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Multiple Sequence Alignment (MSA) and Phylogeny

Multiple Sequence Alignment (MSA) and Phylogeny . MSA input: multiple sequence Fasta file.

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Multiple Sequence Alignment (MSA) and Phylogeny

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  1. Multiple Sequence Alignment (MSA)andPhylogeny

  2. MSA input: multiple sequence Fasta file >gi|10835167|ref|NP_000607.1| CD4 antigen precursor [Homo sapiens] MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLT KGPSKLNDRADSRRSLWDQGNFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLT LTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSI VYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPL HLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAK VSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCV RCRHRRRQAERMSQIKRLLSEKKTCQCPHRFQKTCSPI >gi|57113961|ref|NP_001009043.1| CD4 antigen [Pan troglodytes] MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQTKILGNQGSFLT KGPSKLNDRVDSRRSLWDQGNFTLIIKNLKIEDSDTYICEVGDQKEEVQLLVFGLTANSDTHLLQGQSLT LTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSI VYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPL HLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAK VSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCV RCRHRRRQAQRMSQIKRLLSEKKTCQCPHRFQKTCSPI >gi|50054438|ref|NP_001001908.1| CD4 antigen [Sus scrofa] MDPGTSLRHLFLVLQLAMLPAASGTQEKYLVLGKAGDLAELPCHSSQKKNLPFNWKNSNQTKILGGHGSF WHTASVTELTSRLDSKKNMWDHGSFPLIIKNLEVTDSGIYICEVEDKRIEVQLLVFRLTASVTRVLLGQS LTLTLEGPSGSHPTVQWKGPGNKSKNDVKSLLLPQVGLEDSGLWTCTVSQDQKTLVFRSNIFVLAFQKVP STVYVKEGDQVALSFPLTFEAESLSGELMWRQTKGASSPQSWITFSLKDRKVTVQKSLQNLKLRMAEKLP LQITLLQALPQYAGSGNLTLVLPEGRLHREVNLVVMRATQSKNEVTCEVLGPTPPKVVLSLKLGNQSMKV SDQQKLVTVLDPEAGMWRCLLRDKDKVLLESQVEVLPTAFTRAWPELLASVIGGIIGLLFLAGFCIACVK CWHRRRRAERMSQIKRLLSEKKTCQCAHRQQKNYSLT >gi|6978631|ref|NP_036837.1| Cd4 molecule [Rattus norvegicus] MCRGFSFRHLLPLLLLQLSKLLVVTQGKTVVLGKEGGSAELPCESTSRRSASFAWKSSDQKTILGYKNKL LIKGSLELYSRFDSRKNAWERGSFPLIINKLRMEDSQTYVCELENKKEEVELWVFRVTFNPGTRLLQGQS LTLILDSNPKVSDPPIECKHKSSNIVKDSKAFSTHSLRIQDSGIWNCTVTLNQKKHSFDMKLSVLGFAST SITAYKSEGESAEFSFPLNLGEESLQGELRWKAEKAPSSQSWITFSLKNQKVSVQKSTSNPKFQLSETLP LTLQIPQVSLQFAGSGNLTLTLDRGILYQEVNLVVMKVTQPDSNTLTCEVMGPTSPKMRLILKQENQEAR VSRQEKVIQVQAPEAGVWQCLLSEGEEVKMDSKIQVLSKGLNQTMFLAVVLGSAFSFLVFTGLCILFCVR CRHQQRQAARMSQIKRLLSEKKTCQCSHRMQKSHNLI

