Hybrid docking-assembly algorithm cracks the structures of giant protein complexes

Proteins rarely work alone. Inside every living cell, the molecules that carry out transport, signal transduction and gene regulation typically operate as large multi-chain assemblies, and knowing the precise three-dimensional arrangement of their subunits is essential for understanding how they function and how drugs might intervene. Yet experimentally solving the structures of these molecular machines remains expensive and often technically prohibitive, which is why computational prediction has become a cornerstone of modern structural biology. Now, a team led by Sheng-You Huang at Huazhong University of Science and Technology has unveiled a new tool that dramatically extends the reach of computational structure prediction into territory where even the most celebrated artificial intelligence systems stumble.

The new method, called HDOCK-Multimer, or HDM, is described in Nature Computational Science and tackles a problem that has grown increasingly urgent as AlphaFold and its successors have transformed the field. While deep learning systems such as AlphaFold2, AlphaFold-Multimer and AlphaFold3 achieve remarkable accuracy on small and medium-sized complexes, their performance degrades as the number of chains grows. The reasons are partly architectural and partly practical: end-to-end predictors suffer from insufficient conformational diversity when modeling many interacting chains, and they run into hard memory limits on graphics processing units. AlphaFold-Multimer version 2, for example, can handle only around 3,300 residues on a 40-gigabyte NVIDIA A100 GPU, while AlphaFold3 extends its input limit to roughly 5,000 residues on its server and about 5,120 residues for local execution on an 80-gigabyte card. Many biologically important assemblies, including respiratory chain complexes and ATP synthase components, far exceed these thresholds.

Assembly-based methods offer one workaround. These approaches first predict smaller subcomponents with AlphaFold and then stitch them together into the full complex. MoLPC, for instance, uses Monte Carlo tree search to assemble complexes from predicted dimers or trimers, while CombFold applies a hierarchical combinatorial assembly strategy that explores inter-subunit interactions more broadly and yields higher-quality predictions. But both methods inherit a critical weakness: their success depends entirely on the quality of the subcomponents produced by AlphaFold-Multimer. If a predicted subunit or interface is wrong, the entire assembly collapses. Symmetry-aware tools such as EvoDock and SymProFold improve modeling of symmetric assemblies but do not generalize to the heteromeric, asymmetric complexes that dominate cellular biochemistry.

HDOCK-Multimer resolves this tension by fusing two historically separate traditions: classical protein-protein docking and combinatorial assembly. The framework takes subunit sequences as input and proceeds through three stages. First, it generates monomeric and subcomponent structures using AlphaFold2 and AlphaFold-Multimer, defining subcomponents as all possible trimeric combinations of subunits. Trimers were chosen as a deliberate compromise, capturing more cooperative interactions than pairwise combinations while avoiding the prohibitive computational cost and GPU memory overflow that tetrameric predictions would incur. Second, the system selects appropriate modeling strategies, including asymmetric docking, symmetric docking and combinatorial assembly, based on the complex’s homology and pairwise docking results. Third, it constructs the full complex using the selected strategies, evaluates the resulting models with an iterative knowledge-based scoring function called IT-score, and clusters them using a root-mean-square-deviation cut-off of 5 angstroms to eliminate redundancy.

The docking component is where HDM distinguishes itself most sharply. For symmetric assemblies, the method supports cyclic, dihedral, tetrahedral and octahedral symmetries, inferring candidate symmetry types from chain number and pairwise docking results rather than requiring explicit symmetry input. For asymmetric complexes with fewer than five unique chains, it employs a multi-body docking procedure built on the team’s earlier HDOCK software, which uses a fast Fourier transform-based search. Each chain is treated as a node in a graph, pairwise docking interactions become edges, and complex modeling is recast as a minimum spanning tree construction problem that borrows principles from Prim’s and Kruskal’s algorithms. Iterative score and interface screening prune the enormous combinatorial space of possible chain arrangements, which is precisely the bottleneck that has historically limited docking approaches to dimers.

The assembly side is equally sophisticated. HDM extracts representative subunits and pairwise transformations from predicted subcomponents, performs 500 rounds of random assembly sampling using create, append and merge operations, and then refines the resulting population with a genetic algorithm. Crossover operations recombine subunits from parent structures, while mutations rebuild complexes from alternative transformations. A confidence score, weighted by the number of residues involved in each assembly operation, ranks candidate structures, and molecular dynamics relaxation with the ff14SB force field removes steric clashes before final scoring. Ablation experiments on a benchmark of 35 large heteromeric complexes showed that each component contributes: the basic assembly strategy alone already outperforms MoLPC and CombFold, genetic algorithm optimization increases the number of acceptable predictions, and IT-score ranking after relaxation enriches high-quality models among the top-ranked outputs.

