J Am Soc Nephrol. 2026 Aug 21. doi: 10.1681/ASN.0000001254. Online ahead of print.
ABSTRACT
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 now 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 physiological data can now be mapped onto concrete three-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 modelling failures rather than recognising intrinsically disordered, potentially regulatory segments. Integrative approaches combining cryo-electron tomography, crosslinking mass spectrometry and molecular dynamics can mitigate these limitations but remain labour-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 towards a genuinely mechanistic nephrology and responsible clinical translation of AI-based structural insights.
PMID:42627689 | DOI:10.1681/ASN.0000001254