bioRxivBiochemistryPreprint
A tunable aqueous architecture modulates functionaloutput in biomolecular condensates
Moeka SasazawaiD, Mechi Chen, Rui ZengiD, Uvarov Denis, Sujata Bais, Jillian Hoffstadt, Julian von HofeiD, Norah HoffmanniD, Yulia A. VolkovaiD, Saumya SaurabhiD
Abstract
Biomolecular condensates organize cellular biochemistry, yet the principles governing their internal solvent architectures remain poorly understood. Most current models focus on macromolecular scaffolds while treating the solvent as a passive, spatially uniform background. Here, we introduce Condensate Spatial Topography via Emission Lifetimes (ConSTEL) to map the continuous solvent polarity landscape inside biomolecular condensates. Using PopZ as a model system, we show that the condensate interior contains a persistent, tunable mosaic of aqueous environments whose apparent polarity, reported by Nile Red fluorescence lifetimes, is organized by thermodynamic state and chemical cues. This microphase-separated solvent architecture defines distinct mesoscale rheological regimes, with intermediate aqueous niches supporting fast, confined tracer motion and highly polar or non-polar extremes forming a slower, viscoelastic mesh. We further demonstrate that drug-like small molecules partition non-uniformly across this landscape according to their physicochemical properties, and that exceeding local solubility limits drives "reciprocal sculpting", in which mismatched guests remodel the host solvent architecture. Together, these results highlight internal solvent organization as an active, tunable determinant of condensate material properties, molecular transport, and partitioning, and suggest that predictive models of condensate function and pharmacology would benefit from incorporating the spatial arrangement of solvent environments alongside bulk composition.
- assay: proteomics
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 0
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