Login / Signup

Thermal adaptation of mRNA secondary structure: stability versus lability.

Ming-Ling LiaoYun-Wei DongGeorge N Somero
Published in: Proceedings of the National Academy of Sciences of the United States of America (2021)
Macromolecular function commonly involves rapidly reversible alterations in three-dimensional structure (conformation). To allow these essential conformational changes, macromolecules must possess higher order structures that are appropriately balanced between rigidity and flexibility. Because of the low stabilization free energies (marginal stabilities) of macromolecule conformations, temperature changes have strong effects on conformation and, thereby, on function. As is well known for proteins, during evolution, temperature-adaptive changes in sequence foster retention of optimal marginal stability at a species' normal physiological temperatures. Here, we extend this type of analysis to messenger RNAs (mRNAs), a class of macromolecules for which the stability-lability balance has not been elucidated. We employ in silico methods to determine secondary structures and estimate changes in free energy of folding (ΔG fold ) for 25 orthologous mRNAs that encode the enzyme cytosolic malate dehydrogenase in marine mollusks with adaptation temperatures spanning an almost 60 °C range. The change in free energy that occurs during formation of the ensemble of mRNA secondary structures is significantly correlated with adaptation temperature: ΔG fold values are all negative and their absolute values increase with adaptation temperature. A principal mechanism underlying these adaptations is a significant increase in synonymous guanine + cytosine substitutions with increasing temperature. These findings open up an avenue of exploration in molecular evolution and raise interesting questions about the interaction between temperature-adaptive changes in mRNA sequence and in the proteins they encode.
Keyphrases
  • molecular dynamics simulations
  • high resolution
  • single molecule
  • binding protein
  • molecular docking
  • minimally invasive
  • high intensity
  • deep learning
  • data analysis