Machine Learning Reveals Proteome-Encoded Growth Predictors of <i>Rhodopseudomonas palustris</i> CGA009 on Lignin Aromatics.

ABSTRACT: Microbial utilization of lignin-derived aromatics requires extensive metabolic flexibility, yet growth outcomes vary sharply with substrate chemistry and oxygen availability. Whether this variability reflects distinct growth programs or alternative realizations of shared biochemical constraints remains unclear. Here, we combine quantitative proteomics with cross-condition machine learning to test whether growth-rate variation in Rhodopseudomonas palustris can be predicted directly from proteome composition and to identify the proteomic features that consistently encode growth potential across environments. Using OmniProt, a cross-condition neural modeling and interpretive framework, we predicted growth rates across 16 lignin-derived substrate-oxygen combinations, including held-out

SUBMITTER: Osinuga A 

PROVIDER: S-EPMC12776462 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature