1. Predicting RNA Structure and Function

2. Nobel prize 1989Nobel prize 2009 Ribozyme Ribosome RNA has many biological functions The function of the RNA molecule depends on its folded structure

3. 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

4. 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

5. RNA Structural levels tRNA Secondary Structure Tertiary Structure

6. Protein structuresRNA structures Total 72000 Total <2000 Due to the limited amount of data To date (2012) Predicting RNA tertiary structure is almost impossible

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

8. 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.

9. 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’

10. 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)+…..

11. 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

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

13. 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

14. 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.

15. 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).

16. 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.

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

18. 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

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

20. 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

21. 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

22. 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

23. 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

24. 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.

25. Using RNA secondary structure predictions for prediction functional non-coding RNAs

26. MicroRNAs miRNAs are transcribed as ~70nt precursors and subsequently processed by the Dicer enzyme to give a ~22nt product. The products are thought to have regulatory roles through complementarity to mRNA.

27. Seed alignment (based on 7 sequences)

28. Two major problems which can be addressed by bioinformatics How to find microRNA genes? Given a microRNA gene, how to find its targets?

29. How to find microRNA genes? Searching for sequences that fold to a hairpin ~70 nt - 20-to 24-nt RNAs derived from endogenous transcripts that form local hairpin structures Concentrating in intragenic regions and introns -miRNA genomic loci are distinct from other types of recognized genes. Usually reside in introns. Filtering out non conserved candidates -Mature and pre-miRNA is usually evolutionary conserved

30. Predicted stem/loop secondary structure by RNAfold of known pre-miRNA. The sequence of the mature miRNAs in red. How to find microRNA genes?

31. New human and mouse miRNA detected by homology Entire set of human and mouse pre- and mature miRNA from the miRNA registry was submitted to BLAT search engine against the human genome and then against the mouse genome. Sequences with high % identity were examined for hairpin structure using MFOLD, and 16-nt stretch base paring.

32. 60 new potential miRNAs (15 for human and 45 for mouse) Mature miRNA were either perfectly conserved or differed by only 1 nucleotide between human and mouse. Weber, FEBS 2005

33. Human and mouse miRNAs reside in conserved regions Mmu-mir-345 resides in AK0476268 RefSeq gene. Human orthologue was found upstream of C14orf69, the best BLAT hit for AK0476268.

34. Predicting microRNA target

35. Predicting microRNA target genes Why is it hard to find them ?? –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 MicroRNA targets are located in 3’ UTRs, and complementing mature microRNAs

36. 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

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

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

39. 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

40. How to make more accurate predictions? Incorporating mRNA UTR structure to predict microRNA targets –Make sure the predicted target “accessible”. –Not forming basing pairing its self.

41. How to make more accurate predictions? Searching for Clusters MicroRNA targets conserve across species. Tends to appear in a cluster.