ArticleJournal of molecular biology2022
Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome.
Article in Journal of molecular biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Multi-omics integration and machine learning define an iron-sulfur cluster/zinc-binding protein prognostic signature in esophageal squamous cell carcinoma.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.BMC bioinformatics · 2026Article
- Discovery of a Structurally Distinct Acetylenase in the Biosynthesis of Mangotoxin.Journal of the American Chemical Society · 2026Article
- The transformative power of structural predictions with AI in plant science.The Plant journal : for cell and molecular biology · 2026Review
- Benchmarking Zinc-Binding Site Predictors: A Comparative Analysis of Structure-Based Approaches.Journal of chemical information and modeling · 2025Article
- Control of Replication Stress Response by Cytosolic Fe-S Cluster Assembly (CIA) Machinery.Cells · 2025Review
- Guided tour through the small protein landscape ofmicroLife · 2025Article
- Using deep-learning predictions reveals a large number of register errors in PDB depositions.IUCrJ · 2024Article
- Proteomic strategies to interrogate the Fe-S proteome.Biochimica et biophysica acta. Molecular cell research · 2024Review
- Conserved cysteine residues in Kaposi's sarcoma herpesvirus ORF34 are necessary for viral production and viral pre-initiation complex formation.Journal of virology · 2024Article
- Conserved cysteine residues in Kaposi's sarcoma herpesvirus ORF34 are necessary for viral production and viral pre-initiation complex formation.bioRxiv : the preprint server for biology · 2024Article
- Iron-sulfur protein odyssey: exploring their cluster functional versatility and challenging identification.Metallomics : integrated biometal science · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Improving Signal and Transit Peptide Predictions Using AlphaFold2-predicted Protein Structures.Journal of molecular biology · 2024Article
- Recent Advances in Metalloproteomics.Biomolecules · 2024Review
- The Cryptic Nature of Fe-S Clusters: A Case Study of the Hepatitis B HBx Oncoprotein.Inorganics · 2023Article
- Novel machine learning approaches revolutionize protein knowledge.Trends in biochemical sciences · 2023Review
- Classifying metal-binding sites with neural networks.Protein science : a publication of the Protein Society · 2023Article
- UniProt: the Universal Protein Knowledgebase in 2023.Nucleic acids research · 2023Article
- Quick and Spontaneous Transformation between [3Fe-4S] and [4Fe-4S] Iron-Sulfur Clusters in the tRNA-Thiolation Enzyme TtuA.International journal of molecular sciences · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
DeepMind's AlphaFold2 software has ushered in a revolution in high quality, 3D protein structure prediction. In very recent work by the DeepMind team, structure predictions have been made for entire proteomes of twenty-one organisms, with >360,000 structures made available for download. Here we show that thousands of novel binding sites for iron-sulfur (Fe-S) clusters and zinc (Zn) ions can be identified within these predicted structures by exhaustive enumeration of all potential ligand-binding orientations. We demonstrate that AlphaFold2 routinely makes highly specific predictions of ligand binding sites: for example, binding sites that are comprised exclusively of four cysteine sidechains fall into three clusters, representing binding sites for 4Fe-4S clusters, 2Fe-2S clusters, or individual Zn ions. We show further: (a) that the majority of known Fe-S cluster and Zn binding sites documented in UniProt are recovered by the AlphaFold2 structures, (b) that there are occasional disputes between AlphaFold2 and UniProt with AlphaFold2 predicting highly plausible alternative binding sites, (c) that the Fe-S cluster binding sites that we identify in E. coli agree well with previous bioinformatics predictions, (d) that cysteines predicted here to be part of ligand binding sites show little overlap with those shown via chemoproteomics techniques to be highly reactive, and (e) that AlphaFold2 occasionally appears to build erroneous disulfide bonds between cysteines that should instead coordinate a ligand. These results suggest that AlphaFold2 could be an important tool for the functional annotation of proteomes, and the methodology presented here is likely to be useful for predicting other ligand-binding sites.
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