HDOCK-Multimer: improved structure prediction of large protein complexes by integrating docking and combinatorial assembly

Accurate prediction of large protein assembly structures is crucial for understanding biological processes, yet remains a challenge for end-to-end approaches such as AlphaFold owing to their large size and intricate multi-chain interactions. Docking methods can handle large proteins, but suffer from the huge combinatorial binding space of multiple chains. Assembly methods offer an alternative way, but critically rely on the quality of predicted subcomponents. Here we present HDOCK-Multimer (HDM), a framework that integrates docking and combinatorial assembly for structure prediction of large protein complexes. HDM efficiently reduces reliance on subcomponent quality through docking, while leveraging the pairwise interactions of subcomponents through assembly. When evaluated on 3 benchmarks of 35 large heteromeric complexes, 172 large protein complexes and 7 CASP15 targets, HDM substantially outperforms state-of-the-art methods including CombFold, MoLPC, AlphaFold-Multimer and AlphaFold3. In addition, HDM also demonstrates the capacity to predict the stoichiometry and model the complex without stoichiometry input.

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Publication Details

Journal
Nature Computational Science
Published
2026-10-14
DOI
https://doi.org/10.1038/s43588-026-01062-1
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

HDOCK-Multimer: improved structure prediction of large protein complexes by integrating docking and combinatorial assembly

Sheng‐You Huang, Hao Li, Xuan Yao, Yifan Ya
Nature Computational Science
Protein Structure and Dynamics
article

HDOCK-Multimer: improved structure prediction of large protein complexes by integrating docking and combinatorial assembly

Sheng‐You Huang, Hao Li, Xuan Yao, Yifan Ya
article en

Abstract

Accurate prediction of large protein assembly structures is crucial for understanding biological processes, yet remains a challenge for end-to-end approaches such as AlphaFold owing to their large size and intricate multi-chain interactions. Docking methods can handle large proteins, but suffer from the huge combinatorial binding space of multiple chains. Assembly methods offer an alternative way, but critically rely on the quality of predicted subcomponents. Here we present HDOCK-Multimer (HDM), a framework that integrates docking and combinatorial assembly for structure prediction of large protein complexes. HDM efficiently reduces reliance on subcomponent quality through docking, while leveraging the pairwise interactions of subcomponents through assembly. When evaluated on 3 benchmarks of 35 large heteromeric complexes, 172 large protein complexes and 7 CASP15 targets, HDM substantially outperforms state-of-the-art methods including CombFold, MoLPC, AlphaFold-Multimer and AlphaFold3. In addition, HDM also demonstrates the capacity to predict the stoichiometry and model the complex without stoichiometry input.

Nature Computational Science
Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, Huazhong University of Science and Technology, National Key Research and Development Program of China
Openalex Percentile: Top 24%
Protein Structure and Dynamics
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