Physical principles of building protein megacomplexes in a crowded milieu

TL;DR

This paper introduces a statistical physics framework to understand how protein megacomplexes assemble in crowded cellular environments, revealing the role of divergent subunits and excluded volume effects in complex formation.

Contribution

It presents a novel grand canonical ensemble approach to model protein interactions and megacomplex assembly based on mass spectrometry data, advancing understanding of cellular architecture.

Findings

Divergent proteins orchestrate assembly beyond nearest neighbors.

Excluded volume effects influence cluster formation and architecture.

Framework offers mechanistic insights into protein complex remodeling.

Abstract

Multiple phenotypic protein expressions arising from one genome represent variations in the protein relative abundance and their stoichiometry. A lack of definite compositional parts challenges the modeling of protein megacomplexes and cellular architectures. Despite the advances in protein structural predictions with AI, the mechanism of protein interactions and the emergence of megacomplexes they assemble remains unclear. Here, we present a statistical physics framework of grand canonical ensemble to explore the protein interactions that drive the emergent assembly of a megacomplex using the observational mass spectrometry datasets including protein relative abundance and the cross linked connections. Using chromatin remodeler megacomplex, INO80, as an example, we discovered a class of divergent protein that plays a critical role in orchestrating the assembly beyond nearest neighbors, dependent on the excluded volumes exerted by others. With the constraints of the excluded volumes by varying crowding contents, these divergent subunits orchestrate and form clusters with selective components growing into configurationally distinct architectures. We propose a machinery view for the INO80 chromatin remodeler complex where each loosely associated subunits can be occasionally recruited for parts as attachment into a core assembly driven by excluded volumes. Our computational framework provides a mechanistic insight into taking the macromolecular crowding as necessary physicochemical variables representing cell states to remodel the configurations of protein megacomplexes with structurally loose modules.