PinMol

PinMol designs molecular beacons for live-cell imaging of endogenous mRNAs by using RNA secondary-structure predictions and accessibility analysis to optimize probe binding.

Key Features:

• Python implementation: PinMol is implemented in Python.
• Molecular beacon composition: Designs hairpin oligonucleotides bearing a fluorophore and a quencher that fluoresce upon binding to target RNA.
• Secondary-structure-based design: Leverages RNA secondary-structure predictions derived from energy minimization approaches to guide probe selection.
• Accessibility analysis: Evaluates accessibility of targeted RNA regions to identify suitable binding sites for molecular beacons.
• Interaction assessment: Considers inter- and intramolecular interactions of probe candidates to enhance specificity and stability.

Scientific Applications:

• Live-cell mRNA imaging: Supports design of probes for visualization of endogenous mRNAs in live cells to study gene expression dynamics and cellular processes.
• Case study — oskar mRNA: Used to design osk1236, a molecular beacon targeting oskar mRNA that selected a more accessible region and outperformed the manually designed osk2216 in live-cell imaging experiments.

Methodology:

Computational predictions of RNA secondary structures are generated and analyzed to identify regions of high accessibility, and potential probes are evaluated based on predicted inter- and intramolecular interactions.