1. Multiple Sequence Alignments

2. Multiple Alignments Generating multiple alignments  Web servers Analyzing a multiple alignment  what makes a ‘good’ multiple alignment?  what can it tell us, why is it useful? Adjusting a multiple alignment  Alignment editors and HowTo  Demonstration and practice

3. What is a Multiple Alignment? A comparison of sequences  “multiple sequence alignment” A comparison of equivalents:  Structurally equivalent positions  Functionally equivalent residues  Secondary structure elements  Hydrophobic regions, polar residues

4. Generating multiple alignments Pairwise sequence alignment is easy with sufficiently closely related sequences. Below a certain level of identity sequence alignment may become uncertain :  twilight zone for aa sequences ~ 30%. In or below the twilight zone it is good to make use of additional information, eg, from evolution. A multiple alignment of diverse sequences is more informative than a pairwise alignment:  residues conserved over longer period of time are under stronger evolutionary constraints.

5. Multiple Sequence Alignments Algorithms Multiple sequence alignment uses heuristic methods only:  With dynamic programming, computational time quickly explodes as the number of sequences increases. Different methods/algorithms:  Segment-based (DiAlign, …).  Iterative (HMMs, DiAlign, PRRP, …).  Progressive (Clustalw, T-Coffee, MUSCLE, …).

6. Progressive Alignment Step1: Calculate all pairwise alignments and calculate distances for all pairs of sequences. Step 2: Construct guide tree joining the most similar sequences using Neighbour Joining. ABCDE B2 C44 D666 E6664 F88888 Step 1Step 2

7. Progressive Alignment Step 3: From the tree assign weights for each sequence :  We want to down-weight nearly identical sequences and up-weight the most divergent ones. Step 4: Align sequences, starting at the leaves of the guide tree:  Pairwise comparisons as well as comparison of single sequence with a group of sequences (Profile) Caveat: errors introduced early cannot be corrected by subsequent information

8. Web servers ClustalW: http://www.ebi.ac.uk/Tools/clustalw2/ T-Coffee: http://www.ebi.ac.uk/Tools/t-coffee/http://www.ebi.ac.uk/Tools/t-coffee/ MUSCLE: http://www.ebi.ac.uk/Tools/muscle/ http://www.ebi.ac.uk/Tools/muscle/ DiAlign: http://dialign.gobics.de/ http://dialign.gobics.de/... and more at http://helix.nih.gov/apps/bioinfo/msa.html. http://helix.nih.gov/apps/bioinfo/msa.html

9. Clustalw features Amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Insertions and deletions are more common in loop regions than in the core of the protein!

10. T-Coffee features More accurate than ClustalW Instead of amino acid substitution matrices, uses consistency in a library of pairwise alignments i j Vertices represent positions in protein sequence. Edges represent pairwise alignments between protein sequences. If residues I and J have many common neighbours, their consistency is high.

