Prediction of Metal Ion–Binding Sites in Proteins Using the Fragment Transformation Method | PLOS ONE
![MIB: Metal Ion-Binding Site Prediction and Docking Server | Journal of Chemical Information and Modeling MIB: Metal Ion-Binding Site Prediction and Docking Server | Journal of Chemical Information and Modeling](https://pubs.acs.org/cms/10.1021/acs.jcim.6b00407/asset/images/large/ci-2016-004073_0002.jpeg)
MIB: Metal Ion-Binding Site Prediction and Docking Server | Journal of Chemical Information and Modeling
![Prediction of water and metal binding sites and their affinities by using the Fold-X force field | PNAS Prediction of water and metal binding sites and their affinities by using the Fold-X force field | PNAS](https://www.pnas.org/cms/10.1073/pnas.0501980102/asset/63530a8d-5eab-499a-b191-16749889ffe9/assets/graphic/zpq0280588370005.jpeg)
Prediction of water and metal binding sites and their affinities by using the Fold-X force field | PNAS
![Metal binding prediction subtasks. (a): given sequence; (b) candidate... | Download Scientific Diagram Metal binding prediction subtasks. (a): given sequence; (b) candidate... | Download Scientific Diagram](https://www.researchgate.net/publication/51130574/figure/fig3/AS:324985584406538@1454494009588/Metal-binding-prediction-subtasks-a-given-sequence-b-candidate-ligands-CYS-and.png)
Metal binding prediction subtasks. (a): given sequence; (b) candidate... | Download Scientific Diagram
![MIB: Metal Ion-Binding Site Prediction and Docking Server | Journal of Chemical Information and Modeling MIB: Metal Ion-Binding Site Prediction and Docking Server | Journal of Chemical Information and Modeling](https://pubs.acs.org/cms/10.1021/acs.jcim.6b00407/asset/images/large/ci-2016-004073_0003.jpeg)
MIB: Metal Ion-Binding Site Prediction and Docking Server | Journal of Chemical Information and Modeling
GitHub - sbl-sdsc/metal-binding-prediction: Methods to predict metal binding sites in a protein by its amino acid sequence
![Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41467-023-37870-6/MediaObjects/41467_2023_37870_Fig7_HTML.png)
Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications
![Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning | bioRxiv Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning | bioRxiv](https://www.biorxiv.org/content/biorxiv/early/2022/05/20/2022.05.20.492769/F1.large.jpg)
Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning | bioRxiv
Prediction of Metal Ion–Binding Sites in Proteins Using the Fragment Transformation Method | PLOS ONE
![Frontiers | The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility Frontiers | The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility](https://www.frontiersin.org/files/Articles/503708/fgene-11-00214-HTML/image_m/fgene-11-00214-g001.jpg)
Frontiers | The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility
GitHub - biomed-AI/LMetalSite: MetalSite: alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning
![Exploring the computational methods for protein-ligand binding site prediction - Computational and Structural Biotechnology Journal Exploring the computational methods for protein-ligand binding site prediction - Computational and Structural Biotechnology Journal](https://www.csbj.org/cms/attachment/49549df1-e6b8-4be9-9507-55003f8a7aa8/gr1_lrg.jpg)
Exploring the computational methods for protein-ligand binding site prediction - Computational and Structural Biotechnology Journal
![Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-023-37870-6/MediaObjects/41467_2023_37870_Fig1_HTML.png)
Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications
![PRL-3 has numerous predicted metal binding sites. The crystal structure... | Download Scientific Diagram PRL-3 has numerous predicted metal binding sites. The crystal structure... | Download Scientific Diagram](https://www.researchgate.net/publication/373013297/figure/fig1/AS:11431281180459710@1691604207872/PRL-3-has-numerous-predicted-metal-binding-sites-The-crystal-structure-of-PRL-3-in.png)
PRL-3 has numerous predicted metal binding sites. The crystal structure... | Download Scientific Diagram
![Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins | Nature Communications](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41467-023-37870-6/MediaObjects/41467_2023_37870_Fig5_HTML.png)