1. Predicting RNA Structure and Function

2. Nobel prize 1989 Nobel prize 2009 Ribozyme Ribosome

3. RNA has many biological functions Enzymatic reaction (protein synthesis) Control of mRNA stability (UTR) Control of splicing (snRNP) Control of translation (microRNA) The function of the RNA molecule depends on its folded structure

4. Protein structuresRNA structures Total 65000Total 700

5. RNA Structural levels tRNA Secondary Structure Tertiary Structure

6. 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.

7. RNA Secondary structure Short Range Interactions 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’

8. The function of the RNA molecule depends on its folded structure Example: mRNA structure involved in control of Iron levels G U A G C N N N’ C N N’ 5’3’ conserved Iron Responsive Element IRE Recognized by IRP1, IRP2

9. 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

10. Predicting RNA secondary Structure Most common approach: Search for a RNA structure with a Minimal Free Energy (MFE) 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

11. Free energy model Free energy of a structure is the sum of all interactions energies Each interaction energy can be calculated thermodynamicly Free Energy(E) = E(CG)+E(CG)+…..

12. Why is MFE secondary structure prediction hard? MFE structure can be found by calculating free energy of all possible structures BUT the number of potential structures grows exponentially with the number, n, of bases

13. RNA folding with Dynamic programming (Zucker and Steigler) W(i,j): MFE structure of substrand from i to j ij W(i,j)

14. RNA folding with dynamic programming Assume a function W(i,j) which is the MFE for the sequence starting at i and ending at j (i<j) Define scores, for example base pair (CG) =-1 non- pair(CA)=1 (we want a negative score ) Consider 4 possibilities: –i,j are a base pair, added to the structure for i+1..j-1 –i is unpaired, added to the structure for i+1..j –j is unpaired, added to the structure for i..j-1 –i,j are paired, but not to each other; Choose the minimal energy possibility i (i+1) W(i,j) (j-1) j

15. Simplifying Assumptions for Structure Prediction RNA folds into one minimum free-energy structure. The energy of a particular base can be calculated independently –Neighbors do not influence the energy.

16. Sequence dependent free-energy 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’ Energy is influenced by the previous base pair (not by the base pairs further down).

17. 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 These energies are estimated experimentally from small synthetic RNAs.

18. Mfold :Adding Complexity to Energy Calculations Positive energy - added for destabilizing regions such as bulges, loops, etc. More than one structure can be predicted

19. 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

20. Mfold :Adding Complexity to Energy Calculations Positive energy - added for destabilizing regions such as bulges, loops, etc. More than one structure can be predicted

21. Frey U H et al. Clin Cancer Res 2005;11:5071-5077 ©2005 by American Association for Cancer Research More than one structure can be predicted for the same RNA

22. RNA fold prediction based on Multiple Alignment Information from multiple sequence alignment (MSA) can help to predict the probability of positions i,j to be base-paired. 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

23. Compensatory Substitutions U U C G U A A U G C A UCGAC 3’ G C 5’ Mutations that maintain the secondary structure can help predict the fold

24. RNA secondary structure 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

25. Insight from Multiple Alignment Information from multiple sequence alignment (MSA) can help to predict 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.

26. 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 alignment information to the standard energy model Improvement in prediction accuracy

27. 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

28. 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 alignment and their secondary structures

29. An example of an RNA family miR-1 MicroRNAs mir-1 microRNA precursor family This family represents the microRNA (miRNA) mir-1 family. miRNAs are transcribed as ~70nt precursors (modelled here) and subsequently processed by the Dicer enzyme to give a ~22nt product. The products are thought to have regulatory roles through complementarity to mRNA.

30. Seed alignment (based on 7 sequences)

31. Predicting microRNA target

32. Predicting microRNA target genes Why is it hard?? –Lots of known miRNAs with similar seeds –Base pairing is required only for seed (7 nt) Very High probability to find by chance Initial methods –Look at conserved miRNAs –Look for conserved target sites –Consider the RNA fold

33. TargetScan Algorithm by Lewis et al 2003 The Goal – find miRNA candidate target genes of a given miRNA Stage 1: Select only the 3’UTR of all genes –Search for 7nts which are complementary to bases 2-8 from miRNA (miRNA seed”) in 5’UTRs

34. TargetScan Algorithm Stage 2: Extend seed matches in both directions –Allow G-U (wobble) pairs

35. TargetScan Algorithm Stage 3: Optimize base-pairing in remaining 3’ region of miRNA (not applied in the later versions)

36. Stage 4: Calculate the folding free energy (G) assigned to each putative miRNA:target interaction using RNAfold Low energy get high Score Stage 5: Calculate a final score for a UTR to be a target adding evolutionary conservation (by doing the same steps on UTR from other species) TargetScan Algorithm

37. Predicting targets of RNA-binding protein (RBP)

38. Predicting RBPs target Different types of RBPs –Proteins that regulate RNA stability (bind usually at the 3’UTR) –Splicing Factors (bind exonic and intronic regions) –…… Why is it hard –Most RBP sites are short and degenerative (e.g. CTCTCT )

39. Predicting Exon Splicing Enhancers ESE-finder 1. Built PSSM for ESE, based on experimental data (SELEX)

40. ESE-finder 2. A given sequence is tested against 5 PSSM in overlapping windows 3. Each position in the sequence is given a score 4. Position which fit a PSSM (score above a cutoff) are predicted as ESEs http://rulai.cshl.edu/

41. http://sfmap.technion.ac.il

42. Predicting RBPs target RNA binding sites can be predicted by general motif finders -MEME http://metameme.sdsc.edu/ -DRIM http://bioinfo.cs.technion.ac.il/drim/