1 Towards a model for -1 frameshift sites Alain Denise 1,2, Michaël Bekaert 1, Laure Bidou 1, Guillemette Duchateau-Nguyen 1, Jean-Paul Forest 2, Christine.

1

Towards a model for -1 frameshift sites

Alain Denise1,2, Michaël Bekaert1, Laure Bidou1, Guillemette Duchateau-Nguyen1,

Jean-Paul Forest2, Christine Froidevaux2,

Isabelle Hatin1, Jean-Pierre Rousset1, Michel Termier1

1 IGM (Institut de Génétique et Microbiologie)2 LRI (Laboratoire de Recherche en Informatique)

Université Paris-Sud, Orsay

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Translation

CAU AUG GAU UAC AUG GUC UAA GAU5’ 3’

mRNA

3

Translation

CAU AUG GAU UAC AUG GUC UAA GAU

The ribosome reads bases by triplets (or codons)from a START codon

ribosome

5’ 3’

4

Translation


The ribosome synthetizes one amino-acid per codon

5’ 3’

5

Translation


6

Translation


7

Translation


8

Translation


9

Translation


The synthesis goes on until a STOP codon is read

5’ 3’

1 mRNA gives 1 protein

10

Experimental fact

• Some mRNAs encode two distinct proteins with same 5’ end

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Programmed -1 frameshifting

Non-deterministic event

ORF1a

START0 STOP0

0 phase

STOP-1

ORF1b -1 phase

usualtranslation

-1 frameshift

1 mRNA gives 2 distinct proteinswith accurate ratio

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Typical -1 frameshift site [Brierley, 1989]

NNX XXY YYZ

AUG P SP

S1

L1

S2

L2

L’1

Slippery sequence Secondary structure

5’

3’

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IBV frameshift site

UAU UUA AAC

AUG

S1

S2

Slippery sequence Pseudoknot

5’

3’

GGGUAC

UGACGAUGGGG

GCUG AUACCCC

A G G C U C G

U C C G A G C

G

UUGC

GAAA

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Translation with frameshift

UAU UUA AAC GGG UAC

AUG

5’

3’

UGACGAUGGGG

GCUG AUACCCC

A G G C U C G

U C C G A G C

G

UUGC

GAAA

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UAU UUA AAC GGG UAC

5’

3’

UGACGAUGGGG

GCUG AUACCCC

A G G C U C G

U C C G A G C

G

UUGC

GAAA

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UAU UUA AAC GGG UAC

5’

3’

UGACGAUGGGG

GCUG AUACCCC

A G G C U C G

U C C G A G C

G

UUGC

GAAA

-1 shift

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UA UUU AAA CGG GUA CGG GGU AGC AGU


5’

3’

19



5’

3’

20



5’

3’

21



5’

3’

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Goals

To improve the known model for viral frameshift sites

To identify new frameshift sites in viral and non viral genomes

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Our approach

Biologicalsequences

Formalmodels

Predictiontools

In silicoand in vivo

validation

Applications toother genomes

representexplainpredict

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IBV frameshift site: spacer

5’

3’

GGGUAC

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Spacer consensus

HAST-1 UAC AAA

BEV UGU UG

EAV UGA GAG

HCV GAG UC

IBV GGG UAC

MHV GGG UU

TGEV GAG

RCNMV UAG GC

BWYV GGA GUG

PLRV GGG CAA

BLV UAA UAG A

FIV UGG AAG GC

HIV-1 GGG AAG AU

HTLV-2UCC UUA A

JSR UGG GUG A

MMTV gag-pro UUG UAA A

MMTV pro-pol UGA U

RSV UAG GGA

SRV-1 GGA CUG A

Consensus UGG UAG AGAA GUA

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Lab experiments

lacZ luc

-1 phase

pSV40 lacZ luc

0 phase

pSV40 FS signal

FS signal N

Test construct

Control construct

Expression reporter FS reporter

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Spacer: lab experiments

Spacer relative FS rate

wild-type IBV GGGUA 100U mutant UGGUA 100

A mutant AGGUA 55C mutant CGGUA 32CC mutant CCGUA 70CCU mutant CCUUA 49

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Refining the model: Machine learning

• To identify relevant properties that characterize FS sites

• Disjunctive learning: all sequences do not frameshift for the same reasons [Giedroc et al., 2000]

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Annotating data: spacer

5’

3’

GGGUAC

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Example of data: SP

• SP = GGGUAC

– number of A = 1; C = 1; G = 3; U = 1;

– % of A = 33; C = 33; G = 50; U = 33;

– first = G;

– last = C;

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Annotating data: stem 1

UGACGAUGGGG

GCUG AUACCCC

5’

3’

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Example of data: stem 1

• S1 =

– 5' side : GGGGUAGCAGU– 3' side : CCCCAUAGUCG

– stability : -20,7 kcal/mol

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Annotating data: full sequence

U UUA AAC

5’

3’

GGGUAC

UGACGAUGGGG

GCUG AUACCCC

A G G C U C G

U C C G A G C

G

UUGC

GAAA

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Example of data : FS rate

FS rate = 22 %

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GloBo

Disjunctive learning algorithm

Suited to small amount of data

Won the PTE challenge on analogous data

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Example of rulesIf

SP length 5 and number of G in S1.5’ bottom half 3 and

number of G in S1.5’ 4 and %T in S2.5’ 30 and%G in S2.5’ 70

then FS rate 5%

If %G in S1.5' bottom half 80 and %C in L1 45

then FS rate 5%

If

SP length 5 and S1.3' length 6 and %C in S1.3' 45

then FS rate 5%

...

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Covering and prediction

If

SP length 5 and number of G in S1.5’ bottom half 3 and

number of G in S1.5’ 4 and %T in S2.5’ 30 and%G in S2.5’ 70

then FS rate 5%

Covering of examples : 70 %

Examples predicted in test set : 80 %

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Is R1relevant for frameshift ?

Stem 1 5’-side relative FS R1 rate

wild-type IBV GGGGU AUCAGU 100 yesmutant 1 GGUCG AUCAGU 41 yesmutant 2 GGGGU UCUACA 55 yes

mutant 3 GCUCG AUCAGU 36 nomutant 4 GCCCU AUCAGU 73 no

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Covering and prediction

If

SP length 5 and S1.3' length 6 and %C in S1.3' 45

then FS rate 5%

Covering of examples : 45 %

Examples predicted in test set : 40 %

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Conclusion

• Spacer:– correlation between primary sequence and

FS rate has been established– systematic experimentation going on

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Conclusion

Biologicalsequences

Formalmodels

Predictiontools

In silicoand in vivo

validation

Applications toother genomes

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SpacerVirus Sequence

HAST-I : U A C A A ABEV : U G U U GEAV : U G A G A GHCV : G A G U CIBV : G G G U A CMHV : G G G U UTGEV : G A GRCNMV : U A G G CBWYV : G G A G U GPLRV : G G G C A ABLV : U A A U A G AFIV : U G G A A G G CHIV-1 : G G G A A G A UHTLV-II : U C C U U A AJSR : U G G G U G AMMTV : U U G U A A AMMTV : U G A URSV : U A G G G ASRV-1 : G G A C U G A

Consensus : U G G U A G AG A A G U A

1 Towards a model for -1 frameshift sites Alain Denise 1,2, Michaël Bekaert 1, Laure Bidou 1, Guillemette Duchateau-Nguyen 1, Jean-Paul Forest 2, Christine.

Documents

g g c u c g u c c g

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ggguac slide

shift slide

orsay slide

protein slide