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For protein-only complexes, start with OpenFold-Multimer or ColabFold. ColabFold is the quickest way to generate AlphaFold2- or RoseTTAFold-based hypotheses with MMseqs2 alignments. OpenFold is the better local, trainable PyTorch stack when chains are long or you need control over memory and inference. Choose Chai-1 when you can provide inter-chain or covalent-bond restraints, and Boltz when ligands or binding-affinity prediction are part of the question. AlphaFold 3 is an important scientific reference for mixed biomolecular assemblies, but its implementation and weight licensing must be checked before deployment.
There is no universal “best” model for a large assembly. The right choice depends on stoichiometry, chain length, cofactors, nucleic acids, ligands, available restraints, privacy requirements, GPU memory and whether you need coordinates alone or affinity estimates as well.
What counts as a large protein complex?
“Large” can mean several different things: one unusually long polypeptide, many copies of shorter chains, a protein–nucleic-acid assembly, or a complex whose ligands and modified residues make the input chemically diverse. A model that can process a 4,000-residue sequence is not automatically able to fit a 4,000-residue multimer, and fitting an assembly does not prove that its interfaces are correct.
Before choosing software, write down the biological assembly you actually want to model:
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- Every subunit and its stoichiometry, including repeated chains.
- Whether sequences are complete, signal peptides are present, and unresolved regions are expected.
- Required cofactors, nucleic acids, ligands, ions and post-translational modifications.
- Known contacts, cross-links, mutational constraints or covalent bonds.
- Whether sequence data may be sent to a public alignment service.
- GPU memory, runtime and licensing limits for your intended use.
These decisions determine whether a protein-only multimer workflow is appropriate or whether you need an all-atom, restraint-aware or affinity-capable model.
At-a-glance comparison
| Tool | Best scope | Scale and hardware evidence | MSA and templates | Restraints | Code and weight licensing | Affinity output |
|---|---|---|---|---|---|---|
| OpenFold / OpenFold-Multimer | Protein monomers, protein–protein interactions and multimer modeling; the ecosystem also lists OpenFold3 for cofolding. | Repository documentation reports inference for sequences longer than 4,000 residues on one A100, low-memory attention, CPU offloading and up to about 2× GPU speedups. This is not a guarantee for an entire multimer. | AlphaFold-style parameters and alignment inputs; OpenFold supports inference with AlphaFold’s official parameters and vice versa. | Not established as a first-class input in the cited documentation. | Apache 2.0 is stated for the OpenFold ecosystem page; verify the exact release and databases you deploy. | No. |
| ColabFold | Fast protein and protein-complex hypotheses using AlphaFold2 or RoseTTAFold. | No standardized maximum assembly size is stated. Memory rises with sequence length, number of chains, recycles and model settings. | MMseqs2 supplies fast homology searches; the workflow can use AlphaFold2 or RoseTTAFold models. | Not its defining capability. | Open source; check the repository and database terms for your release and use case. | No. |
| Chai-1 | Complexes where experimental contacts or covalent chemistry should guide folding. | No universal large-assembly limit is established in the cited repository. | Use the package’s documented input and alignment workflow; requirements vary with the model release. | Explicit inter-chain contact restraints and covalent-bond restraints. | Repository states Apache 2.0 for code and weights; it recommends pinning a version such as chai_lab==0.6.1. |
No affinity result is established by the cited documentation. |
| Boltz-1 / Boltz-2 | Protein–ligand and broader biomolecular interaction modeling; Boltz-2 also predicts binding affinities. | No universal maximum is stated. The README documents NVIDIA CUDA acceleration and says CPU inference is significantly slower. | Use the model’s supplied preprocessing and alignment inputs; inspect release notes for changes. | Not the principal differentiator described in the cited material. | Repository states MIT licensing for code and weights, including academic and commercial use. | Boltz-2 jointly models structures and binding affinities. |
| AlphaFold 3 | Mixed assemblies containing proteins, nucleic acids, small molecules, ions and modified residues. | The 2024 Nature paper gives a 4,665-residue coronavirus-spike example, but does not establish a general local hardware limit for every assembly. | Uses the AlphaFold 3 inference pipeline and its required databases or hosted implementation. | Not the main capability highlighted in the cited paper. | Check the current implementation and weight terms before treating it as an open-source deployment. | No affinity prediction is established by the cited paper. |
OpenFold: the strongest local option for long protein chains
OpenFold is a trainable PyTorch reproduction of AlphaFold2. Its repository documents low-memory attention, GPU optimizations and CPU offloading. It reports inference on sequences longer than 4,000 residues on a single A100 and up to approximately twice the GPU speed of the reference implementation under its documented conditions.
OpenFold-Multimer targets protein–protein interactions, while the OpenFold ecosystem lists OpenFold3 as a cofolding system for biomolecular complexes. The ecosystem page states Apache 2.0 licensing, although you should verify the license of each model checkpoint, database and auxiliary component in the exact release you install.
