RRAGAGPPOOLSOOLS: : RNA-As-Graph-Pools RNA-As-Graph-Pools

A Web Server to Assist the Design of Structured A Web Server to Assist the Design of Structured RNA Pools for RNA Pools for In-VitroIn-Vitro Selection Selection

The 3rd Annual ROC Meeting – Madison, WI

May 28-29, 2007

1. RNA Pool Design for In Vitro Selection

2. Modeling of Pool Synthesis

3. Features of RAGPOOLS

4. Conclusions

Namhee Kim

Laboratory of Prof. Tamar Schlick

New York University

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1.1. 1.1. In VitroIn Vitro Selection Selection• An experimental approach to screen large (~1015) random- sequence libraries of RNAs for a specific function (e.g., binding property)

• Numerous aptamers and ribozymes were discovered from in vitro selection

D. Wilson and J.W. Szostak, Annu.Rev.Biochem 68:611 (1999)

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1.2.1.2.Targeted RNA Pool DesignTargeted RNA Pool Design– Already an

experimental goal J.H. Davis and J.W. Szostak, Proc. Natl. Acad. Sci. 99:11616 (2002) M.W. Lau, K.E. Cadieux, and P.J. Unrau, J. Am. Chem. Soc. 126:15686 (2004)

– Random pools are biased to simple topologies

– Complex structures are more active

N. Kim, H.H. Gan, and T. Schlick, RNA 13:478 (2007)

Proposal Design better pools by mixing base composition to target novel structures

J. Gevertz et al., RNA 11:853 (2005)

J. Carothers et al., J. Am. Chem. Soc. 126:5130 (2004)

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2. 2. Modeling of Pool SynthesisModeling of Pool Synthesis– By optimizing compositions of A, U, C and G in four containers (Mixing Matrix) and starting sequence, we seek to design pools with target topologies

e.g.,

40% 20% 10% 30% A20% 30% 40% 10% U30% 10% 20% 20% G10% 40% 30% 40% C

instead of

25% 25% 25% 25% A25% 25% 25% 25% U25% 25% 25% 25% G25% 25% 25% 25% C

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3.3. Algorithm for Structured Pool Design Algorithm for Structured Pool Design Step 1. Specify a target distribution of topologies/shapes

Step 2. Define candidates for starting sequences and mixing matrices that aim to cover the sequence space

Step 3. Compute motif distributions corresponding to all starting sequence/mixing matrix pairs

Step 4. Choose the number of mixing matrices to approximate the designed pool

Step 5. Find an optimal combination of starting sequences and mixing matrices and associated weights to approximate the target RNA motif distribution

RAGPOOLS: RNA-As-Graph-Pools Web Server http://rubin2.biomath.nyu.edu

N. Kim, J. S. Shin, S. Elmetwaly, H.H. Gan, and T. Schlick, submitted (2007)

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Examples of Structured PoolsExamples of Structured PoolsInput Output

Target structure

distributions

Number of mixing matrices

Starting

sequences

Optimized associated weights, mixing matrices and starting

sequences

41, 42:

30%, 30%

2

(conservation of C

and G)

All 78%, MM13, modified GTP aptamer 22%, MM12, Hammerhead ribozyme

51, 52, 53:

20%, 20%, 20%

3 All 12%, MM1, 70S 83%, MMT12, tRNA 5%, MMT4, DsrA ncRNA

51, 61:

30%, 30%

2 80-100 nt 38.5%, MM3, tRNA 61.5%, MMT8, let-7 ncRNA

52, 62:

20%, 20%

2 Riboswitch 77%, MM19, TPP riboswitch 23%, MMT4, TPP riboswitch

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4. 4. ConclusionsConclusions

The RAGPOOLS offers a general tool for designing and analyzing structured RNA pools with specified target motif distributions

In the near future, we expect to expand the set of starting sequences and mixing matrices and provide more detailed analyses of local structural properties

Contact us at: ragpools@biomath.nyu.edu

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AcknowledgmentsAcknowledgments

Prof. Tamar Schlick Dr. Hin Hark Gan Jin Sup Shin Shereef Elmetwaly All members of the Schlick Lab

● NYU McCracken fellowship and IGERT NSF fellowship

● NSF, NIH and HFSP

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Mixing Matrix Motivated by Mixing Matrix Motivated by Biological MutationsBiological Mutations

MAA=MCC=MGG=MUU

(A:1-6)

MCC=MGG

(B:7-10)

MAA=MUU

(C:11-14)

MAC=MUG

(D:15-18)

MCA=MGU

(E:19-22)

MAA MAC MAG MAU

MCA MCC MCG MCU

MGA MGC MGG MGU

MUA MUC MUG MUU

Mixing Matrix M motivated by

biological mutations

A C G U

A

C

G

U

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Starting Sequences and Coverage Starting Sequences and Coverage of Sequence Space of Sequence Space

Starting sequences (a) 51 motif

(e) 42 motif

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Motif DistributionsMotif Distributions (e) 42 motif and Matrices1-22

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GTP Aptamer PoolGTP Aptamer Pool

– Complex 52 and 42 motifs are targeted

– Sequence/structure contour plot of the designed pools is different from random pool

– Targeted structured pool depends on targeted function

J. Carothers et al., J. Am. Chem. Soc. 126:5130 (2004)