The benchmark results are striking. On the first benchmark of 35 heteromeric complexes containing 5 to 20 chains each, HDM achieved top-1 success rates of 83 percent for acceptable accuracy and 66 percent for high-quality predictions, compared with 74 and 54 percent for CombFold, 29 and 11 percent for MoLPC, and 29 and 26 percent for AlphaFold-Multimer v2. Measured by interface quality with the DockQ metric, HDM reached 86 percent top-1 success. In one showcase example, the Polytomella complex I, HDM produced a high-quality prediction with a template modeling score of 0.85, while CombFold and MoLPC managed only 0.64 and 0.32, with CombFold failing primarily because two subunits were misplaced. Notably, AlphaFold-Multimer v2 failed entirely on several targets due to GPU memory limitations, whereas HDM assembled them with varying degrees of accuracy.

On a second benchmark of 172 large complexes with 10 to 30 chains, including 114 homomers and 58 heteromers, HDM achieved top-1 and top-10 success rates of 59 and 66 percent, ahead of CombFold at 41 percent, MoLPC at 30 percent, and AlphaFold3 at 55 and 58 percent. The comparison with AlphaFold3 deserves nuance: the deep learning system produced substantially more high-quality predictions on the complexes it could process, though it could model only 145 of the 172 targets because of input limits, and the benchmark targets predate January 2022 and may overlap with its training data. Where HDM truly shines is scale. For a glycolyl-CoA carboxylase containing 7,062 residues, well beyond the AlphaFold3 server limit, HDM delivered a near-perfect prediction of the resolved regions with a template modeling score of 0.98 while reconstructing the unresolved portion of the alpha subunit with an average predicted confidence of 91.06, recovering 2,749 additional amino acids. For the human GCN5 histone acetyltransferase domain, HDM’s octahedral symmetry docking yielded a high-quality result where AlphaFold3 failed outright.

Tested on seven real targets from the 15th Critical Assessment of Structure Prediction, including two single-chain proteins longer than 3,000 amino acids that were partitioned into domains using disorder predictions, the fully automated pipeline generated acceptable models for six of seven targets, a success rate of 86 percent. CombFold matched that rate, while MoLPC and AlphaFold-Multimer v2 succeeded on only three and one targets respectively. The team also demonstrated that HDM can infer stoichiometry, the number of copies of each subunit, by enumerating possible combinations and using a normalized IT-score, correctly identifying the stoichiometry for 14 of 20 representative complexes, compared with 9 using confidence scores alone. An analysis of interface novelty further showed that prediction accuracy correlates only weakly with similarity to interfaces in AlphaFold’s training data, indicating the method can genuinely assemble complexes it has never seen.

The authors are candid about remaining limitations. Their symmetric docking covers only a subset of symmetries, excluding helical assemblies; model ranking sums pairwise interaction scores rather than capturing higher-order cooperativity; and stoichiometry prediction, while promising, is not absolute and ultimately depends on subcomponent quality. They also note that integrating experimental data such as crosslinking mass spectrometry, which HDM already supports, fluorescence resonance energy transfer and cryo-electron microscopy could further improve accuracy, and that antibody-antigen modeling remains an open question, with the method succeeding on two of four tested targets. Still, with the software freely available on GitHub and full complexes typically built within hours on modest CPU hardware after subcomponent prediction, HDOCK-Multimer represents a pragmatic and powerful hybrid: it keeps the accuracy of deep learning where it works, and deploys decades of docking physics where it does not, opening the door to atomic-level models of the molecular machines that have long eluded both crystallographers and algorithms alike.

Subject of Research: Computational prediction of large protein complex structures by integrating docking and combinatorial assembly

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

Article References: HDOCK-Multimer: improved structure prediction of large protein complexes by integrating docking and combinatorial assembly. (n.d.). https://doi.org/10.1038/s43588-026-01062-1

Image Credits: AI Generated

DOI: 10.1038/s43588-026-01062-1

Keywords: protein structure prediction, HDOCK-Multimer, protein docking, AlphaFold, combinatorial assembly, protein complexes, computational biology, deep learning, stoichiometry prediction, structural biology, symmetric assemblies, CASP15