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Incorporating a Thiophosphate Modification into a Common RNA Tetraloop Motif Causes an Unanticipated Stability Boost.

Pradeep S PallanTerry P LybrandMark K SchlegelJoel M HarpHartmut JahnsMuthiah ManoharanMartin Egli
Published in: Biochemistry (2020)
GNRA (N = A, C, G, or U; R = A or G) tetraloops are common RNA secondary structural motifs and feature a phosphate stacked atop a nucleobase. The rRNA sarcin/ricin loop (SRL) is capped by GApGA, and the phosphate p stacks on G. We recently found that regiospecific incorporation of a single dithiophosphate (PS2) but not a monothiophosphate (PSO) instead of phosphate in the backbone of RNA aptamers dramatically increases the binding affinity for their targets. In the RNA:thrombin complex, the key contribution to the 1000-fold tighter binding stems from an edge-on contact between PS2 and a phenylalanine ring. Here we investigated the consequences of replacing the SRL phosphate engaged in a face-on interaction with guanine with either PS2 or PSO for stability. We found that PS2···G and Rp-PSO···G contacts stabilize modified SRLs compared to the parent loop to unexpected levels: up to 6.3 °C in melting temperature Tm and -4.7 kcal/mol in ΔΔG°. Crystal structures demonstrate that the vertical distance to guanine for the closest sulfur is just 0.05 Å longer on average compared to that of oxygen despite the larger van der Waals radius of the former (1.80 Å for S vs 1.52 Å for O). The higher stability is enthalpy-based, and the negative charge as assessed by a neutral methylphosphonate modification plays only a minor role. Quantum mechanical/molecular mechanical calculations are supportive of favorable dispersion attraction interactions by sulfur making the dominant contribution. A stacking interaction between phosphate and guanine (SRL) or uracil (U-turn) is also found in newly classified RNA tetraloop families besides GNRA.
Keyphrases
  • nucleic acid
  • molecular dynamics
  • machine learning
  • transcription factor
  • high resolution
  • density functional theory
  • deep learning
  • quantum dots