Buhr, J. | Search bioRxiv

bioRxiv2025

Riboflavin metabolism shapes FSP1-driven ferroptosis resistance

Membrane protection against oxidative insults is achieved by the concerted action of glutathione peroxidase 4 (GPX4) and endogenous lipophilic antioxidants such as ubiquinone and vitamin E. Deficiencies in these protective systems lead to an increased propensity to phospholipid peroxidation and ferroptosis. More recently, ferroptosis suppressor protein 1 (FSP1) was identified as a critical ferroptosis inhibitor acting via regeneration of membrane-embedded antioxidants. Yet, regulators of FSP1 are largely uncharacterised, and their identification is essential for understanding the mechanisms buffering phospholipid peroxidation and ferroptosis. Here, we conducted a focused CRISPR-Cas9 screen to uncover factors influencing FSP1 function, identifying riboflavin (vitamin B2) as a new modulator of ferroptosis sensitivity. We demonstrate that riboflavin, unlike other vitamins that act as radical-trapping antioxidants, supports FSP1 stability and the recycling of lipid-soluble antioxidants, thereby mitigating phospholipid peroxidation. Furthermore, we show that the riboflavin antimetabolite roseoflavin markedly impairs FSP1 function and sensitises cancer cells to ferroptosis. Thus, we uncover a direct and actionable role for riboflavin in maintaining membrane integrity by promoting membrane tolerance to lipid peroxidation. Our findings provide a rational strategy to modulate the FSP1-antioxidant recycling pathway and underscore the therapeutic potential of targeting riboflavin metabolism, with implications for understanding the interaction of nutrients and their contributions to a cells antioxidant capacity.

cell biology↗

bioRxiv2025

KIMMDY: a biomolecular reaction emulator

Molecular simulations have become indispensable in biological research. Their accuracy continues to improve, but directly modelling biochemical reactions - central to all life processes - remains computationally challenging. Here, we present a biomolecular reaction emulator that models reactions across conformational ensembles using kinetic Monte Carlo. Our method, KIMMDY, is capable of handling dynamic, large-scale systems with successive, competing reactions, even on the second timescale or slower. It leverages graph neural networks for large-scale prediction of reaction rates, while also being capable of using simpler physics-based or heuristic models. We validate our approach against experimental data and showcase its power and versatility through a series of applications, including radical reactions, nucleophilic substitutions, and photodimerization. Example systems span proteins and DNA. KIMMDY aids the understanding of biochemical reaction cascades in complex systems, helps to re-interpret experimental data, and can inspire future wet-lab experiments.

biochemistry↗