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- You need a reproducible local pipeline rather than a public notebook or alignment service.
- You want to tune inference, modify the PyTorch code or retrain components.
- Your target contains very long chains and you can budget time for memory profiling.
- You need compatibility with AlphaFold’s official parameters; the repository states that OpenFold can use those parameters and that the reverse direction is supported.
What the 4,000-residue figure does—and does not—mean
The documented result applies to a sequence longer than 4,000 residues on one A100 under the project’s stated setup. It is a useful feasibility signal, not an assembly-size guarantee. A multimer with several chains, many recycles or large multiple-sequence alignments can require substantially more memory. Test the complete stoichiometry, not just its longest chain, and compare several seeds.
ColabFold: the easiest route to fast multimer hypotheses
The peer-reviewed ColabFold paper in Nature Methods (2022) describes an open-source workflow that combines MMseqs2’s fast homology search with AlphaFold2 or RoseTTAFold. That combination removes much of the alignment and database setup that makes a fully local AlphaFold-style deployment difficult.
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Why it is a good first pass
- MMseqs2 can produce alignments quickly enough for exploratory work.
- The workflow is familiar to users who already understand AlphaFold2-style inputs and confidence scores.
- It is practical for screening alternative stoichiometries or generating starting hypotheses before a more specialized run.
Important operational caveats
A public MSA service may expose sequences to an external service, which can be unacceptable for unpublished or regulated work. Confirm the service’s current privacy and usage terms, and decide whether local databases are required. Model parameters, database versions and notebook defaults can also change; record them with every prediction.
ColabFold is not a substitute for experimental validation. For a large assembly, inspect interface confidence and consistency across seeds and input variants rather than selecting a single top-ranked model.
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Chai-1: use restraints when biology gives you extra information
Chai-1 is the most distinctive choice when you know something about the complex beyond its sequences. Its repository documents user-specified inter-chain contact restraints and covalent-bond restraints, and it provides both a browser server and a local package. The repository states that code and model weights are Apache 2.0 licensed.
Examples of useful restraints
- A cross-link or mutational result indicating that residues from two chains must be close.
- A known covalent connection between a ligand or modified residue and a protein atom.
- An experimentally supported contact map that can rule out otherwise plausible interfaces.
Convert each piece of evidence into the exact input format documented by the version you install. Pin the package—for example, the repository recommends a version such as chai_lab==0.6.1—and retain the restraint file with the prediction. A restraint can guide a model toward a biologically plausible arrangement, but it does not prove that the remaining interface geometry is correct.
When Chai-1 is not automatically the answer
The available documentation does not establish Chai-1 as universally best for every very large assembly. If no reliable restraints exist, benchmark it against an unconstrained protein multimer workflow and judge the result with interface confidence and independent data.
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Boltz-1 and Boltz-2: choose them for ligands or affinity questions
Boltz-1 is described by its project as fully open source and approaching AlphaFold 3 accuracy. Boltz-2 is presented as a foundation model that jointly predicts complex structures and binding affinities. The repository states that all code and weights are MIT licensed for academic and commercial use.
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If your assembly includes a small molecule and the decision depends on relative binding, coordinates alone are insufficient. Boltz-2 is designed to produce an affinity estimate alongside the complex structure, allowing one workflow to rank hypotheses for follow-up experiments.
Hardware and maturity caveats
The README documents NVIDIA CUDA acceleration and warns that CPU inference is significantly slower. It also says updated Boltz-2 evaluation and training code were still forthcoming in the cited release. Pin the package and model weights, read the release notes, and avoid treating an affinity score as a measured dissociation constant. The project’s claim that Boltz-2 can be 1,000 times faster than physics-based free-energy perturbation methods is a project claim; treat it as directional until independently benchmarked on your system and chemical series.
AlphaFold 3 as a capability reference, not a blanket open-source recommendation
The 2024 Nature paper describes a diffusion model for joint structures containing proteins, DNA or RNA, small molecules, ions and modified residues. In the paper’s evaluation, it was more accurate than many specialized methods across most tested categories, including protein–protein and antibody interfaces. The paper also includes a 4,665-residue coronavirus-spike example.
Those results make AlphaFold 3 a valuable reference when assessing what a modern complex predictor can represent. They do not by themselves answer whether you can deploy a local, open implementation. Check the current software terms, checkpoint terms, database licenses and access restrictions before building a production pipeline around it.
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A practical workflow for a large complex
- Define the assembly. List chains, copy numbers, sequence boundaries, cofactors, nucleic acids, ligands, ions and modifications. Remove accidental truncations and document unresolved regions.
- Classify the problem. Use a protein-only multimer route for protein assemblies; choose a ligand-aware model when chemistry matters; choose a restraint-capable model when you have contacts or covalent information.
- Generate a baseline. Start with ColabFold for a fast hypothesis or OpenFold-Multimer when local control and long-chain behavior are priorities.
- Run more than one seed. Keep model settings, random seeds and alignment versions. Agreement across independent runs is more informative than one ranking score.
