# SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins

## TL;DR

SuperMetal is a new AI tool that quickly and accurately predicts where metal ions bind in proteins, helping with drug discovery and protein engineering.

## Contribution

SuperMetal introduces a diffusion-based, SE(3)-equivariant generative model for precise and rapid metal ion prediction in proteins.

## Key findings

- SuperMetal achieves 94% precision and 90% coverage in predicting zinc ion binding sites.
- It localizes metal ions within 0.52 ± 0.55 Å of experimental positions.
- The model operates in under 10 seconds for proteins with ~2000 residues.

## Abstract

Metal ions, as abundant and vital cofactors in numerous proteins, are crucial for enzymatic activities and protein interactions. Given their pivotal role and catalytic efficiency, accurately and efficiently identifying metal-binding sites is fundamental to elucidating their biological functions and has significant implications for protein engineering and drug discovery. To address this challenge, we present SuperMetal, a generative AI framework that leverages a score-based diffusion model coupled with a confidence model to predict metal-binding sites in proteins with high precision and efficiency. Using zinc ions as an example, SuperMetal outperforms existing state-of-the-art models, achieving a precision of 94 % and coverage of 90 %, with zinc ions localization within 0.52 ± 0.55 Å of experimentally determined positions, thus marking a substantial advance in metal-binding site prediction. Furthermore, SuperMetal demonstrates rapid prediction capabilities (under 10 s for proteins with \documentclass[12pt]{minimal}
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\begin{document}$$\sim$$\end{document}∼ 2000 residues) and remains minimally affected by increases in protein size. Notably, SuperMetal does not require prior knowledge of the number of metal ions—unlike AlphaFold 3, which depends on this information. Additionally, SuperMetal can be readily adapted to other metal ions or repurposed as a probe framework to identify other types of binding sites, such as protein-binding pockets.

Scientific contribution

SuperMetal introduces a diffusion-based, SE(3)-equivariant generative model that places metal ions in proteins with 94 % precision, 90 % coverage, and sub-ångström (0.52 Å) accuracy in under 10 s, surpassing current methods and accelerating metal-aware protein engineering and drug discovery.

The online version contains supplementary material available at 10.1186/s13321-025-01038-9.