Abstract
Molecular beacons are nucleic acid oligomers labeled with a fluorophore and a quencher that fold in a hairpin-shaped structure, which fluoresce only when bound to their target RNA. They are used for the visualization of endogenous mRNAs in live cells. Here, we report a Python program (PinMol) that designs molecular beacons best suited for live cell imaging by using structural information from secondary structures of the target RNA, predicted via energy minimization approaches. PinMol takes into account the accessibility of the targeted regions, as well as the inter- and intramolecular interactions of each selected probe. To demonstrate its applicability, we synthesized an oskar mRNA-specific molecular beacon (osk1236), which is selected by PinMol to target a more accessible region than a manually designed oskar-specific molecular beacon (osk2216). We previously demonstrated osk2216 to be efficient in detecting oskar mRNA in in vivo experiments. Here, we show that osk1236 outperformed osk2216 in live cell imaging experiments.