11. MUSCLE Fast implementation Sometimes more accurate than ClustalW or T-Coffee

12. Example Let’s build a multiple alignment for the following sequences : sequences >query MKNTLLKLGVCVSLLGITPFVSTISSVQAERTVEHKVIKNETGTISISQLNKNVW VHTELGYFSGEAVPSNGLVLNTSKGLVLVDSSWDDKLTKELIEMVEKKFKKRV TDVIITHAHADRIGGMKTLKERGIKAHSTALTAELAKKNGYEEPLGDLQSVTNLK FGNMKVETFYPGKGHTEDNIVVWLPQYQILAGGCLVKSASSKDLGNVADAYV NEWSTSIENVLKRYGNINLVVPGHGEVGDRGLLLHTLDLLK >gi|2984094 MGGFLFFFLLVLFSFSSEYPKHVKETLRKITDRIYGVFGVYEQVSYENRGFISNAY FYVADDGVLVVDALSTYKLGKELIESIRSVTNKPIRFLVVTHYHTDHFYGAKAFR EVGAEVIAHEWAFDYISQPSSYNFFLARKKILKEHLEGTELTPPTITLTKNLNVYLQ VGKEYKRFEVLHLCRAHTNGDIVVWIPDEKVLFSGDIVFDGRLPFLGSGNSRTWL VCLDEILKMKPRILLPGHGEALIGEKKIKEAVSWTRKYIKDLRETIRKLYEEGCDVE CVRERINEELIKIDPSYAQVPVFFNVNPVNAYYVYFEIENEILMGE >gi|115023|sp|P10425| MKKNTLLKVGLCVSLLGTTQFVSTISSVQASQKVEQIVIKNETGTISISQLNKNVW VHTELGYFNGEAVPSNGLVLNTSKGLVLVDSSWDNKLTKELIEMVEKKFQKRVTD VIITHAHADRIGGITALKERGIKAHSTALTAELAKKSGYEEPLGDLQTVTNLKFGNTK VETFYPGKGHTEDNIVVWLPQYQILAGGCLVKSAEAKNLGNVADAYVNEWSTSIE NMLKRYRNINLVVPGHGKVGDKGLLLHTLDLLK >gi|115030|sp|P25910| MKTVFILISMLFPVAVMAQKSVKISDDISITQLSDKVYTYVSLAEIEGWGMVPSNGM IVINNHQAALLDTPINDAQTEMLVNWVTDSLHAKVTTFIPNHWHGDCIGGLGYLQR KGVQSYANQMTIDLAKEKGLPVPEHGFTDSLTVSLDGMPLQCYYLGGGHATDNIV VWLPTENILFGGCMLKDNQATSIGNISDADVTAWPKTLDKVKAKFPSARYVVPGH GDYGGTELIEHTKQIVNQYIESTSKP >gi|282554|pir||S25844 MTVEVREVAEGVYAYEQAPGGWCVSNAGIVVGGDGALVVDTLSTIPRARRLAEWV DKLAAGPGRTVVNTHFHGDHAFGNQVFAPGTRIIAHEDMRSAMVTTGLALTGLWP RVDWGEIELRPPNVTFRDRLTLHVGERQVELICVGPAHTDHDVVVWLPEERVLFAGD VVMSGVTPFALFGSVAGTLAALDRLAELEPEVVVGGHGPVAGP EVIDANRDYLRWV QRLAADAVDRRLTPLQAARRADLGAFAGLLDAERLVANLHRAHEELLGGHVRDAM EIFAELVAYNGGQLPTCLA

13. ClustalW at EBI Many options:  CPU mode,  full/fast alignment,  window length in fast mode,  …  gap penalties.

14. ClustalW at EBI Automatic display of:  Score table  Alignment (optional colouring)  Tree guide Link to Jalview alignment editor!

15. A note on the example It is atypical:  It uses only three sequences.  One should use more in order to extract reliable informations. It illustrates a common mistake:  It uses too closely related sequences.  One should use as divergent and diverse sequences as possible in order to extract relevant informations.

16. A Good Multiple Alignment? Difficult to define… Good ones look pretty!  Aligned secondary structures  Strongly conserved residues / regions  Comparison with known structure helps Bad ones look chaotic and random.

17. A Good Multiple Alignment? ☻ ? conservation quality consensus

18. Multiple Alignment Features Barton (1993)  “The position of insertions and deletions suggests regions where surface loops exist…

19. Multiple Alignment Features

20. Barton (1993)  “The position of insertions and deletions suggests regions where surface loops exist…  Conserved glycine or proline suggests a β -turn...

21. Multiple Alignment Features

22. Barton (1993)  “The position of insertions and deletions suggests regions where surface loops exist…  Conserved glycine or proline suggests a β -turn…  Residues with hydrophobic properties conserved at i, i+2, i+4 (etc) separated by unconserved or hydrophilic residues suggests a surface β-strand…

23. Multiple Alignment Features

24. Barton (1993)  “The position of insertions and deletions suggests regions where surface loops exist…  Conserved glycine or proline suggests a β -turn…  Residues with hydrophobic properties conserved at i, i+2, i+4 (etc) separated by unconserved or hydrophilic residues suggests a surface β -strand…  A short run of hydrophobic amino acids (4 or 5 residues) suggests a buried β -strand…

25. Multiple Alignment Features

26. Barton (1993)  Pairs of conserved hydrophobic amino acids separated by pairs of unconserved or hydrophilic residues suggests an α -helix with one face packed in the protein core. Similarly, an i, i+3, i+4, i+7 pattern of conserved residues.”

