PinMol: Python application for designing molecular beacons for live cell imaging of endogenous mRNAs

Area of Science:

• Molecular Biology
• Bioinformatics
• Cell Imaging

Background:

• Molecular beacons are fluorescent probes for visualizing RNA in live cells.
• Designing effective molecular beacons requires considering RNA accessibility and probe interactions.
• Current design methods may not fully optimize beacons for live cell imaging.

Purpose of the Study:

• To develop a Python program (PinMol) for designing superior molecular beacons for live cell imaging.
• To utilize RNA secondary structure prediction and accessibility analysis in molecular beacon design.
• To compare the performance of a PinMol-designed beacon against a manually designed one.

Main Methods:

• Development of the PinMol Python program incorporating energy minimization for RNA structure prediction.
• Analysis of target RNA accessibility and probe inter/intramolecular interactions.
• Synthesis and live cell imaging experiments using a PinMol-designed oskar mRNA beacon (osk1236) and a manually designed beacon (osk2216).

Main Results:

• PinMol successfully designed an oskar mRNA-specific molecular beacon (osk1236) targeting a more accessible region.
• The PinMol-designed osk1236 beacon demonstrated superior performance in live cell imaging compared to the manually designed osk2216 beacon.
• The study validates PinMol's approach for optimizing molecular beacon design.

Conclusions:

• PinMol is an effective computational tool for designing high-performance molecular beacons for live cell imaging.
• Optimizing molecular beacon design based on RNA accessibility significantly enhances imaging efficiency.
• This approach advances the capability for visualizing endogenous mRNAs in live cells.