3d-coffee mixing sequences and structures cédric notredame

3D-COFFEE Mixing Sequences and Structures

Cédric Notredame

chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: *

chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE * : .* . :

Potential Uses of A Multiple Sequence Alignment?

Extrapolation

Motifs/Patterns

Phylogeny

Profiles

Struc. PredictionMultiple Alignments Are CENTRAL to MOST Bioinformatics Techniques.

Why Is It Difficult To Compute A multiple Sequence Alignment?

A CROSSROAD PROBLEM

BIOLOGY:What is A Good

Alignment

COMPUTATIONWhat is THE Good

Alignment


Why Is It Difficult To Compute A multiple

Sequence Alignment ?

BIOLOGY

CIRCULAR PROBLEM....

GoodSequences

GoodAlignment

COMPUTATION

The T-Coffee Algorithm

Local Alignment Global Alignment

Extension

Multiple Sequence Alignment

Mixing Local and Global Alignments

What is a library?

Extension+T-Coffee

Library Based Multiple Sequence Alignment

2Seq1 MySeqSeq2 MyotherSeq#1 21 1 253 8 70….

3Seq1 anotherseqSeq2 atsecondoneSeq3 athirdone#1 21 1 25#1 33 8 70….

The Triplet Assumption

X

Y

Z

X

Y

SEQ A

SEQ B

Consistency Consensus

ClustalW T-Coffee

Dynamic Programming Using An Extended Library

Progressive Alignment

What Is BaliBaseHow Good is T-Coffee ???

Best Performing Method on MSA benchmark Datasets

BaliBase

-Notredame

-Sonhammer

Ribosomal RNA

-Katoh (Mafft)

Homstrad

-Notredame

OxBench

-Barton

Mixing Heterogenous Data With

T-Coffee



Multiple Alignment

StructuralSpecialist

Mixing Sequences and Structures

Why Do We Want To Mix Sequences and Structures?

1-Predicting Sequence Structures

STUCTURE FUNCTION


•Sequences are Cheap and Common.

•Structures are Expensive and Rare.


Cheapest Structure determination:

Sequence-Structure Alignment

THREADOr

ALIGN

ADKPRRP---LS-YMLWLNADKPKRPKPRLSAYMLWLN



THREADOr

ALIGN

Convincing Alignment

Same Fold


Convincing Alignment

Same Fold

Distant sequences are hard to align



Multiple Sequence Alignments Help

Exploring the Twilight Zone



2-Produce Better Alignments


ADKPRRP---LS-YMLWLNADKPKRPKPRLSAYMLWLNALIGN

Unreliable alignment if %ID <30%


Alignment Unsentitive to %ID


Struc.Superposition

Folds evolve Slower than Sequences


StructureSuperposition



2-Produce Better Alignments

How To Mix Sequences and

Structures

Mixing Heterogenous Data With

T-Coffee



Multiple Alignment

StructuralSpecialist

Struct Vs StructSeq Vs Struct

Thread

Evaluation on Homestrad

Superpose

Seq Vs SeqLocalGlobal

Mixing Sequences and Structures with T-Coffee

The 3D-Coffee LibrariesMethods

•Global: Needlman and Wunsch

•Local: Sim (lalign)

•Threading: Fugue

•Superposition:SAP

•Threading: Fugue

Fugue

•Threading: Fugue

Fugue

•Threading: Fugue

1-Turn Sequence into a profile:-lower penalties in loops-Structure specific matrix

2-Align Profile

withSequence

Evaluating Fugue

•Threading: Fugue

1-Select 967 pairs of sequences in HOMSTRAD

FUGUE T-Coffee2-Align each pair with T-Coffee and Fugue.

Compare

3-Compare the TwoAlignments

Fugue

•Threading: Fugue


2-Align each pair with T-Coffee and Fugue.

3-Compare the TwoAlignments TCdef wins

Fugue wins TCdef: 58.81%Fugue: 61.81%

Superposition:

SAP


1-High Level Dynamic Programming

Substitution Matrix when doing regular Alignments

2-Low Level DP.Forcing the aln of two residues



1

9

12131

8

14

53-Rigid Body Superposition

RMSD




1

9

1213

18

14


RMSD





2-Low Level DP.Evaluate Every Pair



Structure Based Sequence Alignment

Make a DP on the

accumulated traces

Use Traces like a

Substitution Matrix

SAP T- Coff ee

Compare


2-Align each pair with T-Coffee and SAP.




2-Align each pair with T-Coffee and SAP.



TCdef: 58.81%SAP: 86.31%

•SAP•Fugue

TCdef: 58.81%Fugue: 61.81%

TCdef: 58.81%Fugue: 86.31%

Sequences and Structures:

How Good is The Mixture ???

Our Benchmark:

HOM39

-HOMSTRAD: Structure based MSAs that can be used as References.

-COMPACT and DEMANDING

-HOM39: The 39 Most difficult datasets (percent ID lower than 25).

Our BenchMark:

Using HOM39

BENCHMARKING Strategy:

-re-align HOM39 without using ALL the structures

-Compare the result with the reference

Evaluating 3D-Coffee

1- Can a SINGLE structure Help ?

Seq Vs Struct

Thread

Evaluation on HOM39


Using ONE structure with3D-Coffee

HOM39 with ONE Structure per MSA



2- Does it benefit to ALL the Sequences

Is EVERYONE Happier if there is a STAR in the team…

BaliBase

HOM39 TC-Fugue

+

Remove Provided Structure(s)

Comparison



3- Can We Use Two or More Structures

2-Does it benefit to all the sequences

Seq Vs Struct

Fugue



Mixing Sequences and Structures with 3D-Coffee

HOM39 with TWO Structures/MSA

Struct Vs Struct

SAP, LSQ

Indirect Improvement

Direct Improvement



4-Relation Accuracy/ N-structures ???

2-Does it benefit to all the sequences

3-Can we use Two Structures

Seq Vs Struct

Fugue



Mixing Sequences and Structures with T-Coffee

HOM39 with 1-N Structures per MSA

Struct Vs Struct

SAP

Induced Improvement

Conclusion

-Structures Help

BUT NOT SO MUCH

The More Structures The Merrier

Credits

Orla O’Sullivan: University College, Cork, Ireland

Des Higgins: University College, Cork, Ireland

Karsten Suhre: IGS-CNRS, Marseille, France

Conclusion

The program is available on request from:

[email protected]

3d-coffee mixing sequences and structures cédric notredame

Documents

fold slide

convincing alignment

clustalwtcoffee slide

good alignment chite

tcoffee algorithm slide

multiple sequence alignments

dcoffee mixing sequences

fold distant sequences