Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms.

Cultivating the vast majority of uncultured microbes requires knowledge of their physiological preferences, particularly optimal growth temperature (OGT). We present a machine learning approach that utilizes protein domain frequencies from bacterial genomes to predict OGT across a wide continuous range (1–83 °C). Our Random Forest model, trained on a dataset of 1,498 genomes, achieved high predictive accuracy (R²=0.853 on test data, 82.4% of predictions within a ± 10 °C error margin), substantially advancing current capabilities and offering a practical tool to guide cultivation experiments. Analysis of the model identified key protein domain signatures associated with thermal adaptation. The enrichment of domains related to polyamine metabolism, the tRNA methyltransferase family, and CRISPR-Cas systems was positively correlated with higher OGTs, providing genomic evidence for their roles in thermotolerance. Conversely, domains involved in redox homeostasis, transport, and nucleic acid binding were more abundant at lower temperatures. These findings not only facilitate targeted cultivation efforts but also deepen our understanding of the molecular strategies bacteria employ to thrive across diverse thermal niches.