Membrane proteins play key roles in various cellular functions and are key targets for drug intervention. In fact, approximately 60 percent of the drugs currently on the market target these specific proteins. In order to develop effective drugs that interact with membrane proteins, it is essential to understand the structure of membrane proteins and the fundamentals of the folding process. Recognizing this urgent need, Prof. Duyoung Min and his research team at UNIST’s Department of Chemistry embarked on a pioneering study to reveal the folding dynamics of helical membrane proteins. By developing a robust single-molecule tweezers approach using dibenzocyclocycloaddition and traptavidin binding, the team succeeded in estimating the folding “speed limit” of these proteins. These findings provide valuable insights into structural states, dynamics, and energy barrier properties and are critical to advancing our understanding of this field. Single-molecule tweezers, including magnetic tweezers, have emerged as powerful tools for studying nanoscale structural changes of individual membrane proteins under force. However, previous studies were limited by weak molecular chains due to force-induced bond scission, preventing long-term observations of repeat molecular transfer. Overcoming this challenge is critical to obtaining reliable characterizations of structural states and dynamics. In their study, published in the May 2023 issue of the journal eLife, Prof. Min and his research team introduced an innovative single-molecule tweezers approach that exhibited superior stability compared to conventional linkage systems. The lifetime of this new method is more than 100 times longer than that of existing methods, surviving for 12 hours at forces up to 50 pn and allowing approximately 1000 pull cycle experiments. Using this advanced technique, the research team observed many structural transitions within the engineered single-chain transmembrane dimer under a constant force of 12 pN for 9 hours. These observations provide unprecedented insight into its folding pathway and reveal previously hidden dynamics associated with helical-coil switching. To accurately characterize the energy barrier heights and folding times during these transitions, the researchers employed a model-independent deconvolution approach combined with Hidden Markov Modeling analysis. Results obtained using the Kramers rate framework revealed a very low speed limit of 21 ms for helical hairpin formation in lipid bilayers, in contrast to the typical microsecond scale of soluble protein folding. This difference can be attributed to the high viscosity of lipid membranes, which hinders helix-helix interactions. These findings provide more effective guidance for understanding the kinetics and free energy associated with membrane protein folding, which is a key factor in the development of drugs targeting membrane proteins. Since approximately 60 percent of drugs on the market focus on these proteins, this research opens new avenues to enhance drug research and design.

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RNA pull-down assay is a useful tool for researching RNA-protein interactions in plants. The idea is to separate proteins that attach themselves to RNA by using RNA with biotin tagging as bait. First, attach the biotin-tagged RNA to streptavidin beads, next, add a cell lysate or protein extract to the mix. The beads will attract and pull the RNA-binding proteins, and the RNA-protein complexes can then be analyzed by western blotting, mass spectrometry, or sequencing.

 

RNA pull-down assay has been used to study a wide range of RNA-protein interactions in plants, including:

  • Interactions between regulatory RNAs and transcription factors: identify transcription factors that bind to microRNAs, long non-coding RNAs, and other regulatory RNAs.
  • Interactions between messenger RNAs and RNA-binding proteins: identify RNA-binding proteins that bind to specific messenger RNAs.
  • Interactions between viruses and host proteins: identify host proteins that bind to viral RNAs.

 

The RNA pull down test is a universal method to study RNA-protein interactions in different types and parts of plant cells. It’s also helpful for analyzing RNA-protein interactions under various circumstances, like stress or growth.

 

Here are some examples of how RNA pull-down assay has been used in plant research:

  • Identify microRNAs that bind to transcription factors that regulate plant growth and development.
  • Identify RNA-binding proteins that regulate the translation of messenger RNAs involved in plant stress responses.
  • Identify host proteins that bind to viral RNAs, which has helped scientists to develop new strategies to control virus infections in plants.

 

RNA pull down assay is a powerful tool that is revolutionizing the way that scientists study RNA-protein interactions in plants. It has the potential to lead to new insights into the biology of plants and to the development of new strategies to improve plant productivity and resilience.

 

Advantages of RNA Pull Down Assay for Plant

  • Sensitivity: sensitive technique to detect even weak RNA-protein interactions.
  • Versatility: study RNA-protein interactions in a variety of plant cell types and tissues, and under different conditions.
  • Specificity: identify the specific proteins that bind to a particular RNA.

 

Challenges of RNA Pull Down Assay for Plant

  • Off-target effects: It is important to design the biotin-labeled RNA probe carefully to avoid off-target binding to other RNAs.
  • False positives: It is also important to control for false positives, such as proteins that bind to the streptavidin beads non-specifically.
  • Optimization: RNA pull-down assay conditions need to be optimized to ensure that the specific RNA-protein interactions of interest are being detected.

 

Conclusion

RNA pull down assay is a useful tool for researching RNA-protein interactions in plants. This method has potential to provide valuable insights into plant biology and improve plant productivity and resilience. However, there are challenges to be aware of, such as off-target effects, false positives, and optimization.

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