1. Predicting RNA Structure and Function

2. According to the central dogma of molecular biology the main role of
DNA
RNA
protein
According to the central dogma of molecular biology the main role of
RNA is to transfer genetic information from DNA to protein

3. RNA has many other biological functions
Protein synthesis (ribosome)
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. Ribozyme
Ribosome
Nobel prize
1989
Nobel prize
2009

5. Protein structures
RNA structures
~Total 80,000
Total ~800

6. RNA Structural levels
Secondary Structure
Tertiary Structure
tRNA

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

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

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

10. 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

11. Predicting RNA secondary Structure
Most common approach: Zucker & Stiegler (1981)
Search for a RNA structure with a
Minimal Free Energy (MFE)
U U
C G
U A
A U
G C
G A U C U U G A U C

12. Free energy of a structure is the sum of all interactions energies
Free energy model
Free energy of a structure is the sum of all interactions energies
exclude coaxial stacking, metal ions, nonstandard bonds, folding pathway, etc
Free Energy(E) = E(CG)+E(CG)+…..
Each interaction energy can be calculated thermodynamicly

13. 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

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

15. 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
(CG) =-1 (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;
W(i,j)
i (i+1)
(j-1) j
Choose the minimal energy possibility

16. 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.

17. 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’
Energy is influenced by the previous base pair
(not by the base pairs further down).

18. 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

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

20. 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

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

22. More than one structure can be predicted for the same RNA
GNAS1 mRNA folding structures predicted by MFOLD. The mRNA sequence carrying the T393C polymorphism was used for secondary folding structure model building by the use of the computer program MFOLD (26).
Frey U H et al. Clin Cancer Res 2005;11:
©2005 by American Association for Cancer Research

23. 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

24. Compensatory Substitutions
Mutations that maintain the secondary structure
can help predict the fold
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. 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

28. 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 (pre-mir) and subsequently processed to give a ~22nt product (miRNA=mir). The products are thought to have regulatory roles through complementarity to mRNA.

29. Seed alignment (based on 7 sequences)