Pairwise sequence alignments

Transcription

1 Pairwise sequence alignments Volker Flegel Vassilios Ioannidis VI Page 1 Outline Introduction Definitions Biological context of pairwise alignments Computing of pairwise alignments Some programs VI Page 2

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 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 G ++VD +A WCGPCK IAP +++ A Y??? GAILVDFWAEWCGPCKMIAPILDEIADEY Extrapolate??? THIO_EMENI SwissProt VI Page 4

3 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 Page 5 Example: An alignment - textual view Two similar regions of the Drosophila melanogaster Slit and Notch proteins SLIT_DROME SLIT_DROME FSCQCAPGYTGARCETNIDDCLGEIKCQNNATCIDGVESYKCECQPGFSGEFCDTKIQFC FSCQCAPGYTGARCETNIDDCLGEIKCQNNATCIDGVESYKCECQPGFSGEFCDTKIQFC..:.:..:.: :. :. :.: :.:...:.:...:.:.... : : :.. :.. : : ::.. ::.... :.: :.: ::..:. ::..:. :. :. :. :. : : NOTC_DROME NOTC_DROME YKCECPRGFYDAHCLSDVDECASN-PCVNEGRCEDGINEFICHCPPGYTGKRCELDIDEC YKCECPRGFYDAHCLSDVDECASN-PCVNEGRCEDGINEFICHCPPGYTGKRCELDIDEC VI Page 6

4 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: VI 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 Page 8

5 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 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 True positives G G G True negatives False positives False negatives X G G X X X X Matches VI Page 10

6 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 False positives False negatives Less sensitivity Greater selectivity VI Page 11 Pairwise sequence alignment Concept of a sequence alignment Pairwise Alignment:! Explicit mapping between the residues of 2 sequences deletion Seq A GARFIELDTHELASTFA-TCAT Seq B GARFIELDTHEVERYFASTCAT errors / mismatches insertion Tolerant to errors (mismatches, insertion / deletions or indels) Evaluation of the alignment in a biological concept (significance) VI Page 12

7 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 but also: 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 ) VI 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: is better than: CGAGGCACAACGTCA CGATGCAAGACGTCA ATTGGACAGCAATCAGG ACGATGCAAGACGTCAG We can express this notion more rigorously, by using a scoring system VI Page 14

8 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!Score: 5 CGAGGCACAACGTCA CGATGCAAGACGTCA ATTGGACAGCAATCAGG ACGATGCAAGACGTCAG VI 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 aminoacid by another VI Page 16

9 Substitution matrices (log-odds matrices) Example matrix (Leu, Ile): 2 (Leu, Cys): 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 & log $ % observed 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 Page 17 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 VI Page 18

10 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 VI Page 19 Matrix choice Limitations Substitution matrices do not take into account long range interactions between residues. They assume that identical residues are equal (whereas in reallife 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. VI Page 20

11 Alignment score Amino acid substitution matrices Example: PAM250 Most used: Blosum62 Raw score of an alignment TPEA _ _ APGA Score = = 9 VI Page 21 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 can be improved by inserting a gap GCATGCATG--CAACTGCAT GCATGCATGGGCAACTGCAT VI Page 22

12 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 gap opening gap extension Gap opening penalty CGATGCAGCAGCAGCATCG CG-TG-AGCA-CA--AT-G Counted each time a gap is opened in an alignment (some programs include the first extension into this penalty) Gap extension penalty Counted for each extension of a gap in an alignment VI Page 23 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 gap opening gap extension CGATGCAGCAGCAGCATCG CG-TG-AGCA-CA--AT-G 13 x x 1 = x 1-5 x 10-6 x 1 = -43 VI Page 24

13 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 VI Page 25 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). Random sequences Pairwise alignments Score distribution Ala Ala Val Val Tr Tr p p score x score y obs score VI Page 26

14 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. Threshold significant alignment score x: our alignment has a great probability of being the result of random sequence similarity score y: our alignment is very improbable to obtain with random sequences score VI Page 27 Statistical evaluation of results 100% 0% 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 N 0 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 VI Page 28

15 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 VI Page 29 Dotplot construction Simple example A dot is placed at each position where two residues match.! The colour of the dot can be chosen according to the substitution value in the substitution matrix T Note THEFA-TCAT THEFASTCAT This method produces dotplots with too much noise to be useful! The noise can be reduced by calculating a score using a window of residues! The score is compared to a threshold or stringency VI Page 30

16 Dotplot construction Window example Each window of the first sequence is aligned (without gaps) to each window of the 2nd sequence A colour is set into a rectangular array according to the score of the aligned windows HEF THE CAT THE HEF Score: VI Page 31 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.! It does not provide an alignment. Tissue-Type plasminogen Activator Urokinase-Type plasminogen Activator VI Page 32

17 Creating an alignment Relationship between alignment and dotplot An alignment can be seen as a path through the dotplot diagramm. Seq Seq B B Seq Seq A A A-CA-CA ACA--CA A-CA-CA ACA--CA ACCAAC- A-CCAAC ACCAAC- A-CCAAC VI Page 33 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. Alignement types Global Alignment between the complete sequence A and the complete sequence B Local Alignment between a sub-sequence of A an a subsequence of B Computer implementation (Algorithms) Dynamic programing Global Needleman-Wunsch Local Smith-Waterman VI Page 34

18 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: VI Page 35 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: VI Page 36

19 Optimal alignment extension How to extend optimaly an optimal alignment An optimal alignment up to positions i and j can be extended in 3 ways. Keeping the best of the 3 guarantees an extended optimal alignment. Seq A Seq B a 1 a 2 a 3... a i-1 a i b 1 b 2 b 3... b j-1 b j Seq A Seq B a 1 a 2 a 3... a i-1 a i b 1 b 2 b 3... b j-1 b j a i+1 b j+1 Score = Score ij + b j+1 Subst i+1j+1 Seq A Seq B a 1 a 2 a 3... a i-1 a i b 1 b 2 b 3... b j-1 b j a i+1 - Score = Score ij - gap Seq A Seq B a 1 a 2 a 3... a i-1 a i b 1 b 2 b 3... b j-1 b j - b j+1 Score = Score ij - gap We have the optimal alignment extended from i and j by one residue. VI Page 37 Exact algorithms Simple example (Needleman-Wunsch) Scoring system:! Match score: 2! Mismatch score: -1! Gap penalty: -2 F (i- -d 1,j) F(i,j): score at position i, j GATTA G A s(x i,y A -6 j ): TA -8 match T -10 or C -12 mismatch T score T (or substitution C matrix value) for residues x i and y j d: gap penalty (positive value) F (i-1,js 1) (xi,yj) F (i,j- 1) -d F (i,j) Note 0-2 GA-TTA GAATTC We have to keep track of the origin of the score for each element in the matrix.! This allows to build the alignment by traceback when the matrix has been completely filled out. Computation time is proportional to the size of sequences (n x m). VI Page 38

20 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) VI Page 39