1. RNA Structure Prediction
Tutorial 9
RNA Structure Prediction

2. RNA Structure Prediction
RNA secondary structure prediction
RNAfold, RNAalifold
microRNA prediction
TargetScan –
Cool story of the day:
How viruses use miRNAs to attack humans

3. RNA secondary structure prediction
GGGCUAUUAGCUCAGUUGGUUAGAGCGCACCCCUGAUAAGGGUGAGGUCGCUGAUUCGAAUUCAGCAUAGCCCA
Base pair probability

4. RNA structure prediction by Vienna RNA package
RNAfold server 
Minimum free energy structures and base pair probabilities from single RNA or DNA sequences.
RNAalifold server 
Consensus secondary structures from an alignment of several related RNA or DNA sequences. You need to upload an alignment.

6. RNAfold
Gives best stabilized structure (structure with minimal free energy (MFE))
Uses a dynamic programming algorithm that exploits base pairing and thermodynamic probabilities in order to predict the most likely structures of an RNA molecule.

7. RNAfold - input
RNA sequence

8. RNAfold - output Minimal free energy structure Structure prediction
Free energy of the ensamble
Best “average” structure

9. Graphic representation
An average, may not exist in the ensemble
MFE structure

10. Structure prediction based on alignments
RNAalifold
Structure prediction based on alignments
Alignment

11. RNAalifold - output

12. Understanding the color scheme
C-G
U-A
C-G
G-C
U-A
A-U
C-C

13. MicroRNAs
miRNA
gene
mature
miRNA
Target gene

14. MicroRNA in Cancer
Sun et al, 2012

15. The challenge for Bioinformatics:
- Identifying new microRNA genes
- Identifying the targets of specific microRNA

16. How to find microRNA genes?
Searching for sequences that fold to a hairpin ~70 nt
-RNAfold
-other efficient algorithms for identifying stem loops
Concentrating on intragenic regions and introns
- Filtering coding regions
Filtering out non conserved candidates
-Mature and pre-miRNA is usually evolutionary conserved

17. How to find microRNA genes?
A. Structure prediction
B. Evolutionary Conservation

18. Predicting microRNA targets
MicroRNA targets are located in 3’ UTRs, and complementing mature microRNAs
Why is it hard to find them ??
Base pairing is required only in the seed sequence
(7-8 nt)
Lots of known miRNAs have similar seed sequences
Very high probability to find by chance
mature miRNA
3’ UTR of Target gene

19. Predicting microRNA target genes
General methods
- Find motifs which complements the seed sequence (allow mismatches)
Look for conserved target sites
Consider the MFE of the RNA-RNA pairing ∆G (miRNA+target)
Consider the delta MFE for RNA-RNA pairing versus the folding of the target
∆G (miRNA+target )- ∆G (target)

22. A score reflecting the probability that a site is conserved due to selective maintenance of miRNA targeting rather than by chance or any other reason.
Sum of phylogenetic branch lengths between species that contain a site
More negative scores represent a more favorable site
The stability of of a miRNA-target duplex

23. Mir 136

24. Mir 136 - conserved* microRNA
* conserved across most mammals, but usually not beyond placental mammals

25. How to evaluate our results
True positive (TP) = correctly identified
(miRNA targets correctly identified)
False positive (FP)= incorrectly identified
(non miRNA targets miss identified as targets)
True negative (TN)= correctly rejected
(non miRNA targets correctly not identified)
False negative (FN) = incorrectly rejected
(miRNA targets not identified)
Sensitivity (recall)= True positive Rate = TP /(TP+FN)
Specificity (precision)= True Negative Rate = TN /(TN+FP)

26. How viruses use miRNAs to attack humans?
Cool Story of the day
How viruses use miRNAs to attack humans?

28. The group developed an algorithm for predicting
miRNA targets and applied it to human
Cytomegalovirus (hcmv) miRNAs
MICB, an immunorelated gene, was among the highest ranking predicted targets and the top prediction for hcmv-miR-UL112.
They found that hcmv-miR-UL112 specifically
down-regulates MICB expression during viral
infection, leading to reduced killing
by Natural Killer cells (A human virus-defense
mechanism)

29. Natural Killer (NK) cells
NK cells are cytotoxic lymphocyte that kill virus-infected cells and tumor cells.
In order to function they should be activated through receptors. One of these is NKG2D.
MICB is a stress-induced ligand of NK cells through the NKG2D receptor).
Cerwenka et al. Nature Reviews Immunology 2001