ArticleScientific data2024
Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Ligand-agnostic off-target site prediction for early toxicity screening by leveraging point cloud-based protein cavity analysis.Briefings in bioinformatics · 2026Article
- Thermal shift assay to identify ligands for bacterial sensor proteins.FEMS microbiology reviews · 2025Review
- Diversity, Distribution and Structural Prediction of the Pathogenic Bacterial Effectors EspN and EspS.Genes · 2024Article
- CavitOmiX Drug Discovery: Engineering Antivirals with Enhanced Spectrum and Reduced Side Effects for Arboviral Diseases.Viruses · 2024Article
- Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.Scientific data · 2024Article
- Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.BioMedicine · 2024Review
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Authors and funding
9 authors.
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Abstract
Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA's BioNeMo platform, as well as homology modeling using Innophore's CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions.
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