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

Abstract

Single-molecule (SM) imaging (SMI)-based approaches have the powerful ability to capture receptor interactions - 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. Deep-FISIKs performance is robust in the presence of a variety of deviations from the training data, indicating Deep-FISIKs applicability to many receptor systems and SMI experiments.