1 / 16

RNA Secondary Structure Prediction

RNA Secondary Structure Prediction. 16s rRNA. RNA Secondary Structure. Pseudoknot. Dangling end. Single- Stranded. Interior Loop. Bulge. Junction (Multiloop). Stem. Hairpin loop. Image– Wuchty. RNA secondary structure. G A A A G G A-U U-G C-G

chloe
Download Presentation

RNA Secondary Structure Prediction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. RNA Secondary Structure Prediction

  2. 16s rRNA

  3. RNA Secondary Structure Pseudoknot Dangling end Single- Stranded Interior Loop Bulge Junction (Multiloop) Stem Hairpin loop Image– Wuchty

  4. RNA secondary structure G A A A G G A-U U-G C-G A-U G-C Loop wobble pair Stem canonical pair

  5. Pseudoknots RNA secondary structure representation Legitimate structure

  6. Non-canonical interactions of RNA secondary-structure elements These patterns are excluded from the prediction schemes as their computation is too intensive. Pseudoknot Kissing hairpins Hairpin-bulge contact

  7. “Rules for 2D RNA prediction” • Base Pairs in stems: GOOD • Additional possible assumptions: e.g., G:C better than A:T • Bulges, Loops: BAD • Canonical Interactions (base pairs, stems, bulges, loops): OK • Non canonical interactions (pseudoknots, kissing hairpins): Forbidden • The more interactions: The better

  8. Predicting RNA secondary Structure • Allowed base pairing rules (Watson-Crick A:U, G:C, and Wobble pair G:U) • Sequences may form different structures • An free energy value is associated with each possible structure • Predict the structure with the minimal free energy (MFE)

  9. Simplifying Assumptions for Structure Prediction • RNA folds into one minimum free-energy structure. • There are no non-canonical interactions. • The energy of a particular base pair in a double stranded regions is sequence independent • Neighbors have no influence. Was solved by dynamic programming Zucker and Steigler 1981

  10. Sequence-dependent free-energy (the nearest neighbor model) U U C G U A A U G C A UCGAC 3’ U U C G G C A U G C A UCGAC 3’ Example values: GC GC GC GC AU GC CG UA -2.3 -2.9 -3.4 -2.1

  11. Free energy computation U U A A G C G C A G C U A A U C G A U A 3’ A 5’ +5.9 (4 nt loop) -1.1 mismatch of hairpin -2.9 stacking +3.3 (1 nt bulge) -2.9 stacking -1.8 stacking -0.9 stacking -1.8 stacking 5’ dangling -2.1 stacking -0.3 G= -4.6 KCAL/MOL -0.3

  12. Prediction Programs • Mfold http://www.bioinfo.rpi.edu/applications/mfold/old/rna/form1.cgi • Vienna RNA Secondary Structure Prediction http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi

  13. Mfold - Suboptimal Folding • For any sequence of N nucleotides, the expected number of structures is greater than 1.8N • A sequence of 100 nucleotides has ~31025 possible folds. If a computer can calculate 1000 folds/second, it would take 1015 years (age of universe = ~1010 years)! • Mfold generates suboptimal folds whose free energy fall within a certain range of values. Many of these structures are different in trivial ways. These suboptimal folds can still be useful for designing experiments.

  14. Example:

  15. Output:

More Related