Emerging experimental and computational methods for studying redox-regulated structural transitions.
TL;DR
A comparative overview of dual experimental and computational advancements is provided and how the integration of generative diffusion models could facilitate the real-time simulation of conditional, multi-state structural ensembles across the redox proteome is highlighted.
Abstract
Thiol-based redox switches utilize the unique nucleophilicity of cysteine and selenocysteine to dynamically link real-time cellular redox fluctuations to metabolic regulation and signaling pathways. Capturing the precise atomic-level thermodynamic and kinetic mechanisms driving these oxidative modifications has long been limited by the chemical instability of transient intermediates and the immense computational costs of classical molecular dynamics simulations. However, recent advancements in chemoselective small-molecule probes now allow for the high-purity trapping and enrichment of specific sulfenic and sulfinic acid states. In parallel, a paradigm shift toward machine learning, graph neural networks, and protein language models has bypassed traditional computational bottlenecks, enabling high-throughput, proteome-wide predictions of redox switches in seconds. Furthermore, emerging data reveal that these redox modifications do not merely alter well-structured proteins, but actively dictate conditional folding transitions and structural transformations within intrinsically disordered proteins and biomolecular condensates. Here, we provide a comparative overview of these dual experimental and computational advancements and highlight how the integration of generative diffusion models could facilitate the real-time simulation of conditional, multi-state structural ensembles across the redox proteome.
