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

1. The 3rd Annual ROC Meeting – Madison, WI May 28-29, 2007 RAGPOOLS: RNA-As-Graph-PoolsA Web Server to Assist the Design of Structured RNA Pools for In-Vitro Selection Namhee Kim Laboratory of Prof. Tamar Schlick New York University 1. RNA Pool Design for In Vitro Selection 2. Modeling of Pool Synthesis 3. Features of RAGPOOLS 4. Conclusions

2. 1.1. In Vitro 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)

3. 1.2.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 J. Gevertz et al., RNA 11:853 (2005) J. Carothers et al., J. Am. Chem. Soc. 126:5130 (2004) ProposalDesign better pools by mixing base composition to target novel structures N. Kim, H.H. Gan, and T. Schlick, RNA 13:478 (2007)

4. 40% 20% 10% 30% A 20% 30% 40% 10% U 30% 10% 20% 20% G 10% 40% 30% 40% C 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., instead of 25% 25% 25% 25% A 25% 25% 25% 25% U 25% 25% 25% 25% G 25% 25% 25% 25% C

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 RAGPOOLS: RNA-As-Graph-PoolsWeb Server http://rubin2.biomath.nyu.edu N. Kim, J. S. Shin, S. Elmetwaly, H.H. Gan, and T. Schlick, submitted (2007)

9. Examples of Structured Pools

13. 4. Conclusions • 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

14. Acknowledgments • 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

15. MAA=MCC=MGG=MUU (A:1-6) Mixing Matrix M motivated by biological mutations MCC=MGG (B:7-10) A C G U MAA MAC MAG MAU MCA MCC MCG MCU MGA MGC MGG MGU MUA MUC MUG MUU A C G U MAA=MUU (C:11-14) MAC=MUG (D:15-18) MCA=MGU (E:19-22) Mixing Matrix Motivated by Biological Mutations

16. Starting Sequences and Coverage of Sequence Space Starting sequences (a) 51 motif (e) 42 motif

17. Motif Distributions (e) 42 motif and Matrices1-22

18. GTP 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)