Modeling Mutation-Dependent Phase Separation of SARS-CoV-2 Nucleocapsidprotein and Full-Length gRNA at the Viral Scale

Abstract

The phase separation between viral nucleoproteins (NP) and full-length genomic RNA (gRNA) plays a crucial role in the assembly and replication of SARS-CoV-2 via liquid-liquid phase separation (LLPS). However, the impact of different types of mutations on this process at the viral scale remains poorly understood. Here, we develop a coarse-grained sticker-spacer model to represent the interactions between SARS-CoV-2 NP and full-length gRNA, and employ lattice Monte Carlo simulations to systematically investigate their phase separation behavior. Our model captures the essential features of multivalent interactions and allows for the explicit incorporation of various mutation types. By integrating state-of-the-art AlphaFold structure predictions with machine-learning regression, we derive sequence-specific coarse-grained interaction matrices that faithfully encode protein–RNA binding heterogeneity, enabling simulations that quantitatively reproduce experimental observables such as condensate size distributions and kinetic convergence trends. Simulation results reveal that specific mutations in the NP or gRNA sequences can significantly modulate the kinetic behaviors, condensate morphology, and the overall propensity for phase separation. Notably, certain mutations enhance the driving force for condensate formation, while others disrupt the multivalent network, leading to reduced phase separation. These findings provide mechanistic insights into how sequence variations may regulate viral assembly and offer a computational framework for predicting the effects of emerging mutations on SARS-CoV-2 genome packaging and virion-assembly. Beyond this specific system, the methodological framework developed here is readily transferable to studies of biomolecular phase separation in other biological LLPS scenarios.

Date of Award	2025
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Haibin SU (Supervisor)