Structure-informed Siamese graph neural networks classify CirA missense variants with implications for cefiderocol susceptibility

Cefiderocol uptake in Enterobacterales depends partly on TonB-dependent catecholate transporters, including CirA, yet the functional interpretation of CirA missense variation remains limited by an absence of large experimental phenotype datasets. Here we describe a structure-informed Siamese graph neural network (GNN) framework designed to prioritise CirA missense variants that are likely to impair transporter function and thereby contribute to reduced cefiderocol susceptibility. Because large experimental datasets of CirA missense phenotypes are not available, we trained the model on a synthetic mutant set generated from structurally motivated rules applied to the CirA reference structure (AlphaFold model, UniProt P17315). Each residue was represented using protein language model embeddings, backbone geometry, and amino-acid identity, and paired wild-type and mutant graphs were compared through a shared encoder. On synthetic held-out benchmarks, the model achieved strong classification performance on a position-held-out split (macro-F1 = 0.989 against synthetic labels). Applied to a collection of Escherichia coli CirA protein sequences, the framework prioritised a subset of variants as high-confidence non-benign candidates and assigned many others to review or abstain categories, reflecting predictive uncertainty outside the synthetic training distribution. A post-hoc severity-ranking scheme triages disruptive candidates for follow-up. This framework demonstrates that structure-informed synthetic data generation paired with Siamese GNN inference can bridge the gap between sequence-level genomic surveillance and mechanistic functional prediction of outer-membrane transporter variants.