DeepAssembly2: A Web Server for Protein Complex Structure Assembly Based on Domain-Domain Interactions
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Abstract
Proteins often perform biological functions by forming complexes, thereby accurately predicting the structure of protein complexes is crucial to understanding and mastering their functions, as well as facilitating drug discovery. Protein monomeric structure prediction has made a breakthrough in recent years, but the accurate prediction of complex structure remains a challenge. In this work, we present DeepAssembly2, a web server for automatically assembling protein complex structure based on domain-domain interactions. First, the features are constructed according to the input complex sequence and monomeric structures, then these features are used to predict the inter-chain residue distance through a deep learning model, and finally, the complex structure is assembled under the guidance of inter-chain residue distances. Compared with the previously developed version, DeepAssembly2 is trained on a newly constructed inter-chain domain-domain interaction dataset. Meanwhile, several important features have been added, such as Interface Residue Propensity and Ultrafast Shape Recognition. In addition, we introduced the inter-chain residue distance from the AlphaFold-Multimer model to further improve the accuracy. Finally, we also integrate our recently developed model quality assessment method to select the output models. The performance of DeepAssembly2 is significantly improved compared with the previous version, and it is expected to provide new insights and an effective tool for drug development, vaccine design, etc. The web server of DeepAssembly2 is freely available at https://zhanglab-bioinf.com/DeepAssembly/.
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Computational Resources for Molecular Biology 2025
Mathews, D. H., Casadio, R., & Sternberg, M. J. E. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169222
Cui, X., Ge, L., Yang, X., Li, X., & Zhang, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.23.684122
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- Authors
- Xia, Y., Pu, Y., Wang, S., Zhuang, J., Liu, D., Hou, M., & Zhang, G.
- Affiliations
- Zhejiang University of Technology (CN)
- Corresponding author
- Zhang, Guijun
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 437
- Issue
- 15
- Pages
- 169128
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W4409201509
- Cited by
- 2
- References
- 40
- Citations by year
- 2025: 2
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Molecular Biology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62173304, 2025C01190)
- National Key Research and Development Program of China (2022ZD0115103)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein complex structure prediction, protein complex formation, domain-domain interactions, web server, shape recognition, AlphaFold-Multimer, model quality assessment, drug discovery
- MeSH terms
- Protein Domains, Deep Learning, Models, Molecular, Protein Conformation, Proteins, Software, Computational Biology, Internet, Databases, Protein
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