  3. Clustal X

  4. Step1: Load the sequences

  5. Uploaded sequences A little unclear…

  6. Edit Fasta headers… >Homo_sapiens_CD4 <gi|10835167|ref|NP_000607.1| CD4 antigen precursor [Homo sapiens] MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLT KGPSKLNDRADSRRSLWDQGNFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLT LTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSI VYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPL HLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAK VSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCV RCRHRRRQAERMSQIKRLLSEKKTCQCPHRFQKTCSPI MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQTKILGNQGSFLT KGPSKLNDRVDSRRSLWDQGNFTLIIKNLKIEDSDTYICEVGDQKEEVQLLVFGLTANSDTHLLQGQSLT LTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSI VYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPL HLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAK VSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCV RCRHRRRQAQRMSQIKRLLSEKKTCQCPHRFQKTCSPI MDPGTSLRHLFLVLQLAMLPAASGTQEKYLVLGKAGDLAELPCHSSQKKNLPFNWKNSNQTKILGGHGSF WHTASVTELTSRLDSKKNMWDHGSFPLIIKNLEVTDSGIYICEVEDKRIEVQLLVFRLTASVTRVLLGQS LTLTLEGPSGSHPTVQWKGPGNKSKNDVKSLLLPQVGLEDSGLWTCTVSQDQKTLVFRSNIFVLAFQKVP STVYVKEGDQVALSFPLTFEAESLSGELMWRQTKGASSPQSWITFSLKDRKVTVQKSLQNLKLRMAEKLP LQITLLQALPQYAGSGNLTLVLPEGRLHREVNLVVMRATQSKNEVTCEVLGPTPPKVVLSLKLGNQSMKV SDQQKLVTVLDPEAGMWRCLLRDKDKVLLESQVEVLPTAFTRAWPELLASVIGGIIGLLFLAGFCIACVK CWHRRRRAERMSQIKRLLSEKKTCQCAHRQQKNYSLT MCRGFSFRHLLPLLLLQLSKLLVVTQGKTVVLGKEGGSAELPCESTSRRSASFAWKSSDQKTILGYKNKL LIKGSLELYSRFDSRKNAWERGSFPLIINKLRMEDSQTYVCELENKKEEVELWVFRVTFNPGTRLLQGQS LTLILDSNPKVSDPPIECKHKSSNIVKDSKAFSTHSLRIQDSGIWNCTVTLNQKKHSFDMKLSVLGFAST SITAYKSEGESAEFSFPLNLGEESLQGELRWKAEKAPSSQSWITFSLKNQKVSVQKSTSNPKFQLSETLP LTLQIPQVSLQFAGSGNLTLTLDRGILYQEVNLVVMKVTQPDSNTLTCEVMGPTSPKMRLILKQENQEAR VSRQEKVIQVQAPEAGVWQCLLSEGEEVKMDSKIQVLSKGLNQTMFLAVVLGSAFSFLVFTGLCILFCVR CRHQQRQAARMSQIKRLLSEKKTCQCSHRMQKSHNLI >Pan_troglodytes_CD4 >gi|57113961|ref|NP_001009043.1| CD4 antigen [Pan troglodytes] >Sus_scrofa_CD4 >gi|50054438|ref|NP_001001908.1| CD4 antigen [Sus scrofa] >gi|6978631|ref|NP_036837.1| Cd4 molecule [Rattus norvegicus] >Rattus_norvegicus_CD4

  7. Uploaded sequences Much better

  8. Step2: Perform alignment

  9. Multiple Sequence Alignment and conservation view

  10. Step 3: Create tree

  11. The Newick tree format is used to represent trees as strings A C B D In Newick format: ((A,C),(B,D)); • Each pair of parenthesis () encloses a clade in the tree • A comma “,” separates the members of the corresponding clade • A semicolon “;” is always the last character

  12. Step 4: View tree with NJPlot Note: unrooted tree

  13. Step 4.5: defining an outgroup

  14. Swapping nodes Note: The order inside a split doesn’t matter

  15. Bootstrap

  16. Bootstrap A. Resample (100-1000 time). 12345 100 1 : ATCTG…A 2 : ATCTG…C 3 : ACTTA…C 4 : ACCTA…T 12345 100 1 : AATTT…T 2 : AATTT…G 3 : AACTT…T 4 : AACTT…T 11244 x 12345 100 1 : TTTAT…T 2 : TAACC…G 3 : TAACC…T 4 : TGGGA…T 47789…x 12345 100 1 : AGGTA…T 2 : AGGAC…G 3 : AAAAC…A 4 : AAAGG…C 15578… x

