected by chance during evolution PAM250 From: A. D. Baxevanis, "Bioinformatics" VI - 2004 Page 17 Matrix choice Different kind of matrices • PAM series (Dayhoff M., 1968, 1972, 1978) P ercent A ccepted M utation. A unit introduced by Dayhoff et al . to quantify the amount of evolutionary change in a protein sequence. 1.0 PAM unit, is the amount of evolution which will change, on average, 1% of amino acids in a protein sequence. A PAM(x) substitution matrix is a look-up table in which scores for each amino acid substitution have been calculated based on the frequency of that substitution in closely related proteins that have experienced a certain amount (x) of evolutionary divergence. � Based on 1572 protein sequences from 71 families � Old standard matrix: PAM250 VI - 2004 Page 18
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