GEM-Reasoner: Protein Function Reasoning for Genome-Scale Metabolic Model Reconstruction

Genome-scale metabolic models (GEMs) provide a computational foundation for systems biology and rational chassis design. Existing methods for automatically reconstructing GEMs from genome sequences typically transfer functional annotations from reference proteins based on sequence or protein-embedding similarity. However, these similarity signals do not reliably reflect functional equivalence, particularly under remote homology, functional divergence, or differences in substrate specificity, potentially resulting in erroneous or missing reaction annotations. To address this limitation, we propose GEM-Reasoner, replacing similarity-driven functional transfer with explicit reasoning about functional equivalence. To support this reasoning when retrieved annotations are incomplete or conflicting, we develop CAPA-RAG, a conflict-aware retrieval-augmented method that uses conflicts across functional dimensions to guide iterative retrieval and evidence integration. GEM-Reasoner uses CAPA-RAG to construct a structured functional profile for each protein and then applies a large language model to assess functional equivalence from paired profiles. We further construct the Protein Function Similarity (PFS) Benchmark, comprising PFS-Core and PFS-Challenge; PFS-Challenge pairs low-similarity positives with high-similarity negatives. On PFS-Challenge, GEM-Reasoner improves MCC by 0.168 over CLEAN, the strongest baseline. In GEM reconstruction for three bacterial species, GEM-Reasoner achieves the highest macro-average F1 for both substrate utilization and gene essentiality prediction. These results suggest that explicit functional reasoning can improve automated GEM reconstruction. Our code is available at https://anonymous.4open.science/r/GEM-Reasoner-825F.