1. Multiple Sequence Alignment Carlow IT Bioinformatics November 2006

2. MSA A central technique in bioinformatics along with: –homology searching –multiple sequence alignment –phylogenetic trees

3. An example “all you have to do” is re-write your sequences so that similar features finish up in the same columns

4. Evolutionary relationship “similar features” ideally means homologous – with a shared ancestor clustalW and T-coffee mimic the process of evolution –by weighting similar residues by how conserved they are in evolution Important AAs don’t mutate Less important AAs change easily, even randomly –by inserting judicious gaps

5. Criteria for alignment Amino acids in the same column have –Structural similarity (used by threading progs) Practical exercise inferring position of Bsu recA AAs –Evolutionary similarity – residues have a common ancestor –Functional similarity (active site, C-C bonds) may have to hand edit known functions –Sequence similarity The first 3 (clear biological attributes) are, you hope, reflected by the last (an abstraction) which is what MSA programs use

6. Applications Discover conserved patterns/motifs –A step to describing a protein domain –MSA can add a distant relative to your protein family A step to define DNA regulatory elements. Prediction of 2 nd Structure and helps 3-D A step to phylogenetic trees: to describe or show the process of evolution PCR analysis/primer design –find most and least degenerate regions of your sequence

7. So why difficult? Trivial 2 seq alignment: 3 possibilities. As length and # of seqs increase, number of possible permutations goes astronomical FGDERTHHS FGD--DHRS FGDERTHHS FGDD--HRS FGDERTHHS FGD-D-HRS Where put the gap?

8. Some data Cat ATGAAACGTCGGATCTAA Dog ATGAATCGACCCATCTAA Mus ATGGCGTGGCTTGGCATGTGA Rat ATGGCATGTCGTGGCATGTAG Protocol step 1 Align each pair of seqs C-D, C-M, C-R etc Get a score for each alignment And make a …

9. Similarity matrix Cat Dog Mus Rat Cat ID 14 10 10 Dog ID 10 10 Mus ID 16 Rat ID Number of identical residues –Which pair of sequences is most similar?

10. Progressive alignment Align the two most similar sequences, inserting any gaps. Mus/Rat: lock these sequences together (call it “RODent) Return to similarity matrix to find next most similar seqs or sequence cluster Dog/Cat: align and lock (call it CARnivore) –if next step requires a gap, then gap inserted in both carnivore sequences Align next most …(now its iterative)

11. An alignment Cat ATGAAACGTCGG---ATCTAA Dog ATGAATCGACCC---ATCTAA Mus ATGGCGTGGCTTGGCATGTGA Rat ATGGCATGTCGTGGCATGTAG *** * * ** * Good: Always a two “sequence” problem –So computationally possible Bad: Can’t rewrite or decouple (part of) the dog/cat alignment in the light of later info. Locked in a (suboptimal?) trough.

12. More complex 10 seq example

13. Choosing the right seqs Use MSA to inform you! Always use AA/protein if possible –can copygaps back to DNA later Start with 6-15 sequences Eliminate very different (<30% id) seqs Eliminate identical sequences Watch out for partial sequences …or sequences that need ++ gaps to align Check for repeats with dotlet, Lalign

14. Less is more Large alignments –take ++ CPU and time –are hard to do well –are difficult to display –are difficult to use: in trees for example –may include marginal seqs that wreck whole alignment So start small and add/eliminate seqs until you have a clear informative picture

15. Level of variation is important Choose sequence family with best rate of evolution for your taxonomic group –Histones evolve very slow (compare kingdoms) –Transferrins are fast (compare classes,orders) Closely related sequences may have identical protein (but variable DNA) Distantly related sequences no DNA signal (“saturated”)

16. ClustalW at embnet.ch.org Paste in your FASTA sequences

17. Output choices

18. ClustalW at EBI Paste in your (FASTA) sequences

19. EBI: loads of options

20. T-coffee Minimal input parameters and STILL a better job than ClustalW

21. Output EBI clustalW Pairwise distance etc Alignment Guidetree What you submitted Jalview alignment editor

22. An alignment fragment ACT_CANAL -MDGEEVAALIIDNGSGMCKA ACT_CANDU -MDGEEVAALVIDNGSGMCKA ACT_PICAN -MDGEDVAALVIDNGSGMCKA ACT_PICPA -MDGEDVAALVIDNGSGMCKA ACT_KLULA -MDS-EVAALVIDNGSGMCKA ACT_YEAST -MDS-EVAALVIDNGSGMCKA ACT_YARLI -MED-ETVALVIDNGSGMCKA ACT2_ABSGL MSMEEDIAALVIDNASGMCKA ACT2_SCHCO --MDDEIQAVVIDNGSGMCKA : *:::**.****** * All AA in column identical : AA similar size & hydrophobicity. AA similar size or hydrophobicity ClustalW format

23. The alignment, so what next? Look at it very closely Hand edit if necessary (probably) Eliminate problem sequences and redo? Use display option best for next step –Phylip format for trees

24. Parameter changes Substit matrix PAM, Gonnet, Blosum –Clustalw chooses which matrix within family PAM30 for closely related pairs; PAM120; PAM250 for more distant –Difficult alignment: matrix change may help Gap penalty (open and extend) have optimal values for each family: find which by trial and error. –Clustalw puts gaps (which are often external loops) near previous gaps (longer loop) MSA does the grunt work. YOU do the fine tuning.

25. Guide tree To figure which pairs of sequences to align first, a phylogenetic tree is calculated from pairwise distance matrix. –Stored in a DND (dendrogram) file Never use this file to draw a tree Clustalw can construct a tree from the multiple sequence alignment (better than pairwise)

26. Alignment display: weblogo Always remember: sequence represents a 3-D structure

27. Patterns to recognise (more reliable in MSA than in single seq) Alternate hydrophobic residues –Surface  -sheet (zig-zag-zig-zag) Runs of hydrophobic residues –Interior/buried  -sheet Residues with 3.5AA spacing ( amphipathic ) –  -helix WNNWFNNFNNWNNNF Gaps/indels –Probably surface not core MSA improves 2ndary structure (  -helix  -sheet) prediction by >6%)

28. Conserved residues W,F,Y large hydrophobic, internal/core –conserved WFY best signal for domains G,P turns, can mark end of  -helix  -sheet C conserved with reliable spacing speaks C-C disulphide bridges - defensins H,S often catalytic sites in proteases (and other enzymes) KRDE charged: ligand binding or salt-bridge L very common AA but not conserved –except in Leucine zipper L234567L234567L234567L

29. Finish with an alignment: defensins 3 pairs of C residues: 3 disulphide bridges