Page 1 August 2006 Pairwise sequence alignments Etienne de Villiers Adapted with permission of Swiss EMBnet node and SIB

Page 2 August 2006 Outline Introduction Definitions Biological context of pairwise alignments Computing of pairwise alignments Some programs

Page 3 August 2006 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

Page 4 August 2006 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 G ++VD +A WCGPCK IAP +++ A Y ??? GAILVDFWAEWCGPCKMIAPILDEIADEY THIO_EMENI GFVVVDCFATWCGPCKAIAPTVEKFAQTY G ++VD +A WCGPCK IAP +++ A Y ??? GAILVDFWAEWCGPCKMIAPILDEIADEY THIO_EMENI SwissProt Extrapolate ???

Page 5 August 2006 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 3D Fold Same OriginSame Function

Page 6 August 2006 Example: An alignment - textual view Two similar regions of the Drosophila melanogaster Slit and Notch proteins SLIT_DROME FSCQCAPGYTGARCETNIDDCLGEIKCQNNATCIDGVESYKCECQPGFSGEFCDTKIQFC..:.: :. :.:...:.:.. : :.. : ::... :.: ::..:. :. :. : NOTC_DROME YKCECPRGFYDAHCLSDVDECASN-PCVNEGRCEDGINEFICHCPPGYTGKRCELDIDEC SLIT_DROME FSCQCAPGYTGARCETNIDDCLGEIKCQNNATCIDGVESYKCECQPGFSGEFCDTKIQFC..:.: :. :.:...:.:.. : :.. : ::... :.: ::..:. :. :. : NOTC_DROME YKCECPRGFYDAHCLSDVDECASN-PCVNEGRCEDGINEFICHCPPGYTGKRCELDIDEC

Page 7 August 2006 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:

Page 8 August 2006 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.

Page 9 August 2006 Matches Definition example The set of all globins and a test to identify them True positives True negatives False positives False negatives 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 G G G G X X X X X

Page 10 August 2006 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).

Page 11 August 2006 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. True positives True negatives False positives False negatives Greater sensitivity Less selectivity Less sensitivity Greater selectivity

Page 12 August 2006 Pairwise sequence alignment Concept of a sequence alignment Pairwise Alignment:  Explicit mapping between the residues of 2 sequences – Tolerant to errors (mismatches, insertion / deletions or indels) – Evaluation of the alignment in a biological concept (significance) Seq A GARFIELDTHELASTFA-TCAT ||||||||||| || |||| Seq B GARFIELDTHEVERYFASTCAT Seq A GARFIELDTHELASTFA-TCAT ||||||||||| || |||| Seq B GARFIELDTHEVERYFASTCAT errors / mismatchesinsertion deletion

Page 13 August 2006 Pairwise sequence alignment Number of alignments There are many ways to align two sequences Consider the sequence fragments below: a simple alignment shows some conserved portions but also: CGATGCAGACGTCA |||||||| CGATGCAAGACGTCA CGATGCAGACGTCA |||||||| CGATGCAAGACGTCA CGATGCAGACGTCA |||||||| CGATGCAAGACGTCA CGATGCAGACGTCA |||||||| CGATGCAAGACGTCA Number of possible alignments for 2 sequences of length 1000 residues:  more than gapped alignments (Avogadro 10 24, estimated number of atoms in the universe )

Page 14 August 2006 Alignment 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: is better than: CGAGGCACAACGTCA ||| ||| |||||| CGATGCAAGACGTCA CGAGGCACAACGTCA ||| ||| |||||| CGATGCAAGACGTCA ATTGGACAGCAATCAGG | || | | ACGATGCAAGACGTCAG ATTGGACAGCAATCAGG | || | | ACGATGCAAGACGTCAG We can express this notion more rigorously, by using a scoring system

Page 15 August 2006 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.  Score: 12 CGAGGCACAACGTCA ||| ||| |||||| CGATGCAAGACGTCA CGAGGCACAACGTCA ||| ||| |||||| CGATGCAAGACGTCA ATTGGACAGCAATCAGG | || | | ACGATGCAAGACGTCAG ATTGGACAGCAATCAGG | || | | ACGATGCAAGACGTCAG  Score: 5

