AI-Predicted Model-Guided Rebuilding of the Experimental Structure of Mouse δ-Aminolevulinic Acid Dehydratase

Experimental macromolecular structures are foundational for elucidating molecular mechanisms and guiding drug design and protein engineering. However, inaccurate structural models are occasionally deposited in the Protein Data Bank (PDB), potentially confounding structural analyses and misleading subsequent studies. Although the integration of artificial intelligence (AI)-predicted models to improve experimentally determined structures has been proposed, the impact of AI-guided rebuilding on functional interpretation remains largely uncharacterized. Here, AI-predicted models of δ-aminolevulinic acid dehydratase (ALAD) were analyzed, and a misinterpreted region in its original crystal structure was rebuilt using an AlphaFold3 (AF3) model as a template. The incorrectly modeled region between Cys122 and Leu142 was corrected utilizing the AF3 main-chain conformation. This rebuilding decreased the Rfree value and improved structural geometry compared to the experimental structure. Notably, the initially deposited ALAD structure exhibited an inactive conformation characterized by a disrupted substrate-binding A-site. In contrast, the AI-rebuilt ALAD model restored a biologically relevant active-site conformation, featuring a coordinated zinc-binding pocket, mediated by Cys122, Cys124, and Cys132, and an intact substrate-binding configuration at the A-site. These results demonstrate the feasibility of AI-predicted model-guided rebuilding in the present ALAD case and may provide a basis for future studies evaluating its applicability to other protein systems.