Quantum Zeno Effect Permits Magnetosensitivity in Lipid Peroxidation despite Fluctuating Inter-Radical Coupling

量子芝诺效应使得脂质过氧化反应在自由基间耦合波动的情况下仍能表现出磁敏感性

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Abstract

Lipid peroxidation, the oxidative degradation of lipids, occurs via a complex radical chain reaction and is central to biological processes such as cellular aging and ferroptosis. It is also linked to numerous pathologies, often marked by excessive oxidative stress. Growing evidence implies that lipid peroxidation is sensitive to weak magnetic fields, with the radical pair mechanism (RPM) proposed as a possible explanation. Previous studies have demonstrated these effects with simple models but have yet to evaluate the efficacy of the RPM under biologically realistic conditions. For a complete picture, models must include the effects of strong inter-radical interactions, which typically suppress magnetic field sensitivity and expedite spin relaxation. Using Brownian dynamics-informed spin dynamics calculations, we investigate the impact of dynamic inter-radical dipolar coupling on the predicted magnetic field sensitivity of the chain termination reaction. We find that weak magnetic field effects persist despite strong, fluctuating dipolar interactions, provided that the spin-selective radical recombination is sufficiently fast to induce the quantum Zeno effect. Under such conditions, the recombination quantum yield exhibits a strong dependence on the recombination rate constant, with certain rates giving rise to low-field effects, while others enhance high-field sensitivity or eliminate the magnetic responsiveness altogether. At high magnetic fields, spin relaxation driven by g-matrix anisotropy dominates, potentially leading to pronounced magnetosensitivity for fast recombination processes. Overall, our results demonstrate that magnetic field effects are viable in strongly coupled radical pairs within biological membranes, given appropriate dynamical and kinetic constraints, and highlight the potential for broader magnetosensitivity in confined, low-mobility biological environments than previously anticipated based on standard RPM models.

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