  17. Sp1 Sp2 Sp3 Sp4 Bootstrap B. Reconstruct a tree from each data set. 12345 100 1 : AATTT…T 2 : AATTT…G 3 : AACTT…T 4 : AACTT…T 11244 x 12345 100 1 : TTTAT…T 2 : TAACC…G 3 : TAACC…T 4 : TGGGA…T 47789…x 12345 100 1 : AGGTA…T 2 : AGGAC…G 3 : AAAAC…A 4 : AAAGG…C 15578… x Sp1 Sp1 Sp2 Sp2 Sp3 Sp3 Sp4 Sp4

  18. Sp1 Sp2 Sp3 Sp4 Bootstrap C. We compute the majority rule consensus. Sp1 Sp1 Sp2 Sp2 Sp3 Sp3 Sp4 Sp4 In 67% of the data sets, the split between SP1+SP2 and the rest of the tree was found. 67% Sp1 100% Sp2 Sp3 Sp4

  19. Step 3.5 - Bootstrap

  20. Bootstrap values on NJPlot Note:ClustalX saves trees with .ph extension. Trees with bootstrap are saved with .phb extension

  21. Detecting selection forces using phlogeny (ConSeq, ConSurf, Selecton)

  22. “Important”sites evolve slower than “unimportant” ones.

  23. Conservation = functional/structural importance

  24. Use Phylogenetic information

  25. http://conseq.tau.ac.ilSite-specific rate computation tool

  26. Using ConSeq

  27. ConSeq results

  28. ConSeq conservation scores

  29. Conservation scores: • The scores are standardized: the average score for all residues is zero, and the standard deviation is one • The lowest score represents the most conserved site in the protein • negative values: slowly evolving (= low evolutionary rate), conserved sites • The highest score represents the most variable site in the protein • positive values: rapidly evolving (= fast evolutionary rate), variable sites • Scores are relative to the protein. Scores of different proteins are incomparable !!!

  30. ConSeq results

  31. Color-coded results

  32. Conservation in the structure Protein core:structurally constrained - usually conserved Active site: functionally constrained - usually conserved Surface loops: tolerant to mutations - usually variable Active site Surface loops Hydrophobic core

  33. Color-coded results

  34. http://consurf.tau.ac.il Same algorithm as ConSeq, but here the results are projected onto the 3D structure of the protein

  35. Using ConSurf

  36. ConSurf results

  37. First Glance in Jmol visual presentation

  38. ConSeq/ConSurf user intervention(advanced options) • Choosing the method for calculating the amino-acid conservation scores: (Bayesian/Max Likelihood) • Entering your own MSA file • Performing the MSA using: (MUSCLE/CLUSTALW) • Collecting the homologs from: (SWISS-PROT/UniProt) • Max. number of homologs: (50) • No. of PSI-BLAST iterations: (1) • PSI-BLAST 3-value cutoff: (0.001) • Model of substitution for proteins: (JTT/Dayhoff/mtREV/cpREV/WAG) • Entering your own PDB file • Entering your own TREE file

  39. Solution – look at the DNA Adaptive evolution = Positive selectionNon-syn > Syn Purifying selectionSyn > Non-syn NeutralselectionSyn = Non-syn

  40. Ka/Ks also known as… (or dn/ds, or ω) Selection score (Ka/Ks) < 1 purifying selection Selection score (Ka/Ks) > 1 positive selection Selection score (Ka/Ks) = 1 no selection Ka Ks Non-synonymous mutation rate Synonymous mutation rate

  41. http://selecton.tau.ac.il

  42. Selecton input Codon-level sequences !!! • The user must provide the sequences – no psi-blast option • Coding sequences • Only ORF • No stop codons • If an MSA is provided it must be codon aligned(RevTrans)

  43. Comparing H0 and H1 in Selecton H1 H0

  44. > 0.05 (a) accept H0 < 0.05 (a) reject H0 P-value Solution: statistics helps us to compare between hypotheses • H0: There’s no positive selection • H1: There is positive selection • H0: compute the probability (likelihood) of the data using a model that does not account for positive selection • H1: compute the probability (likelihood) of the data using a model that does account for positive selection • Perform a statistical test to accept or reject H0 (likelihood ratio test)

  45. Comparing H0 and H1 in Selecton

  46. Comparing H0 and H1 in Selecton

  47. Selecton results:

  48. Results Human rhesus swaps at sites 332, 335-340 confer human resistance to HIV and rhesus resistance to SIV

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