- Add evidence deliberately. Use Chai-1 restraints when contacts or bonds are experimentally supported. Do not add speculative restraints simply to force a preferred interface.
- Model ligand context if needed. Use Boltz when ligand placement or affinity is part of the biological question, and report the affinity as a prediction rather than an assay result.
- Inspect confidence by region. Separate per-residue confidence, inter-chain interface confidence, predicted alignment error and model-to-model variation. A well-folded monomer does not guarantee a correct quaternary arrangement.
- Validate independently. Compare against cryo-electron microscopy density, cross-linking, mutagenesis, biochemical assays or other orthogonal evidence whenever the model will guide an experiment or decision.
GPU and memory planning
A CUDA-capable NVIDIA GPU is the most relevant hardware category for these workflows. OpenFold documents substantial GPU speedups, low-memory attention and an A100 result for sequences longer than 4,000 residues. Boltz documents CUDA acceleration and significantly slower CPU inference. These facts support using a workstation GPU or rented cloud GPU, but they do not identify one universally correct model.
Estimate the workload before buying hardware
- Sequence length and chain count: attention and pair representations grow with the assembly, not just the longest individual chain.
- Recycles and seeds: more recycles and independent predictions multiply runtime and memory pressure.
- Precision and offloading: reduced precision and CPU offloading can make a run fit, at the cost of speed or complexity.
- Alignment generation: MSA searches can consume substantial CPU, RAM and storage even when inference runs on a GPU.
- Batching: process large assemblies one at a time unless you have measured that batching fits safely.
Run a small representative fragment and then the complete stoichiometry. Do not infer full-assembly feasibility from a monomer test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Licensing, privacy and reproducibility checklist
- Record the exact package, model checkpoint, database snapshot, container and GPU driver.
- Read separate terms for code, weights, sequence databases, templates and hosted MSA services.
- Prefer local alignment generation for confidential sequences unless an external service is approved.
- Pin versions; Chai-1’s recommendation to use a specific package version illustrates why this matters.
- Save input FASTA files, chain order, stoichiometry, restraints, seeds and confidence outputs.
- For commercial deployment, obtain legal review of every component, even when the main repository uses a permissive license.
Common failure modes and fixes
The run exceeds GPU memory
Reduce batch size, use low-memory attention or CPU offloading where supported, lower unnecessary recycles, and test chains separately to identify the dominant contributor. If the complete assembly remains infeasible, split the biological question into domains or interfaces—but label the result as a partial model.
Different seeds produce incompatible interfaces
Check sequence completeness, stoichiometry and MSA quality first. Then compare interface confidence and predicted alignment error across models. Persistent disagreement is a result to investigate, not a reason to select the visually most appealing structure.
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Use a ligand-aware workflow such as Boltz when ligand context is central, verify atom and bond definitions, and test the pose against mutagenesis or biochemical data. Protein-only models are not designed to answer every chemical question.
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A public workflow is convenient but unsuitable for confidential sequences
Move MSA generation and inference to a controlled local or private environment, document database provenance, and confirm that the model and weight licenses permit the intended use.
Which tool should you choose?
| Your primary need | Start with | Reason |
|---|---|---|
| Fast protein-only exploration | ColabFold | MMseqs2-based alignments and established AlphaFold2 or RoseTTAFold workflows reduce setup effort. |
| Long chains, local control or retraining | OpenFold-Multimer | PyTorch control, low-memory attention, CPU offloading and documented long-sequence behavior. |
| Known contacts or covalent links | Chai-1 | Explicit inter-chain and covalent-bond restraints. |
| Ligands plus affinity ranking | Boltz-2 | Joint structure and binding-affinity prediction, with MIT-licensed code and weights stated by the project. |
| Mixed protein, nucleic-acid and small-molecule capability benchmark | AlphaFold 3 | Broad modality coverage and strong 2024 Nature evaluation; verify deployment terms before use. |
The most defensible large-complex workflow is often comparative: generate a protein-only baseline, add restraints or ligand context only when justified, and require confidence metrics plus experimental evidence before making a structural claim.
Frequently Asked Questions
Can AlphaFold-Multimer handle a very large assembly?
It can generate hypotheses for many protein assemblies, but no single residue-count threshold guarantees that the complete multimer will fit or be accurate. Check the full stoichiometry, GPU memory, interface confidence and agreement across independent runs.
Is OpenFold’s 4,000-residue result a guarantee for my complex?
No. The project documents sequences longer than 4,000 residues on one A100 under its stated setup. That benchmark concerns a sequence and hardware configuration, not every multimer, ligand set or inference setting.
Can these predictors run without a GPU?
Some workflows can run on CPUs, but performance may be impractical for large assemblies. Boltz specifically describes CPU inference as significantly slower; GPU memory and runtime should be tested with a representative input.
Are Boltz affinity scores equivalent to experimental measurements?
No. They are model predictions for prioritization. Confirm rankings with binding assays or another independent method before treating them as quantitative measurements.
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