ArticleNature communications2026
Atlas of predicted protein complex structures across kingdoms.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Predicted protein-protein interactions between sugar beet root maggot trypsins and sugar beet Kunitz trypsin inhibitors using deep learning.Data in brief · 2026Article
- DirectContacts2: a wiring diagram of human physical protein interactions.Nature communications · 2026Article
- FlyPredictome: A structural atlas of predicted protein-protein interactions inbioRxiv : the preprint server for biology · 2026Article
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Authors and funding
37 authors.
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Abstract
Protein complexes are fundamental to all biological processes. Public repositories have expanded to include millions of potential protein-protein interactions (PPIs) from human and diverse model organisms. Yet, large-scale structural characterization of these complexes-especially across different biological kingdoms-has lagged far behind, leaving most potential and unidentified interactions unresolved. Here, we present a comprehensive atlas of 1.1 million predicted protein-protein interaction structures generated with the AlphaFold2-based ColabFold framework. This dataset spans proteome-wide interactions from bacteria, archaea, humans, mice, plants, and human-virus pairs. Overall, we identify 181,671 high-confidence protein complex structures, especially 37,855 in the human interactome. Structural clustering revealed numerous conserved protein complex architectures shared across kingdoms, providing insights into previously uncharacterized biological functions. Supported by co-immunoprecipitation experiments, we further identify candidate viral receptors for Human mastadenovirus A and Papiine alphaherpesvirus 2. Comparative analyses integrating our complex structures with the AlphaFold monomeric structure database uncovered widespread gene fusion and fission events during evolution. Finally, we demonstrate how our dataset can enhance protein binding-surface prediction using deep learning approaches, illustrating its broad utility beyond structural modeling alone. Altogether, this atlas to our knowledge, represents one of the most extensive cross-kingdom resources and opens avenues for future discoveries in various biomedical applications.
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