1. Predicting RNA Structure and Function

2. Ribozyme

3. The Ribosome : The protein factory of the cell mainly made of RNA

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

5. Some biological functions of ncRNA
Control of mRNA stability (UTR)
Control of splicing (snRNP)
Control of translation (microRNA)
The function of the RNA molecule depends on its folded structure

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

7. F: Ferritin = iron storage TR: Transferin receptor = iron uptake
IRP1/2
IRE 3’ 5’
F mRNA
IRP1/2 3’
TR mRNA 5’
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

8. RNA Structural levels
Secondary Structure
Tertiary Structure
tRNA

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

10. RNA Secondary structure Short Range Interactions
G G
A U
U G C
C G
G A
U A
G C
A G C U U
HAIRPIN LOOP
BULGE
INTERNAL LOOP
STEM
DANGLING ENDS 5’ 3’

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

12. Predicting RNA secondary Structure
Searching for a structure with Minimal
Free Energy (MFE)

13. Free energy model
Free energy of structure (at fixed temperature, ionic concentration) = sum of loop energies
Standard model uses experimentally determined thermodynamic parameters
exclude coaxial stacking, metal ions, nonstandard bonds, folding pathway, etc

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

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

16. 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 a base pair’s score is less than a non-pair
Consider 4 recursion possibilities:
i,j are a base pair, added to the structure for i+1..j-1
Define this as V(i,j)
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; the structure for i..j adds together sub-structures for 2 sub-sequences:
i..k and k+1..j a bifurcation (i<k<j)
Choose the minimal energy possibility

17. 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 calculated independently
Neighbors do not influence the energy.

18. Sequence dependent free-energy Nearest Neighbor Model
U U
C G
G C
A U
A UCGAC 3’
U U
C G
U A
A U
G C
A UCGAC 3’ 5’ 5’
Assign negative energies to interactions between base pair regions.
Energy is influenced by the previous base pair
(not by the base pairs further down).

19. Sequence dependent free-energy values of the base pairs (nearest neighbor model)
U U
C G
G C
A U
A UCGAC 3’
U U
C G
U A
A U
G C
A UCGAC 3’ 5’ 5’
These energies are estimated experimentally from small synthetic RNAs.
Example values:
GC GC GC GC
AU GC CG UA

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. Free energy computation
U U
A A
G C A
U A
A U
C G
A ’ 5’
nt loop
-1.1 mismatch of hairpin
-2.9 stacking
+3.3 1nt 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

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

23. Insight from 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

24. Compensatory Substitutions
Mutations that maintain the secondary structure
U U
C G
U A
A U
G C
A UCGAC 3’ G C 5’

25. 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
RNA secondary structure can be revealed by identification of compensatory mutations
U C
U G
C G
N N’
G C
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

26. 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.

27. 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

28. Other related programs
Sean Eddy’s Lab WU
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

29. RNA families Rfam : General non-coding RNA database
(most of the data is taken from specific databases)
Includes many families of non coding RNAs and functional
motifs, as well as their alignment and their secondary structures

30. Rfam /Pfam Pfam uses the HMMER (based on Hidden Markov Models)
Rfam uses the INFERNAL
(based on Covariation Model)

31. Rfam (currently version 7.0)
Different RNA families or functional
Motifs from mRNA, UTRs etc.
View and download multiple sequence alignments
Read family annotation
Examine species distribution of family members
Follow links to otherdatabases

32. 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.

33. Seed alignment (based on 7 sequences)

34. Predicting microRNA target

35. Predicting microRNA target genes
Why is it hard??
Lots of known miRNAs
Mostly unknown target genes
Initial method outline
Look at conserved miRNAs
Look for conserved target sites

36. miRNAs in animals 0.5%-1.0% of predicted genes encode miRNA (!!)
One of the more abundant regulatory classes
Tissue-specific or developmental stage-specific expression
High evolutionary conservation

37. TargetScan Algorithm by Lewis et al 2003
The Goal – a ranked list of candidate target genes
Stage 1: Search UTRs in one organism
Bases 2-8 from miRNA = “miRNA seed”
Perfect Watson-Crick complementarity
No wobble pairs (G-U)
7nt matches = “seed matches”

38. TargetScan Algorithm Stage 2: Extend seed matches
Allow G-U (wobble) pairs
Both directions
Stop at mismatches

39. TargetScan Algorithm Stage 3: Optimize basepairing
Remaining 3’ region of miRNA
35 bases of UTR 5’ to each seed match
RNAfold program (Hofacker et al 1994)

40. TargetScan Algorithm
Stage 4: Folding free energy (G) assigned to each putative miRNA:target interaction
Assign rank to each UTR
Repeat this process for each of the other organisms with UTR datasets

41. Predicting RNA-binding protein (RBP) targets

42. 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
Different proteins bind different sequences
Most RBP sites are short and degenertaive (e.g. CTCTCT )

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

44. 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

45. Predicting RBPs target
RNA binding sites can be predicted by general motif finders
MEME
DRIM