Back

Localisation of Apicomplexa motor Myosin A with axial nanometric precision using graphene energy transfer

Ferrari, G.; Song, Y.; Hensel, M.; Psathaki, O. E.; Nguyen Nguyen, D. T.; Gras, S.; Meissner, M.; Periz, J.; Tinnefeld, P.

2025-08-01 biophysics
10.1101/2025.07.30.667387 bioRxiv
Show abstract

Migration in Apicomplexan parasites, a phylum that includes Plasmodium spp. responsible for malaria and the zoonotic Toxoplasma, is explained by the linear migration motor model. This model predicts that Myosin A is confined beneath the plasma membrane and above the inner membrane complex (IMC), a membranous barrier separating the cell membrane and the cytoplasm. Cumulative data, using mutant cell lines and biochemical data support this model. Paradoxically, a proof of the precise localization of Myosin A is still lacking due to limitations in resolving the motor with nanometric precision within the space comprising the membrane and the IMC. Here, we implement graphene energy transfer (GET), a novel axial nanometric ruler with a resolution of [~]1 nm, to determine the relative axial position of Myosin A and IMC1 (a reference protein defining IMC position). Using GET, we estimate the IMC dimensions with precision matching electron microscopy (EM) data and the added advantage of identifying specific bauplan proteins. We complement these measurements with 2D STED microscopy in MIRA confiners, uExMIC, and cryo-immunolabeling. Our data present the first direct localization of Myosin A populations with nanometric resolution in the third dimension, which is compatible with the linear motor model. One sentence summaryUsing the novel GET method, this study presents the first direct localization of Myosin A populations in Toxoplasma gondii with 1-4 nm axial precision, supporting the linear model.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.