Graph neural network modeling of receptor interaction kinetics from single-molecule imaging data

Authors

Nguyen, K., Jaqaman, K.

Abstract

Single-molecule (SM) imaging (SMI)-based approaches have the powerful ability to capture receptor interactions, which are necessary for cell signaling, in their native live-cell environment. Yet, due to substoichiometric labeling, SMI generally provides only partial information on these interactions. We developed Deep-FISIK, which utilizes graph neural networks and multi-head attention for message-passing, to predict from SMI data the kinetics of homotypic interactions of the full receptor system. The input to Deep-FISIK are the SM detections in SMI experiments, without the need for explicit tracking. Thus, Deep-FISIK is compatible with labeling a higher fraction of receptors in the SMI experiments, increasing the prediction accuracy of the interaction kinetics parameters. The performance of Deep-FISIK is robust in the presence of a variety of deviations from the training data, indicating the applicability of Deep-FISIK to many receptor systems and SMI experiments.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 09 Jul 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.