Abstract
The ability to predict the three-dimensional structure of a protein from its amino acid sequence has potential to provide insights into its function, and in the context of disease, its pathogenic mechanisms and potential drug targets. Artificial intelligence (AI)-driven algorithms, particularly AlphaFold and RoseTTAFold, have revolutionized the field of protein modelling, enabling rapid, high-confidence predictions of protein structures. In nephrology, these advances have clarified the molecular architecture of key renal systems such as podocyte slit diaphragm complexes, the conformational states of membrane transporters and the structural basis of channelopathies that affect polycystin channels. These developments have also enabled low-resolution modelling of complex macromolecular structures, providing insights into structural changes that might underlie the pathogenesis of disease mutants, and enabled virtual screening of drugs and toxins. Although these AI models have yielded important new insights, their integration with experimental methods, particularly cellular cryogenic electron tomography, remains crucial for capturing the domain flexibility, conformational dynamics and binding specificity of proteins in their native environment. Combining AI-based structure prediction with experimental validation will uncover novel pathophysiological mechanisms, guide drug discovery and reveal potential new avenues to target mechanisms of kidney disease.
Key points
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Artificial intelligence (AI)-driven protein modelling tools such as AlphaFold and RoseTTAFold have transformed the field of structural biology, enabling high-accuracy prediction of protein structures directly from amino acid sequences; these approaches can overcome limitations of traditional experimental methods, especially for membrane and multi-domain complexes.
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Structural insights from AI models have clarified mechanisms of kidney disease by mapping mutations in key proteins; these analyses have revealed how sequence variants disrupt folding, domain organization and conformational dynamics in disorders such as distal renal tubular acidosis, Gitelman syndrome and autosomal-dominant polycystic kidney disease.
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Integrative approaches that combine AI predictions with experimental data are redefining how protein–protein interactions, ligand binding and toxin effects are interpreted in kidney biology; such synergy has elucidated the structural principles underlying glomerular slit diaphragm assembly and viral or toxin interactions with renal targets.
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Next-generation AI frameworks such as AlphaFold3, RoseTTAFold All-Atom and Boltz-1 enable modelling of protein–ligand and protein–nucleic acid complexes and support virtual drug screening, and complementary tools such as AlphaMissense and related Variant Effect Predictors enable structural interpretation of pathogenic variants. These advances open the door to mechanism-based therapeutic discovery for kidney diseases.
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Ongoing challenges remain in that current AI models primarily capture static conformations and are limited in representing dynamic ensembles, thermodynamic stability, and lipid or cellular contexts. Future progress in nephrology will depend on the use and development of hybrid approaches that integrate AI predictions with molecular dynamics, single-cell studies and multi-omics data to achieve truly physiological modelling of kidney proteins.
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Acknowledgements
The research of I.K. was in part supported by NIH R01 DK077162, the Smidt Family Foundation, the Kleeman Fund and the Factor Family Foundation. The research of Z.H.Z. was in part supported by NIH R01 GM071940.
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AlphaFold Protein Structure Database: https://alphafold.ebi.ac.uk/
Protein Data Bank (PDB): https://www.rcsb.org
SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structure dataset: https://figshare.com/projects/SARS-CoV-2_RBD_single_mutant_AlphaFold2_structures/150089
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Wu, S., Wang, W., Zhou, Z.H. et al. Bridging structure and function: artificial intelligence-based modelling of kidney proteins. Nat Rev Nephrol (2026). https://doi.org/10.1038/s41581-026-01060-6
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DOI: https://doi.org/10.1038/s41581-026-01060-6
