Predicting FOX gene candidates for oxic nitrogen fixation using multi-omic machine learning and comparative bioinformatics.

Nitrogen fixation in oxygenic cyanobacteria depends on a system of genes that protect oxygen-sensitive nitrogenase, many of which likely remain uncharacterized. Here we predict FOX (fixation in the presence of oxygen) gene candidates in Anabaena sp. PCC 7120 by integrating nitrogen step-down RNA-seq (0/6/12/21 hours), quantitative proteomics, promoter architecture, genomic context, and reciprocal-best-hit conservation across diazotrophic and non-diazotrophic cyanobacteria. Using 68 literature-validated FOX genes and 835 conserved non-essential genes as a proxy negative class, we trained logistic regression, Random Forest, and XGBoost models and evaluated them using 20 repeated stratified 80/20 train–test splits. The best models achieved ROC–AUC up to 0.80 and average precision up to 0.55 and precision among the top 20 ranked genes reached 0.39 versus a 0.075 prevalence baseline. Model interpretation highlights late step-down induction, diazotroph-biased conservation, and genomic neighborhood signals as leading predictors. We generated genome-wide FOX probability scores used primarily for candidate ranking, nominating conserved genes spanning heterocyst envelope processes as well as broader redox, metabolism, and electron-pool regulation. We release these predictions and a public web-based optimizer that applies comparative-bioinformatics filters and size constraints to propose candidate accessory-gene complements for experimental testing and heterologous reconstitution efforts.