Inferring the Hierarchical Order in Macromolecular Spatial Organization Across Cell Phenotypes from Proteome Datasets Using a Grand Canonical Framework

The intracellular milieu is highly dynamic, where the proteome undergoes spatial organization that reflects cell phenotypes. Understanding the mechanism driving membraneless compartment formation is always challenging. Macromolecular features such as valency and intrinsic disorder are known to influence cluster formation through phase separation. However, principles underlying the spatial gathering of the proteome with heterogeneous abundance and interactions across diverse cellular environments remain unclear. Here, we present a computational framework that integrates proteome datasets to reveal the hierarchical order of subunits driving protein assemblage with varying crowding conditions. Using yeast INO80 as an example, we deployed a computational approach to model the assembly of the chromatin remodeler complex from its constituent subunits. By parameterizing the interaction energies between subunits and chemical potential from the mass spectrometry-based data, we observed the emergence of macromolecular clusters with a grand canonical coarse-grained model. We observed two distinctive groups of subunits characterized by their contribution to cluster formation -- “divergent” and “convergent”. The former induces nucleation and densifies the nucleus, while the latter participates in the cluster without causing global rearrangement. Secondly, subunit characteristics, along with composition and the size of the emergent cluster, vary with crowding. We provide an agonistic framework connecting mass spectrometry with visual proteome, enabling insights into the physical principles governing proteome spatial organization across diverse cellular environments.