Page 16 August 2006 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

Page 17 August 2006 Substitution matrices (log-odds matrices) Example matrix PAM250 From:A. D. Baxevanis, "Bioinformatics" (Leu, Ile):2 (Leu, Cys):-6... 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 For a set of well known proteins: Align the sequences Count the mutations at each position For each substitution set the score to the log-odd ratio

Page 18 August 2006 Matrix choice Different kind of matrices PAM series(Dayhoff M., 1968, 1972, 1978) Percent Accepted Mutation. 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

Page 19 August 2006 Matrix choice Different kind of matrices BLOSUM series(Henikoff S. & Henikoff JG., PNAS, 1992) Blocks Substitution Matrix. A substitution matrix in which scores for each position are derived from observations of the frequencies of substitutions in blocks of local alignments in related proteins. Each matrix is tailored to a particular evolutionary distance. In the BLOSUM62 matrix, for example, the alignment from which scores were derived was created using sequences sharing no more than 62% identity. Sequences more identical than 62% are represented by a single sequence in the alignment so as to avoid over- weighting closely related family members.  Based on alignments in the BLOCKS database  Standard matrix:BLOSUM62

Page 20 August 2006 Matrix choice Limitations Substitution matrices do not take into account long range interactions between residues. They assume that identical residues are equal ( whereas in real life a residue at the active site has other evolutionary constraints than the same residue outside of the active site) They assume evolution rate to be constant.

Page 21 August 2006 Alignment score Amino acid substitution matrices Example: PAM250 Most used: Blosum62 Raw score of an alignment TPEA ¦| | APGA TPEA ¦| | APGA Score = 1=

Page 22 August 2006 Gaps Insertions or deletions Proteins often contain regions where residues have been inserted or deleted during evolution There are constraints on where these insertions and deletions can happen (between structural or functional elements like: alpha helices, active site, etc.) Gaps in alignments GCATGCATGCAACTGCAT ||||||||| GCATGCATGGGCAACTGCAT GCATGCATGCAACTGCAT ||||||||| GCATGCATGGGCAACTGCAT can be improved by inserting a gap GCATGCATG--CAACTGCAT ||||||||| GCATGCATGGGCAACTGCAT GCATGCATG--CAACTGCAT ||||||||| GCATGCATGGGCAACTGCAT

Page 23 August 2006 Gap opening and extension penalties Costs of gaps in alignments We want to simulate as closely as possible the evolutionary mechanisms involved in gap occurence. Example Two alignments with identical number of gaps but very different gap distribution. We may prefer one large gap to several small ones (e.g. poorly conserved loops between well-conserved helices) CGATGCAGCAGCAGCATCG |||||| ||||||| CGATGC------AGCATCG CGATGCAGCAGCAGCATCG |||||| ||||||| CGATGC------AGCATCG CGATGCAGCAGCAGCATCG || || |||| || || | CG-TG-AGCA-CA--AT-G CGATGCAGCAGCAGCATCG || || |||| || || | CG-TG-AGCA-CA--AT-G gap opening Gap opening penalty Counted each time a gap is opened in an alignment (some programs include the first extension into this penalty) gap extension Gap extension penalty Counted for each extension of a gap in an alignment

Page 24 August 2006 Gap opening and extension penalties Example With a match score of 1 and a mismatch score of 0 With an opening penalty of 10 and extension penalty of 1, we have the following score: CGATGCAGCAGCAGCATCG |||||| ||||||| CGATGC------AGCATCG CGATGCAGCAGCAGCATCG |||||| ||||||| CGATGC------AGCATCG CGATGCAGCAGCAGCATCG || || |||| || || | CG-TG-AGCA-CA--AT-G CGATGCAGCAGCAGCATCG || || |||| || || | CG-TG-AGCA-CA--AT-G gap opening 13 x x 1 = - 3 gap extension 13 x x x 1 = - 43

Page 25 August 2006 Statistical evaluation of results Alignments are evaluated according to their score Raw score  It's the sum of the amino acid substitution scores and gap penalties (gap opening and gap extension)  Depends on the scoring system (substitution matrix, etc.)  Different alignments should not be compared based only on the raw score It is possible that a "bad" long alignment gets a better raw score than a very good short alignment.  We need a normalised score to compare alignments !  We need to evaluate the biological meaning of the score (p-value, e-value). Normalised score  Is independent of the scoring system  Allows the comparison of different alignments  Units: expressed in bits

