Abstract
Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.
Highlights
Living cells can contain on the order of 104 [1] distinct types of proteins and other macromolecules at a given time
We used our self-consistent algorithm (Algorithm 1) to compute {μα} for each cell type at three different volume fractions φ = 0.1, 0.2, and 0.3. These three volume fractions represent the range in the fraction of macromolecular volumes in a cellular volume where macromolecular crowding [14] affects protein dynamics [12,48] and assemblies
As the results from the previous section showed that μB(φ) behaves differently from the other subunits, we focus our attention to subunit B
Summary
Living cells can contain on the order of 104 [1] distinct types of proteins and other macromolecules at a given time In this many-component mixture environment, macromolecules like proteins fold, unfold, and assemble into complexes and organize hierarchically into spatial networks [2,3,4]. The specific arrangements of macromolecules are thought to emerge from the vast amount of weak “quinary” and entropic interactions [7,8,9,10] Of these types of interactions, the most intuitive conception of protein biophysics in this crowded environment is that of volume exclusion [11,12,13] exerted on a given protein by surrounding macromolecules, so called the macromolecular crowding effects [14]. Proteins interact weakly and form higher-order complexes [15] through quinary interactions [16], where counteracting forces
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