Machine learning-based linking of bacterial genomes to optimal growth pH: a foundation for rational microbial engineering

Background Bacterial optimal growth pH is pivotal for enzymatic activity, niche adaptation, and synthetic biology applications (e.g., probiotic design, silage fermentation). Traditional experiments are inefficient, resource-intensive, and miss most unculturable taxa, while direct genome-based prediction of this trait remains unavailable—creating a critical genomic-phenotypic gap that hinders microbial engineering. Results We developed BactoGenopH ( http://silagedb.com/BactoGenopH/ ), a web platform for predicting the optimal growth pH of bacteria. We curated a high-quality dataset of 3,476 samples, integrating directly measured pH values from the BacDive database and peer-reviewed literature with corresponding representative genomes from GTDB. Genomic features were extracted via Prodigal for gene prediction and HMMER for Pfam-based functional annotation, with high-importance genes retained and encoded as a binary presence/absence matrix. The XGBoost regression model exhibited robust performance: test set MAE = 0.477, RMSE = 0.666, and 88.82% accuracy (1-pH-unit tolerance); the independent validation set yielded MAE = 0.492, RMSE = 0.694, and 89.37% accuracy. SHAP analysis identified key pH-adaptation genes (e.g., Na_Ala_symp, MgtE) with well-documented roles in ion transport and pH homeostasis. The freely accessible platform supports real-time predictions via FASTA sequence input or file upload, complemented by data visualization and curated dataset browsing. Conclusion BactoGenopH fills the unmet need for direct, phenotype-grounded bacterial optimal growth pH prediction, bridging genomic-phenotypic gaps with robust performance. This free resource accelerates trait-driven microbial research and supports rational microbial engineering.