r ag p ools : rna-as-graph-pools a web server to assist the design of structured rna pools for...
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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
NYU/BIOMATH
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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: [email protected]
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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