1. Predicting RNA Structure and Function

2. Non coding DNA (98.5% human genome) Intergenic Repetitive elements Promoters Introns mRNA untranslated region (UTR)

3. RNA Molecules mRNA tRNA rRNA Other types of RNA -RNaseP – trimming 5’ end of pre tRNA -telomerase RNA- maintaining the chromosome ends -Xist RNA- inactivation of the extra copy of the x chromosome

4. Some biological functions of ncRNA Nuclear export mRNA cellular localization Control of mRNA stability Control of translation The function of the RNA molecule depends on its folded structure

5. Control of Iron levels by mRNA secondary structure G U A G C N N N’ C N N’ 5’3’ conserved Iron Responsive Element IRE Recognized by IRP1, IRP2

6. IRP1/2 5’ 3’ F mRNA 5’ 3’ TR mRNA IRP1/2 F: Ferritin = iron storage TR: Transferin receptor = iron uptake IRE Low Iron IRE-IRP inhibits translation of ferritin IRE-IRP Inhibition of degradation of TR High Iron IRE-IRP off -> ferritin translated Transferin receptor degradated

7. RNA Secondary Structure U U C G U A A U G C 5’ 3’ 5’ G A U C U U G A U C 3’ STEM LOOP The RNA molecule folds on itself. The base pairing is as follows: G C A U G U hydrogen bond.

8. RNA Secondary structure G G A U U G C C G G A U A A U G C A G C U U INTERNAL LOOP HAIRPIN LOOP BULGE STEM DANGLING ENDS 5’3’

9. RNA secondary Structure representation tRNA

10. RNA secondary Structure representation Large subunits rRNA Small subunit rRNA

11. Legal structure Pseudoknots RNA secondary structure representation

12. Examples of known interactions of RNA secondary structural elements Pseudo-knot Kissing hairpins Hairpin-bulge contact These patterns are excluded from the prediction schemes as their computation is too intensive.

13. Predicting RNA secondary Structure According to base pairing rules only (Watson Crick A-T G-C and wobble pairs G-T) sequences may form different structures An energy value is associated with each possible structure Predict the structure with the minimal free energy (MFE)

14. Simplifying Assumptions for Structure Prediction RNA folds into one minimum free-energy structure. There are no knots (base pairs never cross). The energy of a particular base pair in a double stranded regions is sequence independent –Neighbors do not influence the energy. Was solved by dynamic programming Zucker and Steigler 1981

15. Sequence dependent free-energy values of the base pairs (nearest neighbor model) U U C G G C A U G C A UCGAC 3’ 5’ U U C G U A A U G C A UCGAC 3’ 5’ Example values: GC GC AU GC CG UA -2.3 -2.9 -3.4 -2.1

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

17. Adding Complexity to Energy Calculations Stacking energy - We assign negative energies to these between base pair regions. –Energy is influenced by the previous base pair (not by the base pairs further down). –These energies are estimated experimentally from small synthetic RNAs. Positive energy - added for destabilizing regions such as bulges, loops, etc. More than one structure can be predicted

18. Prediction Tools based on Energy Calculation Fold, Mfold Zucker & Stiegler (1981) Nuc. Acids Res. 9:133-148 Zucker (1989) Science 244:48-52 RNAfold Vienna RNA secondary structure server Hofacker (2003) Nuc. Acids Res. 31:3429-3431

19. Tools’ Features Sub-optimal structures -Provide solutions within a specific energy range. Constraints - Regions known experimentally to be single/double stranded can be defined. Statistical significance - Currently lacking in energy based methods -Recently was suggested to estimate a significant stable and conserved fold in aligned sequences (Washietl ad Hofacker 2004) Support by compensatory mutations.

20. Compensatory Substitutions U U C G U A A U G C A UCGAC 3’ G C 5’ Maintain the secondary structure

21. Evolutionary conservation of RNA molecules can be revealed by identification of compensatory mutations G C C U U C G G G C G A C U U C G G U C G G C U U C G G C C U C U G C G N N’ G C

22. Insight from Multiple Alignment information from multiple alignment about the probability of positions i,j to be base-paired. Conservation – no additional information Consistent mutations (GC  GU) – support stem Inconsistent mutations – does not support stem. Compensatory mutations – support stem.

23. RNAalifold (Hofacker 2002) From the vienna RNA package Predicts the consensus secondary structure for a set of aligned RNA sequences by using modified dynamic programming algorithm that add covariance term to the standard energy model Improvement in prediction accuracy

24. Other related programs COVE RNA structure analysis using the covariance model (implementation of the stochastic free grammar method) QRNA (Rivas and Eddy 2001) Searching for conserved RNA structures tRNAscan-SE tRNA detection in genome sequences Sean Eddy’s Lab WU http://www.genetics.wustl.edu/eddy

25. RNA families Rfam : General non-coding RNA database (most of the data is taken from specific databases) http://www.sanger.ac.uk/Software/Rfam/ Includes many families of non coding RNAs and functional Motifs, as well as their alignement and their secondary structures

26. Rfam (currently version 6.1) 379 different RNA families or functional Motifs from mRNA UTRs etc. GENE INTRON Cis ELEMENTS

27. An example of an RNA family miR-1 MicroRNAs