Beyond the annotated: protein foundation models enable robust prediction of microbial root competence

Background Root competence, the ability of soil bacteria to establish and grow on plant roots, is a key ecological trait influencing plant nutrition, growth, and health. However, identifying genomic determinants of root competence across bacteria remains challenging, in part because model generalisability depends strongly on how genomes are represented. Traditional approaches based on curated annotations are incomplete and biased toward well-characterised organisms and functions, limiting generalisation. Sequence-similarity clustering improves coverage but yields high-dimensional features relative to dataset size, hindering training. Foundation models offer an alternative by learning compact representations without relying on prior annotation. Results Here, we compared pretrained genome representations from protein and DNA foundation models (ESM-2, Bacformer, DNABERT-S) with annotation- and clustering-based features (KEGG orthology, OrthoFinder protein families) for predicting root competence using synthetic microbial community data from Arabidopsis thaliana and assessed generalisability across bacteria. When training and test sets contained taxonomically related bacteria, most approaches performed similarly. However, when test bacteria belonged to phyla entirely absent from training, reflecting high evolutionary separation across all levels of bacterial classification, only pretrained protein representations retained predictive performance. Bacformer-derived representations, which incorporate genomic context, supported the strongest generalisation, suggesting that conserved genomic organisation contributes to predicting root competence. Feature attribution quantifying protein contributions to model decisions linked root competence to TonB/SusD-dependent receptors, small-molecule transporters, and unannotated proteins with conserved regulatory motifs and homology to carbon starvation-response loci. Conclusions Protein foundation models support generalisation across evolutionarily distant bacteria and identify genomic determinants of root competence, including unannotated proteins.