Learning interpretable kinetic models for biomolecular interaction networks

Many cellular machines operate through weak, transient, and multivalent interactions whose functional states are governed by recurring interaction patterns rather than persistent molecular geometries. Here, we introduce interaction-based Markov state models (iMSMs), which construct interpretable kinetic models using unsupervised clustering of time-averaged, identity-resolved interaction distributions around a focal entity. Applied to nucleocytoplasmic transport at two molecular resolutions, iMSMs resolve graded interaction states spanning strong, partial, and weak engagement. During pore transport, partially engaged states provide faster routes to disengagement than strongly bound states, while the networks reveal transport pathways, interaction hubs, bottlenecks, and kinetic commitment. At a finer resolution, FG motifs exchange contacts while remaining associated, recovering established slide-and-exchange dynamics. iMSMs reproduce free energies, permeabilities, and multi-step kinetics, with nearly fourfold faster permeability convergence from truncated trajectories than direct transport-event counting. Thus, iMSMs connect rapidly exchanging contacts to graded interaction states and their kinetics across molecular resolutions.