R AG P OOLS : RNA-As-Graph-Pools A Web Server to Assist the Design of Structured RNA Pools for In-Vitro Selection R AG P OOLS : RNA-As-Graph-Pools A Web.

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Presentation transcript:

R AG P OOLS : RNA-As-Graph-Pools A Web Server to Assist the Design of Structured RNA Pools for In-Vitro Selection R AG P OOLS : RNA-As-Graph-Pools A Web Server to Assist the Design of Structured RNA Pools for In-Vitro Selection The 3rd Annual ROC Meeting – Madison, WI May 28-29, RNA Pool Design for In Vitro Selection 2. Modeling of Pool Synthesis 3. Features of R AG P OOLS 4. Conclusions Namhee Kim Laboratory of Prof. Tamar Schlick New York University

NYU/BIOMATH In Vitro Selection An experimental approach to screen large (~10 15 ) 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)

NYU/BIOMATH Targeted 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)

NYU/BIOMATH 4 2. Modeling 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% A 20% 30% 40% 10% U 30% 10% 20% 20% G 10% 40% 30% 40% C instead of 25% 25% 25% 25% A 25% 25% 25% 25% U 25% 25% 25% 25% G 25% 25% 25% 25% C

NYU/BIOMATH 5 3. 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 R AG P OOLS : RNA-As-Graph-Pools Web Server N. Kim, J. S. Shin, S. Elmetwaly, H.H. Gan, and T. Schlick, submitted (2007)

NYU/BIOMATH 6

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9 Examples of Structured Pools Examples of Structured Pools InputOutput Target structure distributions Number of mixing matrices Starting sequences Optimized associated weights, mixing matrices and starting sequences 4 1, 4 2 : 30%, 30% 2 (conservati on of C and G) All78%, MM13, modified GTP aptamer 22%, MM12, Hammerhead ribozyme 5 1, 5 2, 5 3 : 20%, 20%, 20% 3All12%, MM1, 70S 83%, MMT12, tRNA 5%, MMT4, DsrA ncRNA 5 1, 6 1 : 30%, 30% nt38.5%, MM3, tRNA 61.5%, MMT8, let-7 ncRNA 5 2, 6 2 : 20%, 20% 2Riboswitch77%, MM19, TPP riboswitch 23%, MMT4, TPP riboswitch

NYU/BIOMATH 10

NYU/BIOMATH 11

NYU/BIOMATH 12

NYU/BIOMATH Conclusions wThe R AG P OOLS offers a general tool for designing and analyzing structured RNA pools with specified target motif distributions wIn the near future, we expect to expand the set of starting sequences and mixing matrices and provide more detailed analyses of local structural properties wContact us at:

NYU/BIOMATH 14 Acknowledgments wProf. Tamar Schlick wDr. Hin Hark Gan wJin Sup Shin wShereef Elmetwaly wAll members of the Schlick Lab ●NYU McCracken fellowship and IGERT NSF fellowship ●NSF, NIH and HFSP

NYU/BIOMATH 15 Mixing Matrix Motivated by Biological Mutations M AA =M CC =M GG =M UU (A:1-6) M CC =M GG (B:7-10) M AA =M UU (C:11-14) M AC =M UG (D:15-18) M CA =M GU (E:19-22) M AA M AC M AG M AU M CA M CC M CG M CU M GA M GC M GG M GU M UA M UC M UG M UU Mixing Matrix M motivated by biological mutations A C G U ACGUACGU

NYU/BIOMATH 16 Starting Sequences and Coverage of Sequence Space Starting sequences (a) 5 1 motif (e) 4 2 motif

NYU/BIOMATH 17 Motif Distributions (e) 4 2 motif and Matrices1-22

NYU/BIOMATH 18 GTP Aptamer Pool GTP Aptamer Pool –Complex 5 2 and 4 2 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)