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Two Approaches of RNA Design: Fluorescent Riboswitch & Ligase Selection

The Schlick Group Retreat February 2008. Two Approaches of RNA Design: Fluorescent Riboswitch & Ligase Selection. Namhee Kim and Giulio Quarta Laboratory of Prof. Tamar Schlick. 1. Design and Applications of Novel RNAs 2. Novel Candidates of Fluorescent Riboswitch

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Two Approaches of RNA Design: Fluorescent Riboswitch & Ligase Selection

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  1. The Schlick Group Retreat February 2008 Two Approaches of RNA Design:Fluorescent Riboswitch & Ligase Selection Namhee Kim and Giulio Quarta Laboratory of Prof. Tamar Schlick 1.Design and Applicationsof Novel RNAs 2. Novel Candidates of Fluorescent Riboswitch 3. Pool Design & Selection of Ligase Ribozymes 4. Future Directions

  2. 1. RNA Design • Certain RNA aptamers have been found to bind and function with specific compounds and molecules • Examples: • In vitro: Malachite Green Aptamer (MGA), Hammerhead Ribozyme • In vivo: Thiamine Pyrophosphate Binding Riboswitches (TPP-BR) • RNA Design: Allosteric shifts can prevent/stabilize secondary/tertiary structures upon ligand binding Baugh et al., J. Mol. Biol. 301:117 -128 (2000) Serganov et al., Nature 441:1167 -1171 (2006)

  3. Gene Regulation by TPP Riboswitch Translation initiation (thiM genes), with the dissociation of TPP Transcription termination regulation (thiC genes), with dissociation of TPP Serganov et al., Structural basis for gene regulation by a thiamine pyrophsphate-sensing riboswitchNature 441:1167 -1171 (2006)

  4. 2.1. Modular Design Trans. OFF NO FLUR. + And Trans. ON FLUR. T2 T1 + M1 T3

  5. 2.2. Co-transcriptional Folding of TPP Riboswitch 125nt: Thi-box formed 150nt: a helix formed 175nt: TPP domain is disappeared and anti-terminator formed 185nt: TPP domain is reformed and terminator formed

  6. Thermodynamic Analysis C 2-4 (a) S1 C 2-4 (b) S1

  7. Experimental Results Form 2 State 2 DNA template 2-1 2-2 2-3 2-4 A1-TPP fusion TTP - - + + - - + + - - + + - - + + - - + + Malachite Green - + - + - + - + - + - + - + - + - + - + 6% gel, chase by 100mkM NTPs No Structural Change Upon TPP (2-1, 2-2, 2-3, 2-4) Structural Change Upon TPP (Wild Type: A1-TPP fusion) Provided by the Nudler lab

  8. Wild-Type Riboswitch Landscape TPP State2: Terminator Yes TPP binding State1: Anti-terminator No TPP binding

  9. New Candidate Sequence (Terminator added) TPP C 2-4 State2: Terminator Yes TPP binding No MG binding State1: Anti-terminator No TPP binding Yes MG binding

  10. 3.1. Pool Design for RNA Ligases Requirement: Sequence length ≤ 60 nt Pool 1: 4_2 + 5_2 66% of Seq 8, Matrix 13 AGCGCCGUGGCAGGGCUCAUAACCCUGAUGUCCUCGGAUCGAAACCGAGCGGCGCUACCA 34% of Seq 12, Matrix 8 GGCAGUACCAAGUCGCGAAAGCGAUGAUGGUAA CCUUGCAAAGGGUUAAGCUGCC Pool 2: 4_1 + 5_1 6% of Seq 5, Matrix 4 GGCAGUACCAAGUCGCGAAAGCGAUGGCCUUGCAAAGGGUAUGGUGCUGCC 94% of Seq 12, Matrix 11 GGCAGUACCAAGUCGCGAAAGCGAUGAUGGUAAGCCUUGCAAAGGGUUAAGCUGCC Pool 3: 4_1 + 4_2 77% of Seq 6, Matrix 13 GGGACGAGCACGUGAAUCGUCUCGACGUGUGUAGGGGAAAGUAUCCCCCGUCCC 23% of Seq 25, Matrix 12 GCCCCGCUGAUGAGGUCAGGGAAGACCGAAAGUGUCGACUCUACGGGGC Pool 4: Random pools with 60nt 100% of Seq 8, Matrix 4 AGCGCCGUGGCAGGGCUCAUAACCCUGAUGUCCUCGGAUCGAAACCGAGCGGCGCUACCA Motif ID: 31 Motif ID: 42 Emergence of a fast-reacting ribozyme that is capable of undergoing continuous evolution, Sarah B. Voytek and Gerald F. Joyce, PNAS, (2007) Designed by RAGPOOLS

  11. 3.2. Selection of RNA Ligases • We are scanning 109 sequences in designed pools 1, 2, 3, and random pool using RNAMotif with consensus of DSL ligase and T80 secondary motif without sequence restriction • But we did not get positive results yet in designed pools • We try to start with known sequences (DSL and T80) and use 22 mixing matrices • Preliminary Scanning Results …

  12. 4. Future Directions • Fluorescent riboswitch: Optimize the sequence to have the ideal structural changes and two clear clusters in the energy landscape, in vitro and in vivo experimental verification • Ligase selection: Design pools starting with known or other sequences and provide a proof that designed pools are efficient, expanding in silico pool size into 1015

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