ArticleNature communications2023
Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 71 citations in OpenAlex.
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- Multivalent ion binding site identification with structure-based deep learning.Communications biology · 2026Article
- Protein-Enforced Ligand Environments Reshape Classical Coordination Preferences in Copper Polypyridyl Complexes.Journal of the American Chemical Society · 2026Article
- Geometric deep learning-enabled metal-binding site identification and grafting.Fundamental research · 2026Article
- Probe-based identification of metal-binding sites using deep learning representations.Nature communications · 2026Article
- Zero-shot design of abioRxiv : the preprint server for biology · 2026Article
- Integrated analysis of the redoxome and zinc proteome links ribosomal protein oxidation to zinc homeostasis.Redox biology · 2026Article
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- Master of Metals2: a graph neural network based architecture for the prediction of zinc binding sites in protein structures.Briefings in bioinformatics · 2026Article
- Quantum-machine-assisted drug discovery.npj drug discovery · 2026Review
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- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein.PloS one · 2026Article
- Interpretable prediction of zinc ion location in proteins with ZincSight.Protein science : a publication of the Protein Society · 2025Article
- CysMP reveals metal ion-specific metalloproteomes and copper-regulated PGK1 activity in glycolysis.Science advances · 2025Article
- SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins.Journal of cheminformatics · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.