Evidence map›Paper›PMID 41882029›Full record

ArticleNature communications2026

Atlas of predicted protein complex structures across kingdoms.

Xianzhi Qi, Cheng Ye, Jianqiang Liang, Shimin Wen, Yuanyuan Li, Kai Ding, Yongfu Hao, Junjie Fei, Weian Mao, Liupeng Li and 27 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

37 authors.

Xianzhi Qi *Zhejiang Lab, Hangzhou, China.
Cheng Ye *Zhejiang Lab, Hangzhou, China.
Jianqiang Liang *Zhejiang Lab, Hangzhou, China.
Shimin Wen *College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China.
Yuanyuan Li *Zhejiang Lab, Hangzhou, China.
Kai Ding *Zhejiang Lab, Hangzhou, China.ORCID http://orcid.org/0000-0003-4534-2904
Yongfu Hao *Zhejiang Lab, Hangzhou, China.ORCID http://orcid.org/0000-0002-0800-6170
Junjie Fei *Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China.
Weian Mao *Australian Institute for Machine Learning, The University of Adelaide, Adelaide, Australia.
Liupeng Li *Zhejiang Lab, Hangzhou, China.
Zhiyu LinCollege of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China.
Yichong ShenZhejiang Lab, Hangzhou, China.
Hongjie ZhuCollege of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China.
Yayun HuZhejiang Lab, Hangzhou, China.ORCID http://orcid.org/0000-0002-3902-8903
Rui ZhangZhejiang Lab, Hangzhou, China.ORCID http://orcid.org/0000-0001-9126-9790
Pengli JiZhejiang Lab, Hangzhou, China.
Yafei LuZhejiang Lab, Hangzhou, China.
Bonan LiuCollege of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China.
Han WangCollege of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China.
Yuxuan ChenCollege of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China.
Zhenguo MaZhejiang Lab, Hangzhou, China.ORCID http://orcid.org/0000-0001-7660-735X
Peiyuan YangZhejiang University, Hangzhou, China.
Xinyu XuZhejiang Lab, Hangzhou, China.
Junlong WuDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.ORCID http://orcid.org/0000-0002-4125-7975
Youyuan ZhuZhejiang Lab, Hangzhou, China.
Qiaosha ZouZhejiang Lab, Hangzhou, China.
Wencheng ZhuInstitute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.ORCID http://orcid.org/0000-0001-8123-9504
Kelu YaoZhejiang Lab, Hangzhou, China.
Shuya LiSchool of Engineering, Westlake University, Hangzhou, China.
Hongyi XinGlobal Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0003-2864-7386
Daji ErguCollege of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China.
Jianyang ZengSchool of Engineering, Westlake University, Hangzhou, China.ORCID http://orcid.org/0000-0003-0950-7716
Zhi-Xiong Jim XiaoCenter of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China.ORCID http://orcid.org/0000-0003-2504-5742
Chunhua ShenZhejiang University, Hangzhou, China. chunhuashen@zju.edu.cn.
Ying CaiCollege of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China. caiying@swun.edu.cn.ORCID http://orcid.org/0000-0002-5096-6175
Yong YiCenter of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China. yy-yiyong@scu.edu.cn.ORCID http://orcid.org/0000-0003-4664-9692
Dacheng MaZhejiang Lab, Hangzhou, China. dacheng2023@126.com.ORCID http://orcid.org/0000-0002-2048-4300

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32301230
6 · The paper itself

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.

Indexed as

Multiprotein ComplexesProtein Interaction MapsAnimalsArchaeaBacteriaDatabases, ProteinHumansMiceModels, MolecularPlantsProtein BindingProtein ConformationProtein Interaction MappingProteomeMultiprotein ComplexesProteome

Identifiers

PMID41882029
PMCPMC13181095

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.