Neurons can maintain proteostasis by extruding protein aggregates and damaged organelles via large extracellular vesicles called exophers. However, the biophysical rules governing exopher cargo selection remain poorly understood. Here, we discovered that biomolecular condensates can localize to exophers and investigated whether condensate material properties—ranging from liquids to dynamically arrested gels—determine their recruitment to exophers. By reprogramming interactions in a multidomain protein, we tuned condensate properties and linked them to neuronal exopher recruitment using a multidisciplinary approach combining in vivo dynamics, in vitro material properties, and molecular dynamics simulations. Our findings reveal that gel-like condensates with slower dynamics and higher viscoelasticity localize to exophers more avidly than dynamic liquid-like condensates, demonstrating a strong correlation between condensate dynamics and exopher recruitment. This work lays the framework for investigating the biophysical determinants of exopher cargo selection, providing critical insights into the influence of proteome material state on neuronal protein quality control. Graphical Abstract
Mayur Barai, E. Chuang, Shuo-Lin Weng et al.· bioRxiv· 0 citations
Biomolecular condensates exhibit spontaneous electrochemical microenvironments characterized by asymmetric ion distributions and pH gradients that emerge from protein-sequence-dependent charge regulation. Despite their biological importance, mechanistic understanding of these microenvironments has been constrained by the absence of computationally tractable frameworks capable of treating proton exchange, counterion partitioning, and buffer equilibria on consistent thermodynamic footing. Here, we introduce the buffered Charge-Regulation Monte Carlo (b-CR-MC) framework, which couples grand-canonical exchange of ions and buffer species with explicit charge regulation of titratable residues. By extending the CR-MC ion-merging strategy to multicomponent reservoirs and employing the Restricted Primitive Model, b-CR-MC achieves computational efficiency while maintaining thermodynamic rigor, with quantitative agreement to the more expensive generalized G-RxMC approach. Applied to full-length FUS (net positive) and PGL-3 (net negative) under physiological conditions, the framework reveals sequence-dependent pH gradients: the dense phase of FUS exhibits an alkaline shift, while PGL-3 exhibits an acidic shift, in both cases driving the condensate interior toward the protein’s isoelectric point. Slab-geometry simulations further resolve the Donnan potential and continuous ion profiles across the condensate interface, confirming the direction and magnitude of these electrochemical shifts. Additionally, we identify spatially resolved buffer depletion within dense phases, establishing that dynamic charge regulation is a primary determinant rather than a secondary correction to condensate electrochemistry. By establishing a sequence-resolved, thermodynamically consistent computational platform, b-CR-MC enables quantitative prediction of how mutations and post-translational modifications reprogram condensate microenvironments across biological and pathophysiological contexts.
Shuo-Lin Weng, Shiv Rekhi, Young C. Kim et al.· bioRxiv· 0 citations
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