Page 26 August Statistical evaluation of results Distribution of alignment scores - Extreme Value Distribution Random sequences and alignment scores  Sequence alignment scores between random sequences are distributed following an extreme value distribution (EVD). score obs Ala Val... Trp Random sequencesPairwise alignmentsScore distribution low score high score high score due to "luck"

Page 27 August 2006 score y: our alignment is very improbable to obtain with random sequences Statistical evaluation of results Distribution of alignment scores - Extreme Value Distribution High scoring random alignments have a low probability. The EVD allows us to compute the probability with which our biological alignment could be due to randomness (to chance). Caveat: finding the threshold of significant alignments. score score x: our alignment has a great probability of being the result of random sequence similarity Threshold significant alignment

Page 28 August 2006 Statistical evaluation of results Statistics derived from the scores p-value  Probability that an alignment with this score occurs by chance in a database of this size  The closer the p-value is towards 0, the better the alignment e-value  Number of matches with this score one can expect to find by chance in a database of this size  The closer the e-value is towards 0, the better the alignment Relationship between e-value and p-value:  In a database containing N sequences e = p x N 100% 0% N 0

Page 29 August 2006 Diagonal plots or Dotplot Concept of a Dotplot Produces a graphical representation of similarity regions. The horizontal and vertical dimensions correspond to the compared sequences. A region of similarity stands out as a diagonal. Tissue-Type plasminogen Activator Urokinase-Type plasminogen Activator

Page 30 August 2006 Reading a Dotplot  As simple as projecting the diagonals onto the axis. Tissue-Type plasminogen Activator Urokinase-Type plasminogen Activator Tissue-Type plasminogen Activator AA’BDC Urokinase-Type plasminogen Activator ACBD

Page 31 August 2006 Dotplot limitations  It's a visual aid. The human eye can rapidly identify similar regions in sequences.  It's a good way to explore sequence organisation. Between 2 different sequences or Inside the same sequence (ssDNA repeats, RNA stem loops, etc)  It does not provide an alignment.

Page 32 August 2006 Finding an alignment Alignment algorithms An alignment program tries to find the best alignment between two sequences given the scoring system. This can be seen as trying to find a path through the dotplot diagram including all (or the most visible) diagonals. Alignment types GlobalAlignment between the complete sequence A and the complete sequence B LocalAlignment between a sub-sequence of A an a sub- sequence of B Computer implementation (Algorithms) Dynamic programing GlobalNeedleman-Wunsch LocalSmith-Waterman

Page 33 August 2006 Global alignment (Needleman-Wunsch) Example  Global alignments are very sensitive to gap penalties  Global alignments do not take into account the modular nature of proteins Tissue-Type plasminogen Activator Urokinase-Type plasminogen Activator Global alignment:

Page 34 August 2006 Local alignment (Smith-Waterman) Example  Local alignments are more sensitive to the modular nature of proteins  They can be used to search databases Tissue-Type plasminogen Activator Urokinase-Type plasminogen Activator Local alignments:

Page 35 August 2006 Algorithms for pairwise alignments Web resources LALIGN - pairwise sequence alignment: PRSS - alignment score evaluation: Concluding remarks Substitution matrices and gap penalties introduce biological information into the alignment algorithms. It is not because two sequences can be aligned that they share a common biological history. The relevance of the alignment must be assessed with a statistical score. There are many ways to align two sequences. Do not blindly trust your alignment to be the only truth. Especially gapped regions may be quite variable. Sequences sharing less than 20% similarity are difficult to align:  You enter the Twilight Zone (Doolittle, 1986)  Alignments may appear plausible to the eye but are no longer statistically significant.  Other methods are needed to explore these sequences (i.e: profiles)

Page 36 August 2006 Acknowledgments & References Laurent Falquet, Lorenza Bordoli,Volker Flegel, Frédérique Galisson References Ian Korf, Mark Yandell & Joseph Bedell, BLAST, O’Reilly David W. Mount, Bioinformatics, Cold Spring Harbor Laboratory Press Jean-Michel Claverie & Cedric Notredame, Bioinformatics for Dummies, Wiley Publishing