Tutorial 9 RNA Structure Prediction RNA Structure

Tutorial 9 RNA Structure Prediction

RNA Structure Prediction • RNA secondary structure prediction RNAfold, RNAalifold • micro. RNA prediction Target. Scan – Cool story of the day: How viruses use mi. RNAs to attack humans

RNA secondary structure prediction GGGCUAUUAGCUCAGUUGGUUA GAGCGCACCCCUGAUAAGGGUGA GGUCGCUGAUUCGAAUUCAGCAU AGCCCA Base pair probability

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.

http: //rna. tbi. univie. ac. at/

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.

RNAfold - input RNA sequence

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

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

RNAalifold Structure prediction based on alignments Alignment

RNAalifold - output

Understanding the color scheme C-G U-A C-G C-G G-C U-A A-U C-C http: //www. almob. org/content/pdf/1748 -7188 -6 -26. pdf

Micro. RNAs mi. RNA gene mature mi. RNA Target gene

Micro. RNA in Cancer Sun et al, 2012

The challenge for Bioinformatics: - Identifying new micro. RNA genes - Identifying the targets of specific micro. RNA

How to find micro. RNA 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-mi. RNA is usually evolutionary conserved

How to find micro. RNA genes? A. Structure prediction B. Evolutionary Conservation

Predicting micro. RNA targets Micro. RNA targets are located in 3’ UTRs, and complementing mature micro. RNAs • Why is it hard to find them ? ? – Base pairing is required only in the seed sequence (7 -8 nt) – Lots of known mi. RNAs have similar seed sequences Very high probability to find by chance mature mi. RNA 3’ UTR of Target gene

Predicting micro. RNA 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 (mi. RNA+target) – Consider the delta MFE for RNA-RNA pairing versus the folding of the target ∆G (mi. RNA+target )- ∆G (target)

http: //www. targetscan. org/

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

Mir 136

Mir 136 - conserved* micro. RNA * conserved across most mammals, but usually not beyond placental mammals

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

Cool Story of the day How viruses use mi. RNAs to attack humans?

The group developed an algorithm for predicting mi. RNA targets and applied it to human Cytomegalovirus (hcmv) mi. RNAs MICB, an immunorelated gene, was among the highest ranking predicted targets and the top prediction for hcmv-mi. R-UL 112. They found that hcmv-mi. R-UL 112 specifically down-regulates MICB expression during viral infection, leading to reduced killing by Natural Killer cells (A human virus-defense mechanism)

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 NKG 2 D. • MICB is a stress-induced ligand of NK cells through the NKG 2 D receptor). Cerwenka et al. Nature Reviews Immunology 2001