SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins
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 funct...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-01038-9 |
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| author | Xiaobo Lin Zhaoqian Su Yunchao Lance Liu Jingxian Liu Xiaohan Kuang Peter T. Cummings Jesse Spencer-Smith Jens Meiler |
| author_facet | Xiaobo Lin Zhaoqian Su Yunchao Lance Liu Jingxian Liu Xiaohan Kuang Peter T. Cummings Jesse Spencer-Smith Jens Meiler |
| author_sort | Xiaobo Lin |
| collection | DOAJ |
| description | 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 $$\sim$$ ∼ 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. |
| format | Article |
| id | doaj-art-e607c0b1551749099823a8916a99711f |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-e607c0b1551749099823a8916a99711f2025-08-20T04:03:07ZengBMCJournal of Cheminformatics1758-29462025-07-0117111210.1186/s13321-025-01038-9SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteinsXiaobo Lin0Zhaoqian Su1Yunchao Lance Liu2Jingxian Liu3Xiaohan Kuang4Peter T. Cummings5Jesse Spencer-Smith6Jens Meiler7Data Science Institute, Vanderbilt UniversityData Science Institute, Vanderbilt UniversityComputer Science Department, Vanderbilt UniversityData Science Institute, Vanderbilt UniversityData Science Institute, Vanderbilt UniversityChemical and Biomolecular Engineering Department, Vanderbilt UniversityData Science Institute, Vanderbilt UniversityInstitute for Drug Discovery, Institute for Computer Science, Wilhelm Ostwald Institute for Physical and Theoretical Chemistry, University LeipzigAbstract 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 $$\sim$$ ∼ 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.https://doi.org/10.1186/s13321-025-01038-9Generative AIDiffusion modelMetal-binding sitesMetalloprotein |
| spellingShingle | Xiaobo Lin Zhaoqian Su Yunchao Lance Liu Jingxian Liu Xiaohan Kuang Peter T. Cummings Jesse Spencer-Smith Jens Meiler SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins Journal of Cheminformatics Generative AI Diffusion model Metal-binding sites Metalloprotein |
| title | SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins |
| title_full | SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins |
| title_fullStr | SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins |
| title_full_unstemmed | SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins |
| title_short | SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins |
| title_sort | supermetal a generative ai framework for rapid and precise metal ion location prediction in proteins |
| topic | Generative AI Diffusion model Metal-binding sites Metalloprotein |
| url | https://doi.org/10.1186/s13321-025-01038-9 |
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