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Patterns and Profiles

Patterns and Profiles. Lisa Mullan, HGMP-RC. Terminology. Homologs Two proteins that share a common ancestor Usually similar functions Orthologs : different species Paralogs : same genome Analogs Two sequences that have NO common ancestor, but have similar functions. Protein

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Patterns and Profiles

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  1. Patterns and Profiles Lisa Mullan, HGMP-RC

  2. Terminology Homologs Two proteins that share a common ancestor • Usually similar functions • Orthologs : different species • Paralogs : same genome Analogs • Two sequences that have NO common • ancestor, but have similar functions. Protein • analogs may have the same fold.

  3. 7 10 Multiple sequence alignments CHERRIES CLEMENTIN-ES P-EAR--S GRE-ENAPPLES Most programs use “clustal” – a clustering algorithm

  4. 4 24 Multiple sequence alignments P-EARS----- GREENAPPLES CLEMENTINES CHERR--I-ES

  5. 0 24 Multiple sequence alignments GREENAPPLES CHERR---IES P-EARS----- CLEMENTINES

  6. GREENAPPLES CLEMENTINES CHERRIES PEARS GREENAPPLES CLEMENTINES CHERR---IES P-EARS----- Multiple sequence alignments (cont.)

  7. Multiple sequence alignments (cont.) CLUSTAL W (1.7) multiple sequence alignment Q40236/1-193 GTF-DQLQLVLRWPTSFCNGKNCKRTPKDFTIHGLWPDSEAGELNFCNPRASYTIVRHGTF Q40241/1-189 -----QLQLVLRWPTSFCNGKNCKRTPKDFTIHGLWPDSEAGELNFCNPRASYTIVRHGTF Q42513/1-193 GTF-NQLQLVLRWPASFCKGKKCERTPNNFTIHGLWPDIKGTILNNCNPDAKYASVTGGKF G255586/1-194 GAF-EYMQLVLQWPTAFCHTTPCKNIPSNFTIHGLWPDNVSTTLNFCGKEDDYNIIMDGP- Q40379/1-194 GAF-EYMQLVLQWPTTFCHTTPCKNIPSNFTIHGLWPDNVSTTLNFCGKEDDYNIIMDGP- :****:**::**: . *:. *.:********* . ** *. .* : * Q40236/1-193 EKRN---KHWPDLMRSKDNSMDNQEFWKHEYIKHGSCCTDLFNETQYFDLALVLKDRFDLLT Q40241/1-189 EKRN---KHWPDLMRSKDNSMDNQEFWKHEYIKHGSCCTDLFNETQYFDLALVLKDRFDLLT Q42513/1-193 VKRN---KHWPDLILTEAASLNSQGFWAYQFKKHGTCCSDLFNQEKYFDLALILKDKFDLLT G255586/1-194 EK-NGLYVRWPDLIREKADCMKTQNFWRREYIKHGTCCSEIYNQVQYFRLAMALKDKFDLLT Q40379/1-194 EK-NGLYVRWPDLIREKADCMKTQNFWRREYIKHGTCCSEIYNQVQYFRLAMALKDKFDLLT :** :****: : .:..* ** :: ***:**::::*: :** **: ***:***** Q40236/1-193 TFRIHGIVPRSSHTVDKIKKTIRSVTGVLPNLSCTKNMDLLEIGICFNREASKMIDCTRP Q40241/1-189 TFRIHGIVPRSSHTVDKIKKTIRSVTGVLPNLSCTKNMDLLEIGICFNREASKMIDCTRP Q42513/1-193 TFRNKGIIPKSTCTINKIQKTIRTVTGVVPNLSCTPTMELLEVGICFNRDASKLIDCDQP G255586/1-194 SLKNHGIIRGYKYTVQKINNTIKTVTKGYPNLSCTKGQELWEVGICFDSTAKNVIDCPNP Q40379/1-194 SLKNHGIIRGYKYTVQKINNTIKTVTKGYPNLSCTKGQELWEVGICFDSTAKNVIDCPNP ::: :**: . *::**::**::** ****** :* *:****: *.::*** .* Q40236/1-193 KTCNPGEDNLIGFP Q40241/1-189 KTCNPGEDNLIGFP Q42513/1-193 KTCDTSGNTEIFFP G255586/1-194 KTCKTASNQGIMFP Q40379/1-194 KTCKTASNQGIMFP ***... : * **

  8. Multiple sequence alignments (cont.) ( ( Q40236/1-193:-0.00066, Q40241/1-189:0.00066) :0.18460, Q42513/1-193:0.17928, ( G255586/1-194:0.00258, Q40379/1-194:0.00258) :0.32591);

  9. Motifs - assigned to the secondary structure of a protein E.coli trp repressor

  10. Leucine zipper motif L-X(6)-L-X(6)-L-X(6)-L

  11. http://bioinf.man.ac.uk/dbbrowser/PRINTS/ “A fingerprint is a group of conserved motifs used to characterise a protein family”

  12. Domains Many definitions – depends who you speak to! • Domains are discrete structural units • Defined by structure • Domain boundaries can be inferred from careful sequence analysis • Domains are the common currency of protein function

  13. But – there are slightly more glutamates than aspartates in the alignment! EFGHIVW EYAHMIW DYAHSLW EFGHPLW [ED]- [FY]- [GA]- H- X- [VIL]- W And could X be represented more accurately by {FYW}?

  14. EFGHIVW EYAHMIW DYAHSLW EFGHPLW So, let’s add some numbers to the problem! Positions One 15 5 0 0 0 0 0 0 0 0 0 0 0 0 Two 0 0 10 10 0 0 0 0 0 0 0 0 0 0 Three 0 0 0 0 0 10 10 0 0 0 0 0 0 0 Four 0 0 0 0 20 0 0 0 0 0 0 0 0 0 Five 2 2 -2 -2 2 2 2 2 2 2 2 2 2 -2 Six 0 0 0 0 0 0 0 5 0 0 0 10 5 0 Seven 0 0 0 0 0 0 0 0 0 0 0 0 0 20 E D F Y H G A I M S P L V W

  15. http://www.expasy.ch/prosite

  16. http://protein.toulouse.inra.fr/prodomCG.html

  17. http://www.ebi.ac.uk/interpro

  18. M 1.0 I .50 0.75 0.75 E .75 D .25 F .50 Y .50 S 1.0 V .25 I .25 L .50 X 1.0 1.0 1.0 0.25 1.0 0.25 H 1.0 1.0 W 1.0 But…….profiles do not support gaps…. EFH-IIVW EYH--MIW DYHSISLW EFH-IPLW Hidden Markov Models introduce statistics into profiles

  19. http://www.sanger.ac.uk/pfam

  20. Pfam-A • 2,216 Curated families with annotation. • Pfam-B • 40,000 families derived from Prodom.

  21. http://smart.embl-heidelberg.org.de/

  22. Four character ID code

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