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Tutorial 11

Tutorial 11. RNA Structure Prediction. 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. About RNA…. RNA secondary structure.

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Tutorial 11

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  1. 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.

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

  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. 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 http://www.almob.org/content/pdf/1748-7188-6-26.pdf

  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.

  19. RNAinverse - input

  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.

  22. http://www.targetscan.org/ • 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.

  23. 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

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

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

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

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

  28. Viruses

  29. 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.

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

  31. 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

  32. “…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.

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