ArticleBioinformatics (Oxford, England)2022
mebipred: identifying metal-binding potential in protein sequence.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 48 citations in OpenAlex.
- Metal binding site alignment enables network-driven discovery of recurrent geometries across sequence-divergent proteins and drug off-targets.PLoS computational biology · 2026Article
- Probe-based identification of metal-binding sites using deep learning representations.Nature communications · 2026Article
- Exploring and Targeting the Connection of Iron and Copper Homeostasis to Neurodegenerative Diseases.MedComm · 2026Review
- Bioinformatic study of copper-associated proteins of bread wheat.Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine · 2026Article
- Unlocking Phytoplankton Metallomes with Comparative Analysis of Metal Quotas, Quantitative Proteomics, and Inferred Metalloproteomes.Environmental science & technology · 2025Article
- Topological deep learning for enhancing peptide-protein complex prediction.Communications chemistry · 2025Article
- Interpretable prediction of zinc ion location in proteins with ZincSight.Protein science : a publication of the Protein Society · 2025Article
- Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.Molecules and cells · 2025Review
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- Polymorphism rs259983 of theThe Egyptian journal of medical human genetics · 2025Article
- MgbioRxiv : the preprint server for biology · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings.Bioinformatics (Oxford, England) · 2024Article
- Metal-binding proteins and proteases in RNA viruses: unravelling functional diversity and expanding therapeutic horizons.Journal of virology · 2023Article
- Teleost Piscidins-In Silico Perspective of Natural Peptide Antibiotics from Marine Sources.Antibiotics (Basel, Switzerland) · 2023Review
- Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications.International journal of molecular sciences · 2022Review
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
motivationmetal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability.
resultswe developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred's utility in analyzing microbiome metal requirements. AVAILABILITY AND IMPLEMENTATION: mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.