Understanding protein-complex assembly through grand canonical maximum entropy modeling

Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these vary with cell types. While some protein complexes form putative structures that can be visualized imaging, there are far more yet to solved because their dynamic associations one another. Yet, it is possible infer through physical model. However, often not clear physicists what kind data from biology necessary for such modeling endeavor. Here, we aim model clusters coarse-grained assemblies multiple subunits constraints interactions among and chemical potential each subunit. We obtained on known structures. inferred potential, dictates particle number distribution subunit, knowledge experimental data. Guided by maximum entropy principle, formulate an inverse statistical mechanical method numbers as potentials grand canonical multi-component mixture. Using Monte Carlo simulations, captured high-order complex Succinate Dehydrogenase (SDH) four subunits. The complexity hierarchical varies relative subunit distinctive types lung, heart, brain. When crowding content increases, observed stabilizes emergent do exist dilute conditions. We, therefore, proposed testable hypothesis molecular scale plausible biomarker predicting phenotypes cell.