ArticleNature chemical biology2023
Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.
Article in Nature chemical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 47 citations in OpenAlex.
- Article
- Multivalent ion binding site identification with structure-based deep learning.Communications biology · 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
- Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cross-continental transmission and host adaptation ofiMetaOmics · 2025Article
- Predicting the binding residues of four small molecule ligands by utilizing ensemble algorithms with additional correlation features.Scientific reports · 2025Article
- Signal-strapping as a protein-sequence search method for the discovery of metalloproteins.Nature communications · 2025Article
- CysMP reveals metal ion-specific metalloproteomes and copper-regulated PGK1 activity in glycolysis.Science advances · 2025Article
- Advances in Mammalian Metallomics: New Insights into Metal Dynamics and Biological Significance.International journal of molecular sciences · 2025Review
- Metal-Based Approaches for the Fight against Antimicrobial Resistance: Mechanisms, Opportunities, and Challenges.Journal of the American Chemical Society · 2025Review
- Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.Molecules and cells · 2025Review
- Bacterial Metallostasis: Metal Sensing, Metalloproteome Remodeling, and Metal Trafficking.Chemical reviews · 2024Review
- Novel Insights into Enzymatic Thermostability: The "Short Board" Theory and Zero-Shot Hamiltonian Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- MetalNet2: an enhanced server for predicting metal-binding sites in proteomes.National science review · 2024Article
- Evolution of plant metabolism: the state-of-the-art.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Bridging the Gap between Sequence and Structure Classifications of Proteins with AlphaFold Models.Journal of molecular biology · 2024Article
- Iron-sulfur cluster-dependent enzymes and molybdenum-dependent reductases in the anaerobic metabolism of human gut microbes.Metallomics : integrated biometal science · 2024Review
- Proteomic strategies to interrogate the Fe-S proteome.Biochimica et biophysica acta. Molecular cell research · 2024Review
- Global profiling of functional histidines in live cells using small-molecule photosensitizer and chemical probe relay labelling.Nature chemistry · 2024Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.
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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.