27. Multiple Alignment Features

28. Cysteine is a rare amino acid, and is often used in disulphide bonds ( pairs of conserved cysteines ) Charged residues ( histidine, aspartate, glutamate, lysine, arginine ) and other polar residues embedded in a conserved region indicate functional importance

29. Multiple Alignment Features

30. Quality Assessment Bad residues  Large distance from column consensus Bad columns  Average distance from consensus is high – “entropy” Bad regions  Profile scores Bad quality doesn’t always mean badly aligned! RINAIEVMAKLIQRINAIEVMAKLIQ LIMIILVEIVLAMLIMIILVEIVLAM PERMKIDQGQNMWPERMKIDQGQNMW DLVTWDYAASLDFDLVTWDYAASLDF DNPGGACRTTLIDDNPGGACRTTLID

31. Quality Assessment Profiles  A profile holds scores for each residue type (plus gaps) over every column of a multiple alignment  Concepts: Consensus sequence Amino acid similarity  Some multiple alignment programs use profiles to build or add to an alignment  Any alignment, or even one sequence, can be a profile (one sequence isn’t a very good one…)

32. What can we do with a multiple alignment? Identify subgroups (phylogeny)  Intra-group sequence conservation  Evolutionary relatedness (view tree) Identify motifs (functionality)  Evolutionary signals  Highly conserved residues indicate functional or structural significance! Widen search for related proteins  MA better than single sequence  Consensus sequence / profile useful RPDDWHLHLR GGIDTHVHFI GFTLTHEHIC PFVEPHIHLD PKVELHVHLD

33. What do we want to do? Build a homology model?  Accuracy Perform phylogenetic analysis?  Completeness Functional analysis of a protein family?  Diversity

34. Building the initial alignment Fetch related sequences and run alignment  Clustal, Dialign, TCoffee, Muscle … Fetch a multiple alignment from a database and add sequences of interest  Pfam, ProDom, ADDA … Start from a motif-finding procedure  MEME, Pratt, Gibbs Sampler … MEMEPrattGibbs Sampler

35. Adjusting the alignment 1.Filter alignment:  Remove any redundancy  Remove unrelated sequences  Remove unwanted domains  Recalculate alignment if necessary 2.Look for conserved motifs, adjust any misalignments. Try different colour schemes and thresholds. 3.One step at a time…

36. Jalview Alignment Editor Clamp, M., Cuff, J., Searle, S. M. and Barton, G. J. (2004), "The Jalview Java Alignment Editor", Bioinformatics, 20, 426-7.

37. Colouring your alignment HYDROPHOBIC / POLAR hydrophobic polar BURIED INDEX buried surface β-STRAND LIKELIHOOD probable unlikely HELIX LIKELIHOOD probable unlikely

38. Colouring your alignment By conservation thresholds:

39. Colouring your alignment Conservation index Amino Acid Property Classification Schema, eg: Livingstone & Barton 1993

40. Sequence Features

41. Check PDB Structures Load MA with sequence(s) for known PDB structure  View >> Feature Settings >> Fetch DAS Features (wait...) OR  Right-click >> Associate Structure with Sequence >> Discover PDB ids (quicker) Right-click sequence name >> View PDB Entry Structure opens in new window – residues acquire MA colours Highlight residues by hovering mouse over alignment or structure Label residues by clicking on structure

42. Compare Alignment to Structure

43. Crucial way of checking alignment! Where are gaps / insertions /deletions ?  In secondary structures: bad  In surface loops: okay Where are our key / functional residues?  Are they in probable active site?  Check they are clustered  Check they are accessible, not buried

44. Demonstration and Practice 1.Start Jalview (click here)click here 2.Tools >> Preferences >> Visual select Maximise Window, unselect Quality, set Font Size to 8 or 9, Colour >> Clustal, uncheck Open File Editing check Pad Gaps When Editing 3.File >> Input Alignment >> from URL (use this one)use this one 4.Get used to the controls – selecting and deselecting sequences/groups (drag mouse), dragging sequences/groups (use shift/ctrl), selecting sequence regions, hiding sequences/groups, removing columns and regions… Then explore menus and tools. 5.Now load this alignment – I’ve messed up a good alignment, and now I’d like you to correct it! There are two groups of sequences and one single sequence to adjust.this alignment

45. Demonstration and Practice 6.View >> Feature Settings >> DAS Settings  select Uniprot, dssp, cath, Pfam, PDBsum_ligands, PDBsum_DNAbinding, then click ‘Save as default’  click Fetch DAS Features (then click yes at prompt)...  Move mouse over alignment and read information about features  Move mouse over sequence names to check for PDB ids 7.Open a PDB structure (choose any) 8.View >> uncheck Show All Chains, then use up-arrow key to increase structure size. 9.Hover mouse over structure (see how residues are highlighted in the sequence), then do same for sequence. Select residues in the structure by clicking them – a label will appear. Click again to remove label. 10.Check position of insertions & deletions using this method.