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Nevin Manimala Statistics

Ensemble-Biased Disulfide Chemistry Orchestrates the Multidimensional Gluten Network Topology

J Am Chem Soc. 2026 Jul 15. doi: 10.1021/jacs.6c07565. Online ahead of print.

ABSTRACT

Mixing wheat flour with water transforms an otherwise dispersible powder into a cohesive and extensible dough capable of sustaining large deformations. The emergence of this malleable material is due to polymerization of glutenin subunits. However, the molecular principles governing this transformation remain unclear. Here, we investigate the hydration-induced association behavior of the N-terminal domain of the high-molecular-weight glutenin subunit 1Dx5 (1Dx5-NTD). Upon hydration, 1Dx5-NTD adopts a collapsed, heterogeneous ensemble rather than a single, well-defined folded structure and undergoes intermolecular association into assemblies ranging from trimers to ∼70,000-mers. These associations are primarily determined by favorable enthalpic interactions that are modulated by moderate ionic strength. Hydrophobic residues (Tyr52, Phe65, Tyr66, and Trp82) are buried within the collapsed monomer yet indirectly modulate protein association through correlated ensemble dynamics, as supported by mutational analysis. Importantly, 1Dx5-NTD contains three cysteine residues (Cys10, Cys25, and Cys40) that undergo disulfide bond formation during association. While multiple disulfide linkage patterns are chemically possible, mass spectrometry-based cross-linking analysis reveals a limited subset of preferential disulfide linkages. This finding suggests that disulfide formation is biased by the conformational ensemble and local cysteine accessibility, rather than occurring randomly. Together, these results support a model in which glutenin polymerization emerges from ensemble-averaged intermolecular contacts coupled with statistically biased disulfide bond formation, giving rise to a cohesive, multidimensional protein network without requiring a uniquely defined folded structure. This work highlights how collapsed protein ensembles can encode chemical selectivity and macroscopic material properties.

PMID:42456183 | DOI:10.1021/jacs.6c07565

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