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Webb, M. A.

Publications and source records attributed to Webb, M. A..

2 recordsLinked to original sources

Coarse-grained Modeling of Stress Granule Structure and Dissolution with Small-molecule Compounds

Stress granules are biomolecular condensates composed of RNA and proteins that form in response to stress; their dysregulation is implicated in neurodegenerative diseases. In this study, we develop a minimal stress-granule model, composed of RNA and six key proteins associated with neurodegenerative conditions, and study its characteristics using coarse-grained molecular dynamics simulations. We find that RNA is essential to form stable condensates in these biopolymer mixtures, while underlying protein-protein interactions result in heterogeneous, multi-phasic architectures. Inspired by therapeutic applications, we then challenge the stability of these condensates in the presence of twenty distinct small molecules. Simulation-derived properties classify compounds as "dissolving" or "non-dissolving" with 85% agreement with experimental findings. Further analysis suggests that dissolving compounds disrupt stress granule structure by preferentially associating with RNA and stripping the scaffold that maintains its multiphasic architecture. These insights advance understanding of stress granule stability and demonstrate modeling strategies for screening of therapeutic candidates.

biophysics↗

Active learning of the thermodynamics-dynamics tradeoff in protein condensates

Phase-separated biomolecular condensates exhibit a wide range of dynamical properties, which depend on the sequences of the constituent proteins and RNAs. However, it is unclear to what extent condensate dynamics can be tuned without also changing the thermodynamic properties that govern phase separation. Using coarse-grained simulations of intrinsically disordered proteins, we show that the dynamics and thermodynamics of homopolymer condensates are strongly correlated, with increased condensate stability being coincident with low mobilities and high viscosities. We then apply an "active learning" strategy to identify heteropolymer sequences that break this correlation. This data-driven approach and accompanying analysis reveal how heterogeneous amino-acid compositions and non-uniform sequence patterning map to a range of independently tunable dynamical and thermodynamic properties of biomolecular condensates. Our results highlight key molecular determinants governing the physical properties of biomolecular condensates and establish design rules for the development of stimuli-responsive biomaterials.

biophysics↗