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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Data availability
The 1.1 million predicted protein structures generated in this study have been deposited in the ModelScope database (https://www.modelscope.cn/collections/protein_complex_atlas-2ae5e7d4f4a343). The processed, curated high-confidence PPI structures are available at a companion website (https://www.biopredictnavigator.cn). Accession codes for analysed genomes of representative prokaryotes are available in Supplementary Data 1. Source data are provided with this paper.
Code availability
The code for this manuscript is provided in GitHub repository: https://github.com/wensm77/Protein-Complex-Atlas, and on Zenodo: https://doi.org/10.5281/zenodo.18630539.
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Acknowledgements
This work was supported by National Key R&D Program of China (No.2023YFF1205400 to D.M.), National Natural Science Foundation of China under grant (No. 32571689 and No. 32301230 to D.M., No. 82573859 to Y. Y. and No. 72174172 to D. E.), Zhejiang Laboratory PI start program, Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM502 to J.Z.), the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2024ZD0525100 to J.Z.) and the Scientific and Technological Innovation Team for Qinghai-Tibetan Plateau Research in Southwest Minzu University (Grant No.2024CXTD20). Authors thank Yuanzhao Pan (Beijing National Day School) for valuable support and insightful discussions. Xitong Li (Jiangnan University), Weizhen Ou (Jiangnan University), Jijun Fan (Jiangnan University), Wenbo Deng (China University of Mining and Technology), and Shuhao Niu (Jiangnan University) provided suggestions for language revisions.
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D.M. conceived this project. X.Q., C.Y., J.L., S.W., Yuanyuan L., K.D., Yongfu H., J.F., W.M., L.L., Z.L., Y.S., H.Z., Yayun H., R.Z., P.J., Yafei L., B.L., H.W., Yuxuan C., Z.M., P.Y., X.X., J.W., Y.Z., Q.Z., W.Z., K.Y., S. L., H.X., D.E. performed the computational analysis. J.F. and Y.Y. performed wet-lab experiments. D.M., Y.Y., Ying C., and C.S. supervised the project. D.M., R.Z., Z.X., J.Z., D.E., H.X., and W.Z. wrote the manuscript.
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Qi, X., Ye, C., Liang, J. et al. Atlas of predicted protein complex structures across kingdoms. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70884-4
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DOI: https://doi.org/10.1038/s41467-026-70884-4
