The influence of the environment on the functionality of the oxygen-evolving complex (OEC) of photosystem II has long been a subject of great interest. In particular, various water channels, which could serve as pathways for substrate water diffusion, or proton translocation, are thought to be critical to catalytic performance of the OEC. Here, we address the dynamical nature of hydrogen bonding along the water channels by performing molecular dynamics (MD) simulations of the OEC and its surrounding protein environment in the S₁ and S₂ states. Through the eigenvector centrality (EC) analysis, we are able to determine the characteristics of the water network and assign potential functions to the major channels, namely that the narrow and broad channels are likely candidates for proton/water transport, while the large channel may serve as a path for larger ions such as chloride and manganese thought to be essential during PSII assembly.

Since their discovery as impurities in numerous pharmaceuticals beginning in 2018, there has been a strong push to predict and prevent the formation of mutagenic nitrosamines. Several experimental methods, particularly the Nitrosation Assay Procedure, have been developed to predict a molecule's susceptibility to nitrosation. Here, we have compiled the results of hundreds of these experiments from the literature to construct two structure-activity relationship models: a statistical model and an expert rule-based model. The statistical model has been built with graph neural networks and was trained on a dataset of 207 nitrogen-containing molecules. This model makes a binary call for each nitrogen center, predicting if it is likely to be nitrosated or not. Conversely, the rule-based model labels each possible nitrosamine product as one of four categories, ranging from "unlikely" to "very likely". It makes this determination based on 15 rules, which cover 12 deactivating (inhibit nitrosation) and 3 activating (favor nitrosation) features that have been drawn from the literature. Both models perform remarkably well, with accuracies of ∼80%. The rule-based model is generally biased toward favoring nitrosation while the statistical model is more likely to classify an amine as un-nitrosatable due to the makeup of the dataset. Using the models together can balance these biases and further improve the reliability of both.