Atomistic simulations and machine learning approaches to investigate bromoform interactions with cell membranes: Implications for seaweed-based methane emission reduction

Abstract

Livestock methane emissions contribute critically to global warming. A promising mitigation strategy is the incorporation of seaweed in animal diets, leveraging the seaweed natural production of bromoform, a methanogenesis inhibitor. To boost this approach, it has been proposed to bioengineer seaweed to enhance both bromoform production and storage in specific organelles. For this, it is critical to understand bromoform behavior and interactions with cellular membranes at atomistic resolution. We used full-atomistic models and simulated bromoform with a POPC bilayer at six different concentrations by generating the CHARMM format forcefield files based on published parameters of pure, liquid bromoform. Our simulations reveal that bromoform penetrates the bilayer at all studied concentrations and tends to aggregate outside the membrane at high concentrations. Bromoform presence does not alter the membrane thickness or lipid order parameters, regardless of its aggregation or penetration. On the other hand, at high concentrations bromoform aggregates influence membrane curvature. By calculating the free energy profile and the diffusion coefficient of bromoform within the membrane, we observe that when the bromoform molecules penetrate the membrane, they localize in the hydrophobic core and exhibit slow diffusion along the membrane normal axis compared to that exhibited along the membrane plane. Finally, we employed general local-atomic descriptors with an unsupervised machine-learning technique and demonstrated the local structural resemblance of bromoform between its pure liquid state and its aggregated state in aqueous environment with and without the POPC bilayer. This study sheds light on the interactions of bromoform in a biological environment, paving the way for future endeavors to optimize bromoform production and storage in seaweed, which could significantly contribute to more effective methane emission reduction strategies.