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Towards RNA structure prediction: 3D motif prediction and knowledge-based potential functions

Towards RNA structure prediction: 3D motif prediction and knowledge-based potential functions. Christian Laing Tamar Schlick’s lab Courant Institute of Mathematical Sciences Department of Chemistry New York University. RNA folding is hierarchical. Sequence. Secondary Structure.

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Towards RNA structure prediction: 3D motif prediction and knowledge-based potential functions

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  1. Towards RNA structure prediction: 3D motif prediction and knowledge-based potential functions Christian Laing Tamar Schlick’s lab Courant Institute of Mathematical SciencesDepartment of ChemistryNew York University

  2. RNA folding is hierarchical Sequence Secondary Structure Annotated diagram 3D Structure 5‘gGACUCGGGGUGCCCUUCUGCGUGAAGGCUGAGAAAUACCCGUAUCACCUGAUCUGGAUAAUGCCAGCGUAGGGAAGUUc3' TPP riboswitch (PDB: 2GDI) • Tertiary motifs serve as modular building blocks in the RNA architecture. • To understand the role of RNA tertiary motifs in RNA folding will help to understand RNA 3D prediction.

  3. Annotating 3D RNA • Selected seven key RNA tertiary motifs: coaxial helix, A-minor motif, ribose zipper, tetraloop-tetraloop receptor, pseudoknot, kissing hairpin, and tRNA D-loop:T-loop. • Searched RNA tertiary motifs via different computer programs • Annotate tertiary interaction motifs. • Perform analysis over the diagrams produced.

  4. Examples RNA junctions have a high probability (84%) to contain at least one coaxial helix.

  5. Distribution of tertiary motifs • For 54 high-resolution RNA structures, 615 RNA tertiary interactions were found. • Ribose zippers, coaxial helices and A-minor interactions are highly abundant (88%).

  6. Correlated motifs • Several A-minor motifs (64%) are involved with coaxial helices. • Coaxial helices (70%) interact with A-minor. • Most ribose zippers (70%) contain an A-minor. • Every loop-loop receptor contains a ribose zipper, which in turn contains one or more A-minor motifs 87% of the time. Group I intron (PDB: 1HR2) RNA V.5 1119 (2001)

  7. Can we predict coaxial stacking? HCV virus fragment (PDB: 1KH6) NAT.STRUCT.BIOL. V. 9 370 2002

  8. Topology of 4-way junctions in folded RNAs Analysis on 24 junctions: GUIDELINES • A coaxial staking Hi-Hi+1 is enhanced when Ji,i+1 is small. • There is a strong preference for a H1-H4 stacking. • Coaxial helices in family H have similar lengths. • Pseudoknots usually stack their helices.

  9. A-minor involved in long-range interactions

  10. Statistical Potentials for A-minor prediction • The adenosine (A) can be located in four single stranded regions: internal (I) and terminal (T) loops, junctions (J), and other (O). • The helix receptor (R) can be located in five positions relative to the end site of a helix. • The potential function for an A-minor is defined by: • is the observed number of interacting pairs • is the expected number of interacting pairs • The statistical free energy for each A-minor is:

  11. Improving coarse-grained model Rosetta: - 1-bead model (only the base). - Neglect sugar and phosphate. - Only one RNA (23S rRNA) was considered. Possible improvements: - 3-bead model to consider base, sugar, and phosphate. - Consider our 54-non-redundant RNA dataset. PNAS V. 104 No. 37 14664-9 (2007) PNAS V. 102 No. 19 6789-94 (2005)

  12. From 2D to 3D: RNA assemble Assume we know the RNA secondary structure (and possible some constrains), how can we use this knowledge into our Mesoscopic model? • Stems can be described by type-A helices. • Programs such as Assemble (Westhof) and RNA2D3D (Shapiro) can be used but require manual intervention.

  13. Suggested future directions • Design statistical potentials for coaxial stacking. • The combination of A-minor/coaxial helix prediction may lead to stronger arguments. • Design statistical potentials to predict non canonical basepairs (Leontis/Westhof), and explore the possibility to use them with dynamic programming. • Test the statistical potentials (threading, decoys). • There are 272 non-redundant 4-way junctions in the RNA junction database that can be analyzed topologically and geometrically.

  14. Acknowledgements Yurong Xin and Tamar Schlick Many thanks to: Hin Hark Gan Sean McGuffee Shereef Elmetwaly Human Frontier Science Program NSF/NIGMS initiative in Mathematical Biology (DMS-0201160)

  15. Topology of 3-way junctions in folded RNAs (Lescoute & Westhof RNA 2006 12: 83-93) Analysis over 33 junctions. Back up slide

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