1 / 28

Sequence Homology Treat or Trick?

Sequence Homology Treat or Trick?. Fine Grain Structural Classification using the T-RMSD method Cedric Notredame Luis Serrano Cedrik Magis François Stricher Almer van der Slot. Same sequence Same structure. Same Sequence. Same Origin. Same Function. Same 3D fold.

quana
Download Presentation

Sequence Homology Treat or Trick?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Sequence Homology Treat or Trick? Fine Grain Structural Classification using the T-RMSD method Cedric Notredame Luis Serrano Cedrik Magis François Stricher Almer van der Slot

  2. Same sequence Same structure Same Sequence Same Origin Same Function Same 3D fold

  3. Same sequence Same structure ??? Prion protein PrP-c (normal) Prion protein PrP-sc (pathology) 100% Identical Sequence >P04156|23-230 / PRIO_HUMAN KKRPKPGGWNTGGSRYPGQGSPGGNRYPPQGGGGWGQPHGGGWGQPHGGGWGQPHGGGWGQPHGGGWGQGGGTHSQWNKPSKPKTNMKHMAGAAAAGAVVGGLGGYMLGSAMSRPIIHFGSDYEDRYYRENMHRYPNQVYYRPMDEYSNQNNFVHDCVNITIKQHTVTTTTKGENFTETDVKMMERVVEQMCITQYERESQAYYQRGS 100% Identical Sequence

  4. TNF Receptors (TNFRs) Receptor Ligand Intra Extra Aggarwal, BB. Nat RevImmunol. 2003 Sep;3(9):745-56.

  5. TNFRs: The Cystein Rich Domains (CRDs) Turn 1 Loop 1 Loop 2 Turn 2

  6. TNFR and CRD Collections Domain Databases UNIPROT 17 4 2 34 10 0 1 25 annotated notidentified 6 putativesnotidentified

  7. This suggests the CRDs are homogenous

  8. Is it supported by 3D Superposition ?

  9. Classifications • If they are so different and diverse • Can we classify them? • Can this classification bring functional information?

  10. Structurally? Not really…

  11. Phylogeny? Not quite there…

  12. Add-hoc? Maybe…

  13. Add-hoc? Maybe… Bodmer, JL., Schneider, P., Tschopp, J. Trends Biochem. Sci. 2002 Jan;27(1):19-26.

  14. Add-hoc? • Half Domains • Complex • Explains Little Maybe…

  15. A new Classification ? • Is it possible to design a new classification • Structure based • Functionally informative • Predictive for TNFRs without a known structure • Yes if we can compare structures in a more informative way

  16. The Standard Way: RMSD(Root Mean Square Deviation) • RMSD • Superpose the Structures • Measure The deviation D1 D2 Z X D3 Y W

  17. A Simpler Alternative: the iRMSD D2 D1 D1 D2

  18. UPGMA C A B D T-RMSD: Trees based on iRMSD Dd2 Dd1 A B C D P1 Distance Matrix P1 B

  19. C A B D T-RMSD: Trees based on iRMSD Dd1 Dd2 A B C D P1 A A C C C A B B B D D D Consensus Tree

  20. T-RMSD Vs Clustering

  21. T-RMSD Vs Clustering

  22. Does The Clustering Make Sense ?

  23. Well Conserved Inserts ?

  24. What Does the New Classification Predict ? Type I Type II Type III Outliers

  25. What Does the New Classification Predict ? Type I Type II Type III Outliers Nter CRDs (Pre Ligand Assembly Domains, PLAD) are involved in the complex formation

  26. Next ??? • New Classes ? • New Functions ? • New Structures ???

  27. Next ??? • Which Ligand • How To Align These things • MSA problem ???

  28. Summary • T-RMSD • Fine Grain Structural Classification • TNFRs/CRD • New typology • Structurally meaningful • Functionally informative • Predictive • T-RMSD is available for download and part of the T-Coffee package (www.tcoffee.org) • Collaborators • Cedrik Magis • François Stricher • Almer van der Slot • Luis Serrano • Cedric Notredame

More Related