Artificial Intelligence Structural Biology in Nephrology: Powerful Scaffolds, Fragile Certainties

Artificial intelligence (AI)-based protein structure prediction has rapidly entered nephrology, providing plausible atomic models for channels, transporters, receptors, and scaffolds that previously lacked structural information. AlphaFold and RoseTTAFold currently underpin mechanistic interpretations of inherited kidney diseases, including autosomal dominant polycystic kidney disease, cystinosis, and Alport syndrome, and support experimental design in cryo-electron microscopy and cryo-electron tomography. For nephrologists, this means that renal proteins no longer need to be treated as black boxes: Genetic, biochemical, and physiologic data can at present be mapped onto concrete 3-dimensional scaffolds. However, the apparent precision of AI models can be misleading. Renal proteins frequently fall within underrepresented classes, including multipass membrane proteins, intrinsically disordered scaffolds, and human-specific isoforms with shallow multiple sequence alignments, where confidence scores may not accurately reflect model reliability. Current predictors typically deliver static snapshots, whereas transporters, channels, and slit-diaphragm complexes function as dynamic ensembles embedded in specific lipid and macromolecular environments. Attempts to use AlphaFold directly for variant classification or to estimate changes in thermodynamic stability or function show only modest agreement with mutagenesis data, particularly for gain-of-function and dominant-negative alleles that depend on subtle conformational and interaction changes. Common pitfalls include overinterpreting oligomeric stoichiometry inferred from AlphaFold Multimer, reading too much into domain orientations with high predicted aligned error, and dismissing low-confidence regions as modeling failures rather than recognizing intrinsically disordered, potentially regulatory segments. Integrative approaches combining cryo-ET, crosslinking mass spectrometry, and molecular dynamics can mitigate these limitations but remain labor intensive and low throughput. An explainable, hypothesis-driven use of AI that treats predicted structures as working scaffolds to be cross-validated against imaging, biophysics, and functional assays offers the most reliable path toward a genuinely mechanistic nephrology and responsible clinical translation of AI-based structural insights.