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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.

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Sequence Homology Treat or Trick?

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  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

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