Coria, J. | bioRxiv Science

bioRxiv2026

Structural insights into MBOAT2 catalysis, product retention, and ligand exchange

MBOAT2 suppresses ferroptosis independently of GPX4 and FSP1 by transferring monounsaturated acyl chains from acyl-CoA donors to lysophospholipid acceptors, but the structural basis of its catalytic cycle remains unclear. Here, we report the first cryo-EM structures of human MBOAT2, capturing endogenous and substrate-induced ligand-bound states. Unexpectedly, as-purified MBOAT2 contains a co-purified phospholipid-like density consistent with a retained product, together with a second density at a putative acyl-donor entry site. Oleoyl-CoA addition reduces the ordered product-like density and reveals donor density, whereas LPE addition increases local heterogeneity near the archway. The inactive H373A mutant contains endogenous donor- and acceptor-like densities along the two access pathways, consistent with substrate preloading. Together, these structures define the catalytic architecture of MBOAT2, support a product-retained, donor-primed working model, and provide templates for structure-guided ligand discovery.

biophysics↗

bioRxiv2023

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms

Environmental safety assessments, as mandated by many regulations, require that toxicity data is generated for up to three trophic levels, algae, aquatic invertebrates, and fish. Conducting these tests in vivo is resource-intensive, time-consuming, and causes undue suffering. Computational methods are fast and cost-efficient alternatives, however, their adaptation in regulatory settings has been slow, both due to low accuracy and narrow applicability domains. Here we present a new method for predicting chemical toxicity based on molecular structure. The method is based on a transformer, capturing structural features associated with toxicity, followed by a deep neural network that predicts the corresponding effect concentrations. After training on data from tens of thousands of exposure experiments, the model shows high predictive performance for each of the three trophic levels. Compared to commonly used QSAR methods, the model has both a larger applicability domain and a considerably lower error. In addition, training the model on data that combines multiple types of effect concentrations further improves the performance. We conclude that transformer-based models have the potential to significantly advance computational predictions of chemical toxicity and make in silico approaches a more attractive alternative when compared to animal-based exposure experiments.

pharmacology and toxicology↗