For a protein-only complex, start with OpenFold-Multimer or ColabFold. ColabFold is usually the fastest way to obtain multiple hypotheses because it pairs MMseqs2 homology searches with AlphaFold2 or RoseTTAFold. OpenFold-Multimer is the better local engineering choice when chains are long, GPU memory is tight, or you need a trainable PyTorch stack and reproducible control over inference. Choose Chai-1 when experimental contacts or covalent links can be supplied as restraints, and choose Boltz when ligands or binding-affinity estimates are part of the question.
No model is universally “best” for a very large assembly. Chain length, total stoichiometry, missing residues, cofactors, nucleic acids, restraints, privacy requirements, licensing and available GPU memory all change the answer. Treat every prediction as a hypothesis: compare seeds and models, inspect interface confidence, and validate important conclusions experimentally.
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Quick comparison
| Tool | Primary scope | Why choose it | Important qualification |
|---|---|---|---|
| OpenFold-Multimer | Protein–protein and multimer modeling | Local PyTorch control, long-chain support, low-memory attention and a trainable implementation | A documented residue limit for one sequence is not a guarantee that a whole multimer will fit or be accurate |
| ColabFold | Protein structures and complexes using AlphaFold2 or RoseTTAFold | Fast MMseqs2 MSAs and an approachable notebook/server workflow | Check the privacy and usage terms of any public MSA service, model parameters and databases |
| Chai-1 | Complexes with user-provided contacts or covalent bonds | Explicit inter-chain and covalent-bond restraints, with both a local package and browser server | There is no evidence that it is best for every very large assembly; validate on your target class |
| Boltz-1/2 | Broad biomolecular interactions, including ligands; Boltz-2 also predicts affinity | Useful when coordinates and a binding-affinity estimate are needed together | Boltz-2 evaluation and training code were still described as forthcoming; pin versions before production use |
| AlphaFold 3 | Proteins, nucleic acids, small molecules, ions and modified residues | Scientific reference point for all-atom complex capability | The Nature result is a benchmark, not a blanket recommendation for open-source local deployment; implementation and weight licenses must be checked |
What “large protein complex” means in practice
Size has at least three separate dimensions. A model may accept a long individual chain yet fail when several copies, ligands and nucleic-acid chains are combined. Conversely, a smaller sequence can be difficult if it is disordered, repetitive, poorly represented in sequence databases or assembled in an uncertain stoichiometry.
- Per-chain length: OpenFold documentation reports inference for sequences longer than 4,000 residues on one A100, using its memory-saving techniques and, for longer cases, CPU offloading. That is a project capability figure, not a promise for every multimer.
- Total assembly length: memory and runtime grow with all chains and their pairwise relationships. A 4,000-residue monomer result cannot be converted into a guaranteed 4,000-residue-per-chain multimer result.
- Biological completeness: unresolved termini, splice variants, cofactors, nucleic acids, metals and post-translational modifications can matter as much as residue count.
- Evidence quality: a plausible interface with weak confidence is not equivalent to a validated quaternary structure.
Before launching a run, write down the proposed stoichiometry, chain sequences, residue numbering, missing segments, cofactors, nucleic acids, ligands and known contacts. This prevents a technically successful prediction of the wrong biological assembly.
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OpenFold and OpenFold-Multimer
What it is
OpenFold is a trainable PyTorch reproduction of AlphaFold2. Its project documentation describes GPU speedups of up to about two times, low-memory attention and compatibility with AlphaFold’s official parameters. The OpenFold ecosystem also lists OpenFold3 as an open-source cofolding system and OpenFold-Multimer as a protein–protein and multimer modeling system; that ecosystem page states Apache 2.0 licensing.
Why it suits large local jobs
OpenFold is the strongest first local test when chain length or memory is the limiting factor. The documented ability to handle sequences over 4,000 residues on a single A100, together with CPU offloading for longer sequences, gives a concrete starting point for long-chain planning. Low-memory attention can reduce peak GPU demand, while a PyTorch implementation lets experienced teams tune precision, batching, checkpointing and inference code.
Its trainable design also matters if you need to alter the model, retrain components or integrate prediction into an existing research pipeline. The project notes that OpenFold can use AlphaFold’s official parameters and that the parameters can work in the other direction as well, which can simplify comparisons with established AlphaFold2 workflows.
Limits to keep in view
Do not treat “over 4,000 residues on one A100” as an assembly-size guarantee. It refers to the documented long-sequence behavior under the project’s stated setup. Multimeric pair representations, multiple copies and extra modalities can raise memory use and reduce confidence. Run several seeds, inspect per-chain and interface confidence, and compare the result with another method when the structure will guide an experiment.
ColabFold
What it combines
The peer-reviewed ColabFold paper 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 assembly work that makes a fully local AlphaFold-style pipeline difficult to set up. It is therefore the most approachable starting point for screening several stoichiometries or generating initial multimer hypotheses.
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When it is the right first run
- You need a quick answer rather than a deeply customized training stack.
- You want to compare AlphaFold2-style and RoseTTAFold-style predictions through a mature notebook or server workflow.
- You are exploring alternative chain combinations before committing to a larger local installation.
ColabFold is particularly useful for producing a small ensemble of hypotheses. Do not select a complex solely because one notebook returns a top-ranked model. Compare seeds, inspect interface metrics and check whether the predicted contacts are supported by biology.
Privacy and reproducibility
The convenience of public MSA services comes with governance questions. Confirm where sequences are sent, how long results are retained, whether the service permits commercial or confidential work, and which database and model versions were used. For a sensitive project, reproduce the workflow with local databases or move to a locally controlled OpenFold, Chai-1 or Boltz installation after the exploratory phase.
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Restraints are its differentiator
Chai-1’s repository documents user-specified inter-chain contact restraints and covalent-bond restraints. Those inputs let experimental information guide folding instead of asking the model to infer every interface from sequence and templates alone. The project also provides a browser server for a quick trial and a local package for controlled runs.
Best use cases
Use Chai-1 when cross-linking, mutagenesis, biochemical data or known chemistry identifies contacts that should be enforced or favored. It is also a natural candidate when a covalent connection between components must be represented explicitly. The repository states that both code and model weights are Apache 2.0 licensed.
Reproducible installation
Pin the package version rather than installing an unqualified latest release. The repository gives chai_lab==0.6.1 as an example of a version to pin. Record the restraint file, chain identifiers, residue numbering, model version and random seeds alongside the output coordinates.
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Restraints do not guarantee a correct global fold. An incorrect contact list can force an implausible model, and the repository does not establish that Chai-1 is universally superior for every very large assembly. Compare restrained and unrestrained runs and test whether the imposed contacts are independently credible.
Boltz-1 and Boltz-2
Broader interaction modeling
Boltz-1 is described by its project as fully open source and as approaching AlphaFold 3 accuracy. Boltz-2 is presented as a foundation model that jointly predicts complex structures and binding affinities. That makes Boltz especially relevant when the decision is not only “what might the complex look like?” but also “which ligand or interface is more likely to bind?”
Licensing and deployment
The Boltz repository states that its code and weights are MIT licensed for academic and commercial use. It provides a pip installation path and documents NVIDIA CUDA acceleration. CPU inference is supported but significantly slower than GPU inference, so a CUDA-capable NVIDIA card is the practical default for useful throughput.
Interpret the affinity claim carefully
The project’s 2025 description claims Boltz-2 can be 1,000 times faster than physics-based free-energy perturbation methods. That is a project claim rather than an independent, standardized benchmark. Use the affinity output as a prioritization signal, not as a replacement for binding assays or a validated thermodynamic calculation. The same repository indicated that updated Boltz-2 evaluation and training code were still forthcoming, so pin the release and inspect its notes before treating results as production-grade.
AlphaFold 3 as a capability reference
The 2024 Nature paper describes AlphaFold 3 as a diffusion-based model for joint structures containing proteins, nucleic acids, small molecules, ions and modified residues. In its evaluation, it reported higher accuracy 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.
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Those results establish a scientific reference point for broad complex modeling, not an automatic choice for open-source local work. Before deployment, check the exact implementation, parameter and weight licenses that apply to your use. A published architecture or benchmark does not by itself grant permission to redistribute weights or run a particular implementation commercially.
Decision guide by project need
| Your main question | First tool to test | Reason |
|---|---|---|
| “Can these protein chains form a plausible complex?” | ColabFold, then OpenFold-Multimer | Fast MSA generation is useful for exploration; OpenFold adds local control and long-chain options |
| “Will the assembly fit on our own GPU?” | OpenFold-Multimer | Its documented low-memory attention and long-sequence behavior make memory planning explicit |
| “We know contacts or covalent links from experiments.” | Chai-1 | It accepts user-specified inter-chain and covalent-bond restraints |
| “A ligand is central to the interaction.” | Boltz | Its scope includes ligand-aware complex modeling, and Boltz-2 adds affinity prediction |
| “We need a broad all-atom benchmark.” | Compare against AlphaFold 3 where access and licensing permit | Its Nature evaluation covers proteins, nucleic acids, small molecules, ions and modified residues |
A practical workflow for a large assembly
- Define the biological target. Confirm stoichiometry, chain boundaries, sequence completeness, unresolved regions, cofactors, nucleic acids, ligands and any known contacts. Keep a written mapping between biological chains and the residue numbering used by each tool.
- Make a protein-only baseline. For a protein complex without ligand or nucleic-acid requirements, begin with ColabFold or OpenFold-Multimer. Run multiple random seeds and, if feasible, more than one model family. Save every input and output rather than only the top-ranked structure.
- Inspect interfaces, not just global scores. Examine per-residue confidence, inter-chain confidence, predicted contacts and whether the same interface recurs across seeds. A high-confidence monomer with a changing interface is not a stable complex prediction.
- Add experimental information deliberately. If cross-links, contact maps or known covalent bonds exist, encode them in Chai-1 and compare the restrained result with an unrestrained baseline. Investigate any large conformational change instead of assuming the restrained model is correct.
- Switch to ligand-aware modeling when required. Use Boltz when a small molecule or affinity ranking is part of the question. Keep coordinates and affinity outputs as separate evidence streams; a favorable score does not prove the binding pose.
- Estimate memory before scaling. Test one representative chain or a reduced assembly, monitor peak GPU memory and then increase the number of chains. For long sequences, prioritize the OpenFold configuration that uses low-memory attention and CPU offloading.
- Validate decisions experimentally. Compare predicted interfaces with cryo-electron microscopy density, cross-linking, mutagenesis, biochemical assays or other available evidence. Use the model to choose experiments, not to eliminate them.
GPU and system requirements
Why NVIDIA CUDA is the practical baseline
OpenFold documents GPU acceleration and an A100 long-sequence example. Boltz documents NVIDIA CUDA acceleration and warns that CPU inference is significantly slower. A CUDA-capable NVIDIA workstation GPU or rented cloud GPU is therefore the most relevant hardware category for interactive work and batch screening.
How to size memory without a misleading number
There is no single VRAM figure that guarantees a successful run. Requirements change with total residues, number of chains, templates, MSA depth, precision, batch size, model variant and whether activations are offloaded to system RAM. Start with a small representative test, record peak allocation and leave headroom for the operating system and data pipeline. A GPU that handles one long chain may still fail on a multimer with the same per-chain length.
Local versus rented hardware
Local inference gives better control over confidential sequences, database versions and reproducibility, but it requires driver, CUDA, storage and environment maintenance. A cloud GPU reduces setup time and can provide an A100-class device for occasional long-chain runs, while adding data-transfer, retention and recurring-cost questions. Choose the deployment boundary before uploading proprietary sequences.
Recommended Free Tools
MSAs, templates and databases
Sequence alignments and templates strongly influence protein-only predictors. ColabFold’s MMseqs2 integration makes alignment search convenient, while a fully local OpenFold workflow gives more control over database snapshots and network isolation. Record the database release, search settings, template cutoff and model parameters for every result. Two predictions made with different databases can differ even when the neural-network weights are identical.
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For complexes, an MSA can support individual chain folds without proving that the chains interact in the predicted orientation. Paired or species-consistent evidence may be informative for some assemblies, but its absence is not proof of non-interaction. Treat interface confidence and biological context as separate checks.
Licensing and reproducibility checklist
- Code license: OpenFold’s ecosystem page states Apache 2.0; Chai-1 states Apache 2.0 for code and weights; Boltz states MIT for code and weights. Verify the specific release you install.
- Weights: Do not assume that an open repository and open model parameters have identical redistribution terms. Check the weight license and any notices for bundled components.
- Databases: MSA and template databases can have separate terms from the predictor. Public service access may also impose privacy or retention conditions.
- Version pinning: Record package versions, model checkpoints, database snapshots, CUDA and driver versions, precision settings and random seeds. Pin Chai-1 rather than relying on an unqualified latest package; the project gives
chai_lab==0.6.1as an example. - Audit trail: Keep the exact input sequences, chain order, restraint files, hardware, logs, confidence files and all seeds. Without these, a later rerun may not reproduce the reported interface.
Common failure modes and fixes
Out-of-memory errors
Reduce batch size, use lower-memory attention, enable supported CPU offloading, shorten unstructured tails only when biologically justified, or split exploratory runs by assembly hypothesis. Do not silently drop chains that are essential to the biological question.
Inconsistent interfaces across seeds
Generate additional seeds, compare alternative stoichiometries and inspect whether any interface has strong independent evidence. Consensus across runs is useful, but repeated model agreement is still not experimental validation.
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High monomer confidence but weak complex confidence
This pattern often means the individual folds are well determined while the relative orientation is not. Report chain-level and interface-level confidence separately and avoid presenting the assembly as established.
Restraint-driven artifacts
Check residue numbering, chain identifiers and the chemical plausibility of every restraint. Run an unrestrained control and vary uncertain restraints to see whether the global fold is robust.
Affinity scores treated as measurements
Use Boltz-2 affinity outputs to rank candidates for testing, not as a substitute for binding assays. Record the model release because evaluation and training components were still evolving in the project’s published description.
How to report a result responsibly
A useful report names the assembly hypothesis, sequence sources, chain order, model and checkpoint versions, MSA and template databases, number of seeds, hardware, restraints, confidence metrics and validation evidence. Include alternative models when interfaces disagree. Distinguish a coordinate prediction from a biological claim: “the model places these chains in contact” is more precise than “the complex structure is known.”
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Quick Recap
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