Sequence homology treat or trick
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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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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


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


TNF Receptors (TNFRs)

Receptor

Ligand

Intra

Extra

Aggarwal, BB. Nat RevImmunol. 2003 Sep;3(9):745-56.


TNFRs: The Cystein Rich Domains (CRDs)

Turn

1

Loop

1

Loop

2

Turn

2


TNFR and CRD Collections

Domain Databases

UNIPROT

17

4

2

34

10

0

1

25 annotated

notidentified

6 putativesnotidentified


This suggests the CRDs are homogenous


Is it supported by 3D Superposition ?


Classifications

  • If they are so different and diverse

    • Can we classify them?

    • Can this classification bring functional information?


Structurally?

Not really…


Phylogeny?

Not quite there…


Add-hoc?

Maybe…


Add-hoc?

Maybe…

Bodmer, JL., Schneider, P., Tschopp, J. Trends Biochem. Sci. 2002 Jan;27(1):19-26.


Add-hoc?

  • Half Domains

  • Complex

  • Explains Little

Maybe…


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


The Standard Way: RMSD(Root Mean Square Deviation)

  • RMSD

    • Superpose the Structures

    • Measure The deviation

D1

D2

Z

X

D3

Y

W


A Simpler Alternative: the iRMSD

D2

D1

D1

D2


UPGMA

C

A

B

D

T-RMSD: Trees based on iRMSD

Dd2

Dd1

A

B

C

D

P1

Distance

Matrix

P1

B


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


T-RMSD Vs Clustering


T-RMSD Vs Clustering


Does The Clustering Make Sense ?


Well Conserved Inserts ?


What Does the New Classification Predict ?

Type I

Type II

Type III

Outliers


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


Next ???

  • New Classes ?

  • New Functions ?

  • New Structures

???


Next ???

  • Which Ligand

  • How To Align These things

    • MSA problem

???


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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