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Tertiary Base Triple Formation in the SRV-1 Frameshifting Pseudoknot Stabilizes Secondary Structure Components.

Lixia YangDesiree-Faye Kaixin TohManchugondanahalli S KrishnaZhensheng ZhongYiyao LiuShaomeng WangYubin GongGang Chen
Published in: Biochemistry (2020)
Minor-groove base triples formed between stem 1 and loop 2 of the simian retrovirus type 1 (SRV-1) mRNA frameshifting pseudoknot are essential in stimulating -1 ribosomal frameshifting. How tertiary base triple formation affects the local stabilities of secondary structures (stem 1 and stem 2) and thus ribosomal frameshifting efficiency is not well understood. We made a short peptide nucleic acid (PNA) that is expected to invade stem 1 of the SRV-1 pseudoknot by PNA-RNA duplex formation to mimic the stem 1 unwinding process by a translating ribosome. In addition, we used a PNA for invading stem 2 in the SRV-1 pseudoknot. Our nondenaturing polyacrylamide gel electrophoresis data for the binding of PNA to the SRV-1 pseudoknot and mutants reveal that mutations in loop 2 disrupting base triple formation between loop 2 and stem 1 in the SRV-1 pseudoknot result in enhanced invasion by both PNAs. Our data suggest that tertiary stem 1-loop 2 base triple interactions in the SRV-1 pseudoknot can stabilize both of the secondary structural components, stem 1 and stem 2. Stem 2 stability is thus coupled to the structural stability of stem 1-loop 2 base triples, mediated through a long-range effect. The apparent dissociation constants of both PNAs are positively correlated with the pseudoknot mechanical stabilities and frameshifting efficiencies. The relatively simple PNA local invasion experiment may be used to characterize the energetic contribution of tertiary interactions and ligand binding in many other RNA and DNA structures.
Keyphrases
  • nucleic acid
  • transcription factor
  • computed tomography
  • mass spectrometry
  • genome wide
  • electronic health record
  • single molecule
  • binding protein
  • cell migration
  • circulating tumor
  • artificial intelligence
  • deep learning