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

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Presentation on theme: "Towards RNA structure prediction: 3D motif prediction and knowledge-based potential functions Christian Laing Tamar Schlick’s lab Courant Institute of."— Presentation transcript:

1 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

2 RNA folding is hierarchical 5‘gGACUCG GGGUGCCC UUCUGCGU GAAGGCUG AGAAAUACC CGUAUCAC CUGAUCUG GAUAAUGC CAGCGUAG GGAAGUUc3 ' SequenceSecondary Structure 3D Structure Annotated diagram 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. TPP riboswitch (PDB: 2GDI)

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 H i -H i+1 is enhanced when J i,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. Family HFamily KFamily XFamily t Family Coaxial H. H: 10 H: 1 2.0 0 0.5 0 2.4 0 0.0 0 H1-H4; H2-H4 H1-H2; H3-H4 K: 3 K: 4 K: 1 5.0 4.3 2 2.3 3.8 0 4.3 1.8 1 0.3 1.3 0 H1-H4 H3-H4 H2-H3 X: 54.42.25.63.8 t: 16451H2-H4

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. 102 No. 19 6789-94 (2005) PNAS V. 104 No. 37 14664-9 (2007)

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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