1. RNA Structure Prediction
Tutorial 11
RNA Structure Prediction

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

3. About RNA…

4. RNA secondary structure

5. 3D perspective

6. RNA secondary structure types
stem

7. Structure representations
:::::: free extremes
((())) Stem
<<<>>> Internal Stem
______ Loop
,,,,,, Internal loop
Directionality

8. RNA secondary structure prediction
GGGCUAUUAGCUCAGUUGGUUAGAGCGCACCCCUGAUAAGGGUGAGGUCGCUGAUUCGAAUUCAGCAUAGCCCA
Base pair probability

9. 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.
RNAinverse server 
design RNA sequences for any desired target secondary structure.

11. 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.

12. RNAfold - input
RNA sequence

13. RNAfold - output Minimal free energy structure Structure prediction
Frequency of the structure
Best “average” structure

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

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

16. RNAalifold - output

17. Understanding the color scheme
Lighter color
C-G
U-A
C-G
G-C
U-A
A-U

18. RNAinverse Find sequences that match a structure.
Binding in RNA is based a lot on the structure, and not only the sequence.
Helps identify a function.
Binding in

19. RNAinverse - input
Binding in

20. RNAinverse - output

21. MicroRNA May reside in host genes or in intragenic region.
The RNA structure of the hairpins in a pri-miRNA is recognized by the proteins.
The RNA structure of the hairpins in a pri-miRNA is recognized by the nuclear protein Pasha.
Hairpin length and loop size influence the efficiency of Dicer processing, and imperfect pairing also affects cleavage.
miRNP = RISC (RNA Induced Silencing Complex)

22. Search for predicted microRNA targets in mammals (/worm/fly) 3’ UTRs.
Search for predicted microRNA targets in mammals (/worm/fly) 3’ UTRs.
Find conserved 8mer and 7mer sites that match the seed region of each miRNA.
Predictions are ranked based on the predicted efficacy of targeting as calculated using the context+ scores of the sites.

25. 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

27. Mir 31 - broadly conserved* microRNA
* conserved across most vertebrates, usually to zebrafish

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

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

30. “…We developed an algorithm for the prediction of miRNA targets and applied it to human cytomegalovirus miRNAs…”

31. Viruses

32. Human cytomegalovirus (HCMV) is known to have evolved effective immune evasion strategies.
RepTar has its basis in the observation that miRNA binding sites can repeat several times in the target’s 3′UTR (1). It therefore searches for repetitive elements in each 3′UTR sequence and evaluates these elements as potential miRNA binding sites.
This algorithm is independent of evolutionary conservation of the binding sites.

33. MICB, an immunorelated gene, was among the highest ranking predicted targets and the top prediction for hcmv-miR-UL112.

34. 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

35. “…We show that hcmv-miR-UL112 specifically down-regulates MICB expression during viral infection, leading to decreased binding of NKG2D and reduced killing by NK cells…” “Our results demonstrate a novel miRNA-based evasion strategy used by HCMV, in which a viral miRNA directly down-regulates a host immune defense gene.