ReviewBiochemical Society transactions2026
Mimetics of in-cell and subcellular crowding and solvation for protein folding.
Review in Biochemical Society transactions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Historically, fundamental principles of protein folding were extracted from dilute in vitro experiments that disregarded the complexity of the cell interior. It is now well-established that the cellular environment modulates protein behaviors. Discrepancies between protein properties measured in vitro and in-cell can be disentangled using mimetics that are designed to reproduce cellular interactions in vitro, steric crowding interactions and non-steric sticking interactions. Here, we review recent advances in the development and application of cellular mimetics of in-cell protein folding, with a focus on replicating diverse cell types and cellular compartments. Steric crowding interactions are typically mimicked using inert polymers; coupling these with giant unilamellar vesicles or phase separation allows for the creation of a cell- or organelle-like environment. Mimetics of non-steric chemical interactions must incorporate features of the chemical environment being mimicked. These range from buffers containing physiological concentrations of salt and small molecules to dilute lysates derived from the relevant cell type and/or organelle. Such mimetics of steric and non-steric interactions have greatly aided our understanding of in-cell protein folding. Mimetics can further approach biological accuracy through mixtures that simultaneously account for steric and non-steric interactions. Mimetic mixtures are important because they provide a convenient and cost-effective means to predict protein behavior in diverse cellular environments, which may benefit high-throughput applications, such as screening therapeutic candidates or training machine learning-based in-cell protein